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0 HcmV?d00001 diff --git a/docs/build/html/_downloads/fe02006031f96dc9a3a8b158e5899dae/aaanalysis-CPPPlot-1.py b/docs/build/html/_downloads/fe02006031f96dc9a3a8b158e5899dae/aaanalysis-CPPPlot-1.py new file mode 100644 index 00000000..b2eede39 --- /dev/null +++ b/docs/build/html/_downloads/fe02006031f96dc9a3a8b158e5899dae/aaanalysis-CPPPlot-1.py @@ -0,0 +1,6 @@ +import matplotlib.pyplot as plt +import aaanalysis as aa +sf = aa.SequenceFeature() +df_seq = aa.load_dataset(name='SEQ_DISULFIDE', min_len=100) +labels = list(df_seq["label"]) +df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10) diff --git a/docs/build/html/_images/social_previews/summary__index_tables_9266ddb8.png b/docs/build/html/_images/social_previews/summary__index_tables_9266ddb8.png new file mode 100644 index 0000000000000000000000000000000000000000..2146d725dddd28d40f1d128a569c62a5ae783530 GIT binary patch literal 48017 zcmeFZXH-*b*EQ@p9z{{)Sdb$Ks5AkABT@teR8*vQkP;B3BSfjvRSu#eAYD302_Xce 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zP*j>t`NDhh&ZrR0ULCG34S0ECR>ftrhJQMf3R$2HjfI^+`l{Za-YO3iK)AVTzUUO@%;!#AG88I?G-dBx;1RNq3vk= zxP6L;G!NnoRiwRoa5*l1r_uHeEN*B{%=s_=k7QIg1eLK&PpX8jfQZ%n@pI1t07W0}>goUhl2*2fOLF!9?9Z zQ#*rL0i>Nr+82GXTYmphGKV4G4HDi8pBUQ%e9hoXWCzp=i3WF5Z3%*g4p9D&oWaNP z{>^}6+5|iwi9JYyF6w>wpq|nqc0y^1tlVJJpVh^tz#WrJ;R|}#IK6Ae3y+lRdbwgZcV;1=)SKXXP7+wL zcOVIuoV+W-A5r9;Jh0CzB*1|r7yq%=PSgAd#F=-#a}Vun8nj38ipt57t0LA})u)w# ziY};clBF@LiIlS~^D3Sl5f>mxi6W2rD7Bq*yw4@7g|Q)-$_xeXFCf27X;Y<;OcMx ZhMNYf + + + + + + + + + + + + + + + + + Tables — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      Tables

      + +
      +

      Overview Table

      +

      All tables from the AAanalysis documentation are given here in chronological order of the project history.

      + +++++ + + + + + + + + + + + + + + + + +

      Table

      Description

      See also

      1_overview_benchmarks

      Protein benchmark datasets

      aa.load_dataset

      2_overview_scales

      Amino acid scale datasets

      aa.load_scales

      +
      +
      +

      Protein benchmark datasets

      +

      Three types of benchmark datasets are provided:

      +
        +
      • Residue prediction (AA): Datasets used to predict residue (amino acid) specific properties.

      • +
      • Domain prediction (DOM): Dataset used to predict domain specific properties.

      • +
      • Sequence prediction (SEQ): Datasets used to predict sequence specific properties.

      • +
      +

      The classification of each dataset is indicated as first part of their name followed by an abbreviation for the +specific dataset (e.g., ‘AA_LDR’, ‘DOM_GSEC’, ‘SEQ_AMYLO’). For some datasets, an additional version of it is provided +for positive-unlabeled (PU) learning containing only positive (1) and unlabeled (2) data samples, as indicated by +dataset_name_PU (e.g., ‘DOM_GSEC_PU’).

      + ++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Level

      Dataset

      # Sequences

      # Amino acids

      # Positives

      # Negatives

      Predictor

      Description

      Reference

      Label

      Amino acid

      AA_CASPASE3

      233

      185605

      705

      184900

      PROSPERous

      Prediction of caspase-3 cleavage site

      Song18

      1 (adjacent to cleavage site), 0 (not adjacent to cleavage site)

      Amino acid

      AA_FURIN

      71

      59003

      163

      58840

      PROSPERous

      Prediction of furin cleavage site

      Song18

      1 (adjacent to cleavage site), 0 (not adjacent to cleavage site)

      Amino acid

      AA_LDR

      342

      118248

      35469

      82779

      IDP-Seq2Seq

      Prediction of long intrinsically disordered regions (LDR)

      Tang20

      1 (disordered), 0 (ordered)

      Amino acid

      AA_MMP2

      573

      312976

      2416

      310560

      PROSPERous

      Prediction of Matrix metallopeptidase-2 (MMP2) cleavage site

      Song18

      1 (adjacent to cleavage site), 0 (not adjacent to cleavage site)

      Amino acid

      AA_RNABIND

      221

      55001

      6492

      48509

      GMKSVM-RU

      Prediction of RNA-binding protein residues (RBP60 dataset)

      Yang21

      1 (binding), 0 (non-binding)

      Amino acid

      AA_SA

      233

      185605

      101082

      84523

      PROSPERous

      Prediction of solvent accessibility (SA) of residue (AA_CASPASE3 data set)

      Song18

      1 (exposed/accessible), 0 (buried/non-accessible)

      Sequence

      SEQ_AMYLO

      1414

      8484

      511

      903

      ReRF-Pred

      Prediction of amyloidognenic regions

      Teng21

      1 (amyloidogenic), 0 (non-amyloidogenic)

      Sequence

      SEQ_CAPSID

      7935

      3364680

      3864

      4071

      VIRALpro

      Prediction of capdsid proteins

      Galiez16

      1 (capsid protein), 0 (non-capsid protein)

      Sequence

      SEQ_DISULFIDE

      2547

      614470

      897

      1650

      Dipro

      Prediction of disulfide bridges in sequences

      Cheng06

      1 (sequence with SS bond), 0 (sequence without SS bond)

      Sequence

      SEQ_LOCATION

      1835

      732398

      1045

      790

      nan

      Prediction of subcellular location of protein (cytoplasm vs plasma membrane)

      Shen19

      1 (protein in cytoplasm), 0 (protein in plasma membrane)

      Sequence

      SEQ_SOLUBLE

      17408

      4432269

      8704

      8704

      SOLpro

      Prediction of soluble and insoluble proteins

      Magnan09

      1 (soluble), 0 (insoluble)

      Sequence

      SEQ_TAIL

      6668

      2671690

      2574

      4094

      VIRALpro

      Prediction of tail proteins

      Galiez16

      1 (tail protein), 0 (non-tail protein)

      Domain

      DOM_GSEC

      126

      92964

      63

      63

      nan

      Prediction of gamma-secretase substrates

      Breimann23c

      1 (substrate), 0 (non-substrate)

      Domain

      DOM_GSEC_PU

      694

      494524

      63

      0

      nan

      Prediction of gamma-secretase substrates (PU dataset)

      Breimann23c

      1 (substrate), 2 (unknown substrate status)

      +
      +
      +

      Amino acid scale datasets

      +

      Different amino acid scale datasets are provided

      + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      Dataset

      Description

      # Scales

      Reference

      scales

      Amino acid scales (min-max normalized)

      586

      Breimann23b

      scales_raw

      Amino acid scales (raw values)

      586

      Kawashima08

      scales_classification

      Classification of scales (Aaontology)

      586

      Breimann23b

      scales_pc

      Principal component (PC) compressed scales

      20

      Breimann23a

      top60

      Top 60 scale subsets

      60

      Breimann23a

      top60_eval

      Evaluation of top 60 scale subsets

      60

      Breimann23a

      +
      +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_index/tables.rst.txt b/docs/build/html/_sources/_index/tables.rst.txt new file mode 100644 index 00000000..9e4ceec9 --- /dev/null +++ b/docs/build/html/_sources/_index/tables.rst.txt @@ -0,0 +1,246 @@ +.. + Developer Notes: + This is the index file for all tables of the AAanalysis documentation. Each table should be saved the /tables + directory. This file will serve as template for tables.rst, which is automatically created on the information + provided here and in the .csv tables from the /tables directory. Add a new table as .csv in the /tables directory, + in the overview table at the beginning of this document, and a new section with a short description of it in this + document. Each column and important data types (e.g., categories) should be described. Each table should contain a + 'Reference' column. + Ignore 'tables_template.rst: WARNING: document isn't included in any toctree' warning + +Tables +====================== + +.. contents:: + :local: + :depth: 1 + +Overview Table +-------------- +All tables from the AAanalysis documentation are given here in chronological order of the project history. + +.. _0_mapper: +.. list-table:: + :header-rows: 1 + :widths: 8 8 8 + + * - Table + - Description + - See also + * - 1_overview_benchmarks + - Protein benchmark datasets + - aa.load_dataset + * - 2_overview_scales + - Amino acid scale datasets + - aa.load_scales + + +Protein benchmark datasets +-------------------------- +Three types of benchmark datasets are provided: + +- Residue prediction (AA): Datasets used to predict residue (amino acid) specific properties. +- Domain prediction (DOM): Dataset used to predict domain specific properties. +- Sequence prediction (SEQ): Datasets used to predict sequence specific properties. + +The classification of each dataset is indicated as first part of their name followed by an abbreviation for the +specific dataset (e.g., 'AA_LDR', 'DOM_GSEC', 'SEQ_AMYLO'). For some datasets, an additional version of it is provided +for positive-unlabeled (PU) learning containing only positive (1) and unlabeled (2) data samples, as indicated by +*dataset_name_PU* (e.g., 'DOM_GSEC_PU'). + +.. _1_overview_benchmarks: +.. list-table:: + :header-rows: 1 + :widths: 8 8 8 8 8 8 8 8 8 8 + + * - Level + - Dataset + - # Sequences + - # Amino acids + - # Positives + - # Negatives + - Predictor + - Description + - Reference + - Label + * - Amino acid + - AA_CASPASE3 + - 233 + - 185605 + - 705 + - 184900 + - PROSPERous + - Prediction of caspase-3 cleavage site + - :ref:`Song18 ` + - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) + * - Amino acid + - AA_FURIN + - 71 + - 59003 + - 163 + - 58840 + - PROSPERous + - Prediction of furin cleavage site + - :ref:`Song18 ` + - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) + * - Amino acid + - AA_LDR + - 342 + - 118248 + - 35469 + - 82779 + - IDP-Seq2Seq + - Prediction of long intrinsically disordered regions (LDR) + - :ref:`Tang20 ` + - 1 (disordered), 0 (ordered) + * - Amino acid + - AA_MMP2 + - 573 + - 312976 + - 2416 + - 310560 + - PROSPERous + - Prediction of Matrix metallopeptidase-2 (MMP2) cleavage site + - :ref:`Song18 ` + - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) + * - Amino acid + - AA_RNABIND + - 221 + - 55001 + - 6492 + - 48509 + - GMKSVM-RU + - Prediction of RNA-binding protein residues (RBP60 dataset) + - :ref:`Yang21 ` + - 1 (binding), 0 (non-binding) + * - Amino acid + - AA_SA + - 233 + - 185605 + - 101082 + - 84523 + - PROSPERous + - Prediction of solvent accessibility (SA) of residue (AA_CASPASE3 data set) + - :ref:`Song18 ` + - 1 (exposed/accessible), 0 (buried/non-accessible) + * - Sequence + - SEQ_AMYLO + - 1414 + - 8484 + - 511 + - 903 + - ReRF-Pred + - Prediction of amyloidognenic regions + - :ref:`Teng21 ` + - 1 (amyloidogenic), 0 (non-amyloidogenic) + * - Sequence + - SEQ_CAPSID + - 7935 + - 3364680 + - 3864 + - 4071 + - VIRALpro + - Prediction of capdsid proteins + - :ref:`Galiez16 ` + - 1 (capsid protein), 0 (non-capsid protein) + * - Sequence + - SEQ_DISULFIDE + - 2547 + - 614470 + - 897 + - 1650 + - Dipro + - Prediction of disulfide bridges in sequences + - :ref:`Cheng06 ` + - 1 (sequence with SS bond), 0 (sequence without SS bond) + * - Sequence + - SEQ_LOCATION + - 1835 + - 732398 + - 1045 + - 790 + - nan + - Prediction of subcellular location of protein (cytoplasm vs plasma membrane) + - :ref:`Shen19 ` + - 1 (protein in cytoplasm), 0 (protein in plasma membrane) + * - Sequence + - SEQ_SOLUBLE + - 17408 + - 4432269 + - 8704 + - 8704 + - SOLpro + - Prediction of soluble and insoluble proteins + - :ref:`Magnan09 ` + - 1 (soluble), 0 (insoluble) + * - Sequence + - SEQ_TAIL + - 6668 + - 2671690 + - 2574 + - 4094 + - VIRALpro + - Prediction of tail proteins + - :ref:`Galiez16 ` + - 1 (tail protein), 0 (non-tail protein) + * - Domain + - DOM_GSEC + - 126 + - 92964 + - 63 + - 63 + - nan + - Prediction of gamma-secretase substrates + - :ref:`Breimann23c ` + - 1 (substrate), 0 (non-substrate) + * - Domain + - DOM_GSEC_PU + - 694 + - 494524 + - 63 + - 0 + - nan + - Prediction of gamma-secretase substrates (PU dataset) + - :ref:`Breimann23c ` + - 1 (substrate), 2 (unknown substrate status) + + +Amino acid scale datasets +------------------------- +Different amino acid scale datasets are provided + +.. _2_overview_scales: +.. list-table:: + :header-rows: 1 + :widths: 8 8 8 8 + + * - Dataset + - Description + - # Scales + - Reference + * - scales + - Amino acid scales (min-max normalized) + - 586 + - :ref:`Breimann23b ` + * - scales_raw + - Amino acid scales (raw values) + - 586 + - :ref:`Kawashima08 ` + * - scales_classification + - Classification of scales (Aaontology) + - 586 + - :ref:`Breimann23b ` + * - scales_pc + - Principal component (PC) compressed scales + - 20 + - :ref:`Breimann23a ` + * - top60 + - Top 60 scale subsets + - 60 + - :ref:`Breimann23a ` + * - top60_eval + - Evaluation of top 60 scale subsets + - 60 + - :ref:`Breimann23a ` + + diff --git a/docs/build/html/_sources/api.rst.txt b/docs/build/html/_sources/api.rst.txt index 7365fec0..07ed079c 100644 --- a/docs/build/html/_sources/api.rst.txt +++ b/docs/build/html/_sources/api.rst.txt @@ -21,6 +21,8 @@ Data .. autosummary:: :toctree: generated/ + aaanalysis.load_dataset + aaanalysis.load_scales .. _feature_engineering_api: @@ -29,6 +31,10 @@ Feature Engineering .. autosummary:: :toctree: generated/ + aaanalysis.AAclust + aaanalysis.SequenceFeature + aaanalysis.CPP + aaanalysis.CPPPlot .. _pu_learning_api: @@ -37,6 +43,7 @@ PU Learning .. autosummary:: :toctree: generated/ + aaanalysis.dPULearn .. _plot_api: @@ -54,3 +61,9 @@ Plot Utilities .. autosummary:: :toctree: generated/ + aaanalysis.plot_settings + aaanalysis.plot_set_legend + aaanalysis.plot_gcfs + aaanalysis.plot_get_cmap + aaanalysis.plot_get_cdict + diff --git a/docs/build/html/_sources/generated/aaanalysis.AAclust.rst.txt b/docs/build/html/_sources/generated/aaanalysis.AAclust.rst.txt new file mode 100644 index 00000000..f6e6666c --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.AAclust.rst.txt @@ -0,0 +1,28 @@ +aaanalysis.AAclust +================== + +.. currentmodule:: aaanalysis + +.. autoclass:: AAclust + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~AAclust.__init__ + ~AAclust.cluster_naming + ~AAclust.correlation + ~AAclust.eval + ~AAclust.fit + ~AAclust.get_cluster_centers + ~AAclust.get_cluster_medoids + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.CPP.rst.txt b/docs/build/html/_sources/generated/aaanalysis.CPP.rst.txt new file mode 100644 index 00000000..cdb62bd1 --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.CPP.rst.txt @@ -0,0 +1,24 @@ +aaanalysis.CPP +============== + +.. currentmodule:: aaanalysis + +.. autoclass:: CPP + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CPP.__init__ + ~CPP.eval + ~CPP.run + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.CPPPlot.rst.txt b/docs/build/html/_sources/generated/aaanalysis.CPPPlot.rst.txt new file mode 100644 index 00000000..a4dc39ae --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.CPPPlot.rst.txt @@ -0,0 +1,25 @@ +aaanalysis.CPPPlot +================== + +.. currentmodule:: aaanalysis + +.. autoclass:: CPPPlot + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CPPPlot.__init__ + ~CPPPlot.heatmap + ~CPPPlot.profile + ~CPPPlot.update_seq_size + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.SequenceFeature.rst.txt b/docs/build/html/_sources/generated/aaanalysis.SequenceFeature.rst.txt new file mode 100644 index 00000000..336e5607 --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.SequenceFeature.rst.txt @@ -0,0 +1,30 @@ +aaanalysis.SequenceFeature +========================== + +.. currentmodule:: aaanalysis + +.. autoclass:: SequenceFeature + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~SequenceFeature.__init__ + ~SequenceFeature.add_dif + ~SequenceFeature.add_feat_value + ~SequenceFeature.add_position + ~SequenceFeature.feat_matrix + ~SequenceFeature.feat_names + ~SequenceFeature.get_df_parts + ~SequenceFeature.get_features + ~SequenceFeature.get_split_kws + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.dPULearn.rst.txt b/docs/build/html/_sources/generated/aaanalysis.dPULearn.rst.txt new file mode 100644 index 00000000..fb647fa9 --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.dPULearn.rst.txt @@ -0,0 +1,24 @@ +aaanalysis.dPULearn +=================== + +.. currentmodule:: aaanalysis + +.. autoclass:: dPULearn + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~dPULearn.__init__ + ~dPULearn.eval + ~dPULearn.fit + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.load_dataset.rst.txt b/docs/build/html/_sources/generated/aaanalysis.load_dataset.rst.txt new file mode 100644 index 00000000..2b40842c --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.load_dataset.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.load\_dataset +======================== + +.. currentmodule:: aaanalysis + +.. autofunction:: load_dataset \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.load_scales.rst.txt b/docs/build/html/_sources/generated/aaanalysis.load_scales.rst.txt new file mode 100644 index 00000000..93d4717b --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.load_scales.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.load\_scales +======================= + +.. currentmodule:: aaanalysis + +.. autofunction:: load_scales \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.plot_gcfs.rst.txt b/docs/build/html/_sources/generated/aaanalysis.plot_gcfs.rst.txt new file mode 100644 index 00000000..dc10a3bc --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.plot_gcfs.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.plot\_gcfs +===================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_gcfs \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.plot_get_cdict.rst.txt b/docs/build/html/_sources/generated/aaanalysis.plot_get_cdict.rst.txt new file mode 100644 index 00000000..b00a8b2e --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.plot_get_cdict.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.plot\_get\_cdict +=========================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_get_cdict \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.plot_get_cmap.rst.txt b/docs/build/html/_sources/generated/aaanalysis.plot_get_cmap.rst.txt new file mode 100644 index 00000000..6ffe21f7 --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.plot_get_cmap.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.plot\_get\_cmap +========================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_get_cmap \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.plot_set_legend.rst.txt b/docs/build/html/_sources/generated/aaanalysis.plot_set_legend.rst.txt new file mode 100644 index 00000000..8c082d21 --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.plot_set_legend.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.plot\_set\_legend +============================ + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_set_legend \ No newline at end of file diff --git a/docs/build/html/_sources/generated/aaanalysis.plot_settings.rst.txt b/docs/build/html/_sources/generated/aaanalysis.plot_settings.rst.txt new file mode 100644 index 00000000..b27c5b2f --- /dev/null +++ b/docs/build/html/_sources/generated/aaanalysis.plot_settings.rst.txt @@ -0,0 +1,6 @@ +aaanalysis.plot\_settings +========================= + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_settings \ No newline at end of file diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 2206616b..57d04826 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -5,6 +5,7 @@ Welcome to the AAanalysis documentation ======================================= +.. include:: index/badges.rst .. include:: index/overview.rst Install @@ -24,12 +25,14 @@ Install :caption: OVERVIEW index/introduction.rst + index/usage_principles.rst index/CONTRIBUTING_COPY.rst .. toctree:: :maxdepth: 1 :caption: EXAMPLES + tutorials.rst .. toctree:: :maxdepth: 2 @@ -40,6 +43,7 @@ Install .. toctree:: :maxdepth: 1 + _index/tables.rst index/references.rst Indices and tables diff --git a/docs/build/html/_sources/index/tables_template.rst.txt b/docs/build/html/_sources/index/tables_template.rst.txt new file mode 100644 index 00000000..13007991 --- /dev/null +++ b/docs/build/html/_sources/index/tables_template.rst.txt @@ -0,0 +1,44 @@ +.. + Developer Notes: + This is the index file for all tables of the AAanalysis documentation. Each table should be saved the /tables + directory. This file will serve as template for tables.rst, which is automatically created on the information + provided here and in the .csv tables from the /tables directory. Add a new table as .csv in the /tables directory, + in the overview table at the beginning of this document, and a new section with a short description of it in this + document. Each column and important data types (e.g., categories) should be described. Each table should contain a + 'Reference' column. + Ignore 'tables_template.rst: WARNING: document isn't included in any toctree' warning + +Tables +====================== + +.. contents:: + :local: + :depth: 1 + +Overview Table +-------------- +All tables from the AAanalysis documentation are given here in chronological order of the project history. + +.. _0_mapper: + +Protein benchmark datasets +-------------------------- +Three types of benchmark datasets are provided: + +- Residue prediction (AA): Datasets used to predict residue (amino acid) specific properties. +- Domain prediction (DOM): Dataset used to predict domain specific properties. +- Sequence prediction (SEQ): Datasets used to predict sequence specific properties. + +The classification of each dataset is indicated as first part of their name followed by an abbreviation for the +specific dataset (e.g., 'AA_LDR', 'DOM_GSEC', 'SEQ_AMYLO'). For some datasets, an additional version of it is provided +for positive-unlabeled (PU) learning containing only positive (1) and unlabeled (2) data samples, as indicated by +*dataset_name_PU* (e.g., 'DOM_GSEC_PU'). + +.. _1_overview_benchmarks: + +Amino acid scale datasets +------------------------- +Different amino acid scale datasets are provided + +.. _2_overview_scales: + diff --git a/docs/build/html/_sources/index/usage_principles.rst.txt b/docs/build/html/_sources/index/usage_principles.rst.txt new file mode 100644 index 00000000..c86f9432 --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles.rst.txt @@ -0,0 +1,22 @@ +.. Developer Notes: + This is the index file for usage principles. Files for each part are saved in the /usage_principles directory + and the overview the AAanalysis package is given as component diagram (internal dependencies) and context diagram + (external dependencies). Always give the concise code examples reflecting the usage examples. Instead of including + comprehensive tables here, add them in tables.rst and refer to them with a short explanation + +Usage Principles +================ +Import AAanalysis as: + +.. code-block:: python + + import aaanalysis as aa + +.. toctree:: + :maxdepth: 1 + + usage_principles/data_flow_entry_points + usage_principles/aaontology + usage_principles/feature_identification + usage_principles/pu_learning + usage_principles/xai diff --git a/docs/build/html/_sources/index/usage_principles/aaontology.rst.txt b/docs/build/html/_sources/index/usage_principles/aaontology.rst.txt new file mode 100644 index 00000000..90620117 --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles/aaontology.rst.txt @@ -0,0 +1,5 @@ +AAontology: Classification of amino acid scales +=============================================== + +AAontology is a two-level classification of amino acid scale, introduced in. + diff --git a/docs/build/html/_sources/index/usage_principles/data_flow_entry_points.rst.txt b/docs/build/html/_sources/index/usage_principles/data_flow_entry_points.rst.txt new file mode 100644 index 00000000..8e8af181 --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles/data_flow_entry_points.rst.txt @@ -0,0 +1,8 @@ +Data Flow and Enry Points +========================= + +The AAanalysis toolkit uses different DataFrames starting from DataFrames containing amino acid scales information +(df_scales, df_cat) or sequence information (df_seq), which can be modified to obtain specific sequence parts (df_parts). +Amino acid scales and sequence parts together with split settings are the input for the CPP algorithm, creating +various physicochemical features (df_feat) by comparing two sets of protein sequences. + diff --git a/docs/build/html/_sources/index/usage_principles/feature_identification.rst.txt b/docs/build/html/_sources/index/usage_principles/feature_identification.rst.txt new file mode 100644 index 00000000..27b8acf1 --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles/feature_identification.rst.txt @@ -0,0 +1,7 @@ +Identifying Physicochemical Signatures using CPP +================================================ + +The central algorithm of the AAanalysis platform is Comparative Physicochemical Profiling (CPP), a novel sequence-based +feature engineering algorithm, designed to enable interpretable protein prediction. + + diff --git a/docs/build/html/_sources/index/usage_principles/pu_learning.rst.txt b/docs/build/html/_sources/index/usage_principles/pu_learning.rst.txt new file mode 100644 index 00000000..5020f5a7 --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles/pu_learning.rst.txt @@ -0,0 +1,17 @@ +Learning from unbalanced and small data +======================================= + +Unbalanced and small datasets are everywhere in life science .... + +In a standard binary classification setup, data with positive (1) and negative (0) labels are provided, which can be +used for training by machine learning models. If only a view samples of the negative class exist, data augmentation +techniques (e.g., SMOTE) can be used to extend the negative dataset by artificially generated sequences. Such approaches +are very popular for deep learning-based image recognition, but not feasible for protein sequence prediction tasks +because slight amino acid mutations (sequence alterations or perturbations) can already have dramatic biological effects. +Alternatively, negatives samples can be identified from unlabeled samples (2), which often exist in great quantities. +These unlabeled samples should be biologically as similar as possible to the positive class, beside not containing +the features distinguishing the positive from the negative class. For example, . + +What is PU learning? +-------------------- +Positive Unlabeled (PU) learning is a subfield of machine learning ... \ No newline at end of file diff --git a/docs/build/html/_sources/index/usage_principles/xai.rst.txt b/docs/build/html/_sources/index/usage_principles/xai.rst.txt new file mode 100644 index 00000000..8357963d --- /dev/null +++ b/docs/build/html/_sources/index/usage_principles/xai.rst.txt @@ -0,0 +1,8 @@ +Explainable AI at Sequence Level +================================ + +Unbalanced and small datasets are everywhere in life science ... + +What is explainable AI? +----------------------- + diff --git a/docs/build/html/api.html b/docs/build/html/api.html index a3b83812..2d882f9b 100644 --- a/docs/build/html/api.html +++ b/docs/build/html/api.html @@ -40,8 +40,8 @@ - - + + @@ -70,21 +70,47 @@

      OVERVIEW

      +

      EXAMPLES

      +

      REFERENCES

      @@ -125,6 +151,12 @@

      API

      Data

      + + + + + +

      aaanalysis.load_dataset([name, n, random, ...])

      Load protein benchmarking datasets.

      aaanalysis.load_scales([name, just_aaindex, ...])

      Load amino acid scales, scale classification (AAontology), or scale evaluation.

      @@ -132,6 +164,18 @@

      API

      Feature Engineering

      + + + + + + + + + + + +

      aaanalysis.AAclust([model, model_kwargs, ...])

      AAclust: A k-optimized clustering framework for selecting redundancy-reduced set of numerical scales.

      aaanalysis.SequenceFeature()

      Retrieve and create sequence feature components (Part, Split, and Scale).

      aaanalysis.CPP([df_scales, df_cat, ...])

      Create and filter features that are most discriminant between two sets of sequences.

      aaanalysis.CPPPlot([df_cat, accept_gaps, ...])

      Create and filter features that are most discriminant between two sets of sequences.

      @@ -139,6 +183,9 @@

      API

      PU Learning

      + + +

      aaanalysis.dPULearn([verbose, n_components, ...])

      Deterministic Positive-Unlabeled (dPULearn) model.

      @@ -152,6 +199,21 @@

      Perturbation

      + + + + + + + + + + + + + + +

      aaanalysis.plot_settings([fig_format, ...])

      Configure general settings for plot visualization with various customization options.

      aaanalysis.plot_set_legend([ax, handles, ...])

      Set a customizable legend for a plot.

      aaanalysis.plot_gcfs()

      Get current font size, which is set by ut.plot_settings function

      aaanalysis.plot_get_cmap([name, n_colors, ...])

      Retrieve color maps or color dictionaries specified for AAanalysis.

      aaanalysis.plot_get_cdict([name])

      Retrieve color dictionaries specified for AAanalysis.

      @@ -161,8 +223,8 @@

      Plot Utilities - - + +
      diff --git a/docs/build/html/generated/aaanalysis.AAclust.html b/docs/build/html/generated/aaanalysis.AAclust.html new file mode 100644 index 00000000..7cbb69f9 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.AAclust.html @@ -0,0 +1,428 @@ + + + + + + + + + + + + + + + + aaanalysis.AAclust — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.AAclust

      +
      +
      +class aaanalysis.AAclust(model=None, model_kwargs=None, verbose=False)[source]
      +

      Bases: object

      +

      AAclust: A k-optimized clustering framework for selecting redundancy-reduced set of numerical scales.

      +

      AAclust is designed primarily for amino acid scales but is versatile enough for any set of numerical indices. +It takes clustering models that require a pre-defined number of clusters (k) from +scikit-learn <https://scikit-learn.org/stable/modules/clustering.html>. By leveraging Pearson correlation as +similarity measure, AAclust optimizes the value of k. It then selects one representative sample (termed as ‘medoid’) +for each cluster, which is the closest to the cluster’s center, yielding a redundancy-reduced sample set.

      +
      +
      Parameters:
      +
        +
      • model (callable, optional, default = sklearn.cluster.KMeans) – The employed clustering model requiring pre-defined number of clusters ‘k’, given as ‘n_clusters’ parameter.

      • +
      • model_kwargs (dict, optional, default = {}) – A dictionary of keyword arguments to pass to the selected clustering model.

      • +
      • verbose (bool, optional, default = False) – A flag to enable or disable verbose outputs.

      • +
      +
      +
      +
      +
      +n_clusters
      +

      Number of clusters obtained by AAclust.

      +
      +
      Type:
      +

      int, default = None

      +
      +
      +
      + +
      +
      +labels_
      +

      Cluster labels in the order of samples in the feature matrix.

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +centers_
      +

      Average scale values corresponding to each cluster.

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +center_labels_
      +

      Cluster labels for each cluster center.

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +medoids_
      +

      Representative samples (one for each cluster center).

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +medoid_labels_
      +

      Cluster labels for each medoid.

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +medoid_ind_
      +

      Indices of the chosen medoids within the original dataset.

      +
      +
      Type:
      +

      array-like, default = None

      +
      +
      +
      + +
      +
      +__init__(model=None, model_kwargs=None, verbose=False)[source]
      +
      + +

      Methods

      + + + + + + + + + + + + + + + + + + + + + + + + +

      __init__([model, model_kwargs, verbose])

      cluster_naming([names, labels, ...])

      Assigns names to clusters based on scale names and their frequency.

      correlation(X_test, X_ref[, labels_test, ...])

      Computes the correlation of test data with reference cluster centers.

      eval()

      fit(X[, names, on_center, min_th, ...])

      Fit the AAclust model on the data, optimizing cluster formation using Pearson correlation.

      get_cluster_centers(X[, labels])

      Computes the center of each cluster based on the given labels.

      get_cluster_medoids(X[, labels])

      Computes the medoid of each cluster based on the given labels.

      +
      +
      +fit(X, names=None, on_center=True, min_th=0, merge_metric='euclidean', n_clusters=None)[source]
      +

      Fit the AAclust model on the data, optimizing cluster formation using Pearson correlation.

      +

      AAclust determines the optimal number of clusters, k, without pre-specification. It partitions data(X) into +clusters by maximizing the within-cluster Pearson correlation beyond the ‘min_th’ threshold. The quality of +clustering is either based on the minimum Pearson correlation of all members (‘min_cor all’) or between +the cluster center and its members (‘min_cor center’), governed by on_center.

      +

      The clustering undergoes three stages: +1. Estimate the lower bound of k. +2. Refine k using the chosen quality metric. +3. Optionally merge smaller clusters, as directed by merge_metric.

      +

      Finally, a representative scale (medoid) ‘closest to each cluster center is chosen for redundancy reduction.

      +
      +
      Parameters:
      +
        +
      • X (array-like, shape (n_samples, n_features)) – Feature matrix where n_samples is the number of samples and n_features is the number of features.

      • +
      • names (list of str, optional) – Sample names. If provided, returns names of the medoids.

      • +
      • on_center (bool, default = True) – If True, the correlation threshold is applied to the cluster center. Otherwise, it’s applied to all cluster members.

      • +
      • min_th (float, default = 0) – Pearson correlation threshold for clustering (between 0 and 1).

      • +
      • merge_metric (str or None, default = "euclidean") – Metric used for optional cluster merging. Can be “euclidean”, “pearson”, or None (no merging).

      • +
      • n_clusters (int, optional) – Pre-defined number of clusters. If provided, AAclust uses this instead of optimizing k.

      • +
      +
      +
      Returns:
      +

      names_medoid – Names of the medoids.

      +
      +
      Return type:
      +

      list of str, if names is provided

      +
      +
      +
      +

      Notes

      +

      The ‘fit’ method sets the following attributes: :attr: aaanalysis.AAclust.n_clusters”, +:attr: `aaanalysis.AAclust.labels_, :attr: aaanalysis.AAclust.centers_, +:attr: aaanalysis.AAclust.center_labels_, :attr: aaanalysis.AAclust.medoids_. +:attr: aaanalysis.AAclust.medoid_labels_, :attr: aaanalysis.AAclust.medoid_ind_.

      +

      For further information, refer to the AAclust paper : TODO: add link to AAclust paper

      +
      +
      + +
      +
      +cluster_naming(names=None, labels=None, name_unclassified='Unclassified')[source]
      +

      Assigns names to clusters based on scale names and their frequency.

      +

      This method renames clusters based on the names of the scales in each cluster, with priority given to the +most frequent scales. If the name is already used or does not exist, it defaults to ‘name_unclassified’.

      +
      +
      Parameters:
      +
        +
      • names (list, optional) – List of scale names corresponding to each sample.

      • +
      • labels (list, optional) – Cluster labels. If not provided, uses the labels from the fitted model.

      • +
      • name_unclassified (str, default = "Unclassified") – Name assigned to clusters that cannot be classified with the given names.

      • +
      +
      +
      Returns:
      +

      cluster_names – A list of renamed clusters based on scale names.

      +
      +
      Return type:
      +

      list

      +
      +
      +
      + +
      +
      +static get_cluster_centers(X, labels=None)[source]
      +

      Computes the center of each cluster based on the given labels.

      +
      +
      Parameters:
      +
        +
      • X (array-like, shape (n_samples, n_features)) – Feature matrix where n_samples is the number of samples and n_features is the number of features.

      • +
      • labels (list or array-like, optional) – Cluster labels for each sample in X.

      • +
      +
      +
      Returns:
      +

        +
      • centers (array-like) – The computed center for each cluster.

      • +
      • center_labels (array-like) – The labels associated with each computed center.

      • +
      +

      +
      +
      +
      + +
      +
      +static get_cluster_medoids(X, labels=None)[source]
      +

      Computes the medoid of each cluster based on the given labels.

      +
      +
      Parameters:
      +

      X (array-like, shape (n_samples, n_features)) – Feature matrix where n_samples is the number of samples and n_features is the number of features.

      +
      +
      +
      +
      labelslist or array-like, optional

      Cluster labels for each sample in X.

      +
      +
      +
      +
      Returns:
      +

        +
      • medoids (array-like) – The medoid for each cluster.

      • +
      • medoid_labels (array-like) – The labels corresponding to each medoid.

      • +
      • medoid_ind (array-like) – Indexes of medoids within the original data.

      • +
      +

      +
      +
      +
      + +
      +
      +static correlation(X_test, X_ref, labels_test=None, labels_ref=None, n=3, positive=True, on_center=False, except_unclassified=True)[source]
      +

      Computes the correlation of test data with reference cluster centers.

      +
      +
      Parameters:
      +
        +
      • X_test (array-like) – Test feature matrix.

      • +
      • X_ref (array-like) – Reference feature matrix.

      • +
      • labels_test (list or array-like, optional) – Cluster labels for the test data.

      • +
      • labels_ref (list or array-like, optional) – Cluster labels for the reference data.

      • +
      • n (int, default = 3) – Number of top centers to consider based on correlation strength.

      • +
      • positive (bool, default = True) – If True, considers positive correlations. Else, negative correlations.

      • +
      • on_center (bool, default = False) – If True, correlation is computed with cluster centers. Otherwise, with all cluster members.

      • +
      • except_unclassified (bool, default = True) – If True, excludes ‘unclassified’ clusters from the reference list.

      • +
      +
      +
      Returns:
      +

      list_top_center_name_corr – Names and correlations of centers having strongest (positive/negative) correlation with test data samples.

      +
      +
      Return type:
      +

      list of str

      +
      +
      +
      + +
      +
      +eval()[source]
      +
      + +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.CPP.html b/docs/build/html/generated/aaanalysis.CPP.html new file mode 100644 index 00000000..204f2511 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.CPP.html @@ -0,0 +1,261 @@ + + + + + + + + + + + + + + + + aaanalysis.CPP — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.CPP

      +
      +
      +class aaanalysis.CPP(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]
      +

      Bases: Tool

      +

      Create and filter features that are most discriminant between two sets of sequences.

      +
      +
      Parameters:
      +
        +
      • df_scales (pandas.DataFrame) – DataFrame with amino acid scales.

      • +
      • df_cat (pandas.DataFrame, default = aa.load_categories) – DataFrame with default categories for physicochemical amino acid scales.

      • +
      • df_parts (pandas.DataFrame) – DataFrame with sequence parts.

      • +
      • split_kws (dict, default = SequenceFeature.get_split_kws) – Nested dictionary with parameter dictionary for each chosen split_type.

      • +
      • accept_gaps (bool, default = False) – Whether to accept missing values by enabling omitting for computations (if True).

      • +
      • verbose (bool, default = True) – Whether to print progress information about the algorithm (if True).

      • +
      +
      +
      +
      +

      Notes

      +

      The CPP.run() method performs all steps of the CPP algorithm.

      +
      +
      +
      +__init__(df_scales=None, df_cat=None, df_parts=None, split_kws=None, accept_gaps=False, verbose=True)[source]
      +
      + +

      Methods

      + + + + + + + + + + + + +

      __init__([df_scales, df_cat, df_parts, ...])

      eval([df_feat, features])

      Get evaluation for provided dataset

      run([labels, parametric, n_filter, tmd_len, ...])

      Perform CPP pipeline by creation and two-step filtering of features.

      +
      +
      +run(labels=None, parametric=False, n_filter=100, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, start=1, check_cat=True, n_pre_filter=None, pct_pre_filter=5, max_std_test=0.2, max_overlap=0.5, max_cor=0.5, n_processes=None)[source]
      +

      Perform CPP pipeline by creation and two-step filtering of features. CPP aims to +identify a collection of non-redundant features that are most discriminant between +a test and a reference group of sequences.

      +
      +
      Parameters:
      +
        +
      • labels (array-like, shape (n_samples)) – Class labels for samples in sequence DataFrame (test=1, reference=0).

      • +
      • parametric (bool, default = False) – Whether to use parametric (T-test) or non-parametric (U-test) test for p-value computation.

      • +
      • n_filter (int, default = 100) – Number of features to be filtered/selected by CPP algorithm.

      • +
      • n_pre_filter (int, optional) – Number of feature to be pre-filtered by CPP algorithm. If None, a percentage of all features is used.

      • +
      • tmd_len (int, >0) – Length of TMD used for positions. TODO add link to explanation

      • +
      • start (int, >=0) – Position label of first amino acid position (starting at N-terminus).

      • +
      • jmd_n_len (int, >=0, default = 10) – Length of JMD-N.

      • +
      • jmd_c_len (int, >=0, default = 10) – Length of JMD-C.

      • +
      • ext_len (int, >=0, default = 4) – Length of TMD-extending part (starting from C and N terminal part of TMD). +Should be longer than jmd_n_len and jmd_c_len

      • +
      • check_cat (bool, default = True) – Whether to check for redundancy within scale categories.

      • +
      • pct_pre_filter (int, default = 5) – Percentage of all features that should remain after the pre-filtering step.

      • +
      • max_std_test (float [0-1], default = 0.2) – Maximum standard deviation within the test group used as threshold for pre-filtering.

      • +
      • max_overlap (float [0-1], default = 0.5) – Maximum positional overlap of features used as threshold for filtering.

      • +
      • max_cor (float [0-1], default = 0.5) – Maximum Pearson correlation of features used as threshold for filtering.

      • +
      • n_processes (int, default = None) – Number of CPUs used for multiprocessing. If None, number will be optimized automatically.

      • +
      +
      +
      Returns:
      +

      df_feat – DataFrame with a unique identifier, scale information, statistics, and positions for each feature.

      +
      +
      Return type:
      +

      pandas.DataFrame, shape (n_feature, n_feature_information)

      +
      +
      +
      +

      Notes

      +

      The feature DataFrame contains the following eleven columns, including the unique +feature id (1), scale information (2-4), statistical results used for filtering and +ranking (5-10), and feature positions (11):

      +
        +
      1. features: Feature ID (PART-SPLIT-SCALE)

      2. +
      3. category: Scale category

      4. +
      5. subcategory: Sub category of scales

      6. +
      7. scale_name: Name of scales

      8. +
      9. abs_auc: Absolute adjusted AUC [-0.5 to 0.5]

      10. +
      11. abs_mean_dif: Absolute mean differences between test and reference group [0 to 1]

      12. +
      13. std_test: Standard deviation in test group

      14. +
      15. std_ref: Standard deviation in reference group

      16. +
      17. p_val: Non-parametric (mann_whitney) or parametric (ttest_indep) statistic

      18. +
      19. p_val_fdr_bh: Benjamini-Hochberg FDR corrected p-values

      20. +
      21. positions: Feature positions for default settings

      22. +
      +
      +
      + +
      +
      +static eval(df_feat=None, features=None)[source]
      +

      Get evaluation for provided dataset

      +
      + +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.CPPPlot.html b/docs/build/html/generated/aaanalysis.CPPPlot.html new file mode 100644 index 00000000..81a3e3ee --- /dev/null +++ b/docs/build/html/generated/aaanalysis.CPPPlot.html @@ -0,0 +1,346 @@ + + + + + + + + + + + + + + + + aaanalysis.CPPPlot — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.CPPPlot

      +
      +
      +class aaanalysis.CPPPlot(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]
      +

      Bases: object

      +

      Create and filter features that are most discriminant between two sets of sequences.

      +
      +
      Parameters:
      +
        +
      • accept_gaps (bool, default = False) – Whether to accept missing values by enabling omitting for computations (if True).

      • +
      • jmd_n_len (int, >=0, default = 10) – Length of JMD-N.

      • +
      • jmd_c_len (int, >=0, default = 10) – Length of JMD-C.

      • +
      • ext_len (int, >=0, default = 4) – Length of TMD-extending part (starting from C and N terminal part of TMD). +Conditions: ext_len < jmd_m_len and ext_len < jmd_c_len.

      • +
      • verbose (bool, default = True) – Whether to print progress information about the algorithm (if True).

      • +
      +
      +
      +
      +
      +__init__(df_cat=None, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, ext_len=4, verbose=True)[source]
      +
      + +

      Methods

      + + + + + + + + + + + + + + + +

      __init__([df_cat, accept_gaps, jmd_n_len, ...])

      heatmap([df_feat, y, val_col, val_type, ...])

      Plot a featuremap of the selected value column with scale information (y-axis) versus sequence position (x-axis).

      profile([df_feat, y, val_col, val_type, ...])

      Plot feature profile for given features from 'df_feat'.

      update_seq_size()

      +
      +
      +profile(df_feat=None, y='category', val_col='mean_dif', val_type='count', normalize=False, figsize=(7, 5), title=None, title_kws=None, dict_color=None, edge_color='none', bar_width=0.75, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, xticks_pos=False, ytick_size=None, ytick_width=2.0, ytick_length=5.0, ylim=None, highlight_tmd_area=True, highlight_alpha=0.15, grid=False, grid_axis='both', add_legend_cat=True, legend_kws=None, shap_plot=False, **kwargs)[source]
      +

      Plot feature profile for given features from ‘df_feat’.

      +
      +
      Parameters:
      +
        +
      • df_feat (class:pandas.DataFrame, optional, default=None) – Dataframe containing the features to be plotted. If None, default features from the instance will be used.

      • +
      • y (str, default='category') – Column name in df_feat which contains the categories for grouping.

      • +
      • val_col (str, default='mean_dif') – Column name in df_feat which contains the values to be plotted.

      • +
      • val_type (str, default='count') – Type of value. Available options are specified by the check_value_type function.

      • +
      • normalize (bool, default=False) – If True, the feature values will be normalized.

      • +
      • figsize (tuple, default=(7, 5)) – Size of the plot.

      • +
      • title (str, optional) – Title of the plot.

      • +
      • title_kws (dict, optional) – Keyword arguments to customize the title appearance.

      • +
      • dict_color (dict, optional) – Dictionary mapping categories to colors.

      • +
      • edge_color (str, default='none') – Color of the edges of the bars.

      • +
      • bar_width (float, default=0.75) – Width of the bars.

      • +
      • add_jmd_tmd (bool, default=True) – If True, adds JMD and TMD lines/annotations to the plot.

      • +
      • tmd_len (int, default=20) – Length of the TMD.

      • +
      • start (int, default=1) – Start position.

      • +
      • jmd_n_seq (str, optional) – JMD N-terminal sequence.

      • +
      • tmd_seq (str, optional) – TMD sequence.

      • +
      • jmd_c_seq (str, optional) – JMD C-terminal sequence.

      • +
      • tmd_color (str, default='mediumspringgreen') – Color for TMD.

      • +
      • jmd_color (str, default='blue') – Color for JMD.

      • +
      • tmd_seq_color (str, default='black') – Color for TMD sequence.

      • +
      • jmd_seq_color (str, default='white') – Color for JMD sequence.

      • +
      • seq_size (float, optional) – Font size for sequence annotations.

      • +
      • tmd_jmd_fontsize (float, optional) – Font size for TMD and JMD annotations.

      • +
      • xtick_size (float, default=11.0) – Size for x-tick labels.

      • +
      • xtick_width (float, default=2.0) – Width of the x-ticks.

      • +
      • xtick_length (float, default=5.0) – Length of the x-ticks.

      • +
      • xticks_pos (bool, default=False) – If True, x-tick positions are adjusted based on given sequences.

      • +
      • ytick_size (float, optional) – Size for y-tick labels.

      • +
      • ytick_width (float, default=2.0) – Width of the y-ticks.

      • +
      • ytick_length (float, default=5.0) – Length of the y-ticks.

      • +
      • ylim (tuple, optional) – Y-axis limits.

      • +
      • highlight_tmd_area (bool, default=True) – If True, highlights the TMD area on the plot.

      • +
      • highlight_alpha (float, default=0.15) – Alpha value for TMD area highlighting.

      • +
      • grid (bool, default=False) – If True, a grid is added to the plot.

      • +
      • grid_axis (str, default='both') – Axis on which the grid is drawn. Options: ‘both’, ‘x’, ‘y’.

      • +
      • add_legend_cat (bool, default=True) – If True, a legend is added for categories.

      • +
      • legend_kws (dict, optional) – Keyword arguments for the legend.

      • +
      • shap_plot (bool, default=False) – If True, SHAP (SHapley Additive exPlanations) plot is generated.

      • +
      • **kwargs (dict) – Other keyword arguments passed to internal functions or plotting libraries.

      • +
      +
      +
      Returns:
      +

      ax – The axes object containing the plot.

      +
      +
      Return type:
      +

      matplotlib.axes.Axes

      +
      +
      +
      + +
      +
      +heatmap(df_feat=None, y='subcategory', val_col='mean_dif', val_type='mean', normalize=False, figsize=(8, 5), title=None, title_kws=None, vmin=None, vmax=None, grid_on=True, cmap='RdBu_r', cmap_n_colors=None, dict_color=None, cbar_kws=None, facecolor_dark=False, add_jmd_tmd=True, tmd_len=20, start=1, jmd_n_seq=None, tmd_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size=None, tmd_jmd_fontsize=None, xticks_pos=False, xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, ytick_size=None, add_legend_cat=True, legend_kws=None, add_importance_map=False, cbar_pct=False, **kwargs)[source]
      +

      Plot a featuremap of the selected value column with scale information (y-axis) versus sequence position (x-axis).

      +

      This is a wrapper function for seaborn.heatmap(), designed to highlight differences between two sets +of sequences at the positional level (e.g., amino acid level for protein sequences).

      +
      +
      Parameters:
      +
        +
      • df_feat (DataFrame, shape (n_feature, n_feature_information)) – DataFrame containing unique identifiers, scale information, statistics, and positions for each feature.

      • +
      • y ({'category', 'subcategory', 'scale_name'}, str, default = 'subcategory') – Name of the column in the feature DataFrame representing scale information (shown on the y-axis).

      • +
      • val_col ({'mean_dif', 'feat_impact', 'abs_auc', 'std_test', ...}, str, default = 'mean_dif') – Name of the column in the feature DataFrame containing numerical values to display.

      • +
      • val_type ({'mean', 'sum', 'std'}, str, default = 'mean') – Method to aggregate numerical values from ‘val_col’.

      • +
      • normalize ({True, False, 'positions', 'positions_only'}, bool/str, default = False) –

        Specifies normalization for numerical values in ‘val_col’:

        +
          +
        • False: Set value at all positions of a feature without further normalization.

        • +
        • True: Set value at all positions of a feature and normalize across all features.

        • +
        • ’positions’: Value/number of positions set at each position of a feature and normalized across features. +Recommended when aiming to emphasize features with fewer positions using ‘val_col’=’feat_impact’ and ‘value_type’=’mean’.

        • +
        +

      • +
      • figsize (tuple(float, float), default = (10,7)) – Width and height of the figure in inches passed to matplotlib.pyplot.figure().

      • +
      • title (str, optional) – Title of figure used by matplotlib.pyplot.title().

      • +
      • title_kws (dict, optional) – Keyword arguments passed to matplotlib.pyplot.title().

      • +
      • vmin (float, optional) – Values to anchor the colormap, otherwise, inferred from data and other keyword arguments.

      • +
      • vmax (float, optional) – Values to anchor the colormap, otherwise, inferred from data and other keyword arguments.

      • +
      • cmap (matplotlib colormap name or object, or list of colors, default = 'seismic') – Name of color map assigning data values to color space. If ‘SHAP’, colors from +SHAP will be used (recommended for feature impact).

      • +
      • cmap_n_colors (int, optional) – Number of discrete steps in diverging or sequential color map.

      • +
      • dict_color (dict, optional) – Map of colors for scale categories classifying scales shown on y-axis.

      • +
      • cbar_kws (dict of key, value mappings, optional) – Keyword arguments for matplotlib.figure.Figure.colorbar().

      • +
      • add_jmd_tmd (bool, default = True) – Whether to add colored bar under heatmap indicating sequence parts (JMD-N, TMD, JMD-C).

      • +
      • tmd_len (int, >0) – Length of TMD to be depiceted.

      • +
      • start (int, >=0) – Position label of first amino acid position (starting at N-terminus).

      • +
      • tmd_seq (str, optional) – Sequence of TMD. ‘tmd_len’ is set to length of TMD if sequence for TMD, JMD-N and JMD-C are given. +Recommended if feature impact or mean difference should be depicted for one sample.

      • +
      • jmd_n_seq (str, optional) – Sequence of JMD_N. ‘jmd_n_len’ is set to length of JMD_N if sequence for TMD, JMD-N and JMD-C are given. +Recommended if feature impact or mean difference should be depicted for one sample.

      • +
      • jmd_c_seq (str, optional) – Sequence of JMD_C. ‘jmd_c_len’ is set to length of JMD_C if sequence for TMD, JMD-N and JMD-C are given. +Recommended if feature impact or mean difference should be depicted for one sample.

      • +
      • tmd_color (str, default = 'mediumspringgreen') – Color of TMD bar.

      • +
      • jmd_color (str, default = 'blue') – Color of JMD-N and JMD-C bar.

      • +
      • tmd_seq_color (str, default = 'black') – Color of TMD sequence.

      • +
      • jmd_seq_color (str, default = 'white') – Color of JMD-N and JMD-C sequence.

      • +
      • seq_size (float, optional) – Font size of all sequence parts in points. If None, optimized automatically.

      • +
      • tmd_jmd_fontsize (float, optional) – Font size of ‘TMD’, ‘JMD-N’ and ‘JMD-C’ label in points. If None, optimized automatically.

      • +
      • xtick_size (float, default = 11.0) – Size of x ticks in points. Passed as ‘size’ argument to matplotlib.axes.Axes.set_xticklabels().

      • +
      • xtick_width (float, default = 2.0) – Widht of x ticks in points. Passed as ‘width’ argument to matplotlib.axes.Axes.tick_params().

      • +
      • xtick_length (float, default = 5.0,) – Length of x ticks in points. Passed as ‘length’ argument to matplotlib.axes.Axes.tick_params().

      • +
      • ytick_size (float, optional) – Size of scale information as y ticks in points. Passed to matplotlib.axes.Axes.tick_params(). +If None, optimized automatically.

      • +
      • add_legend_cat (bool, default = True,) – Whether to add legend for categories under plot and classification of scales at y-axis.

      • +
      • legend_kws (dict, optional) – Keyword arguments passed to matplotlib.axes.Axes.legend()

      • +
      • kwargs (other keyword arguments) – All other keyword arguments passed to matplotlib.axes.Axes.pcolormesh().

      • +
      +
      +
      Returns:
      +

        +
      • ax (matplotlib Axes)

      • +
      • Axes object containing the heatmap.

      • +
      +

      +
      +
      +
      +

      Warning

      +
        +
      • ‘cmap_n_colors’ is effective only if ‘vmin’ and ‘vmax’ align with the data.

      • +
      • ‘tmd_seq_color’ and ‘jmd_seq_color’ are applicable only when ‘tmd_seq’, ‘jmd_n_seq’, and ‘jmd_c_seq’ are provided.

      • +
      +
      +
      +

      See also

      +
      +
      seaborn.heatmap

      Plotting heatmap using seaborn. See Seaborn documentation for more details.

      +
      +
      +
      +
      +

      Examples

      +

      Plot CPP feature heatmap:

      +

      (Source code)

      +
      +
      + +
      +
      +update_seq_size()[source]
      +
      + +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.SequenceFeature.html b/docs/build/html/generated/aaanalysis.SequenceFeature.html new file mode 100644 index 00000000..3182f271 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.SequenceFeature.html @@ -0,0 +1,496 @@ + + + + + + + + + + + + + + + + aaanalysis.SequenceFeature — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.SequenceFeature

      +
      +
      +class aaanalysis.SequenceFeature[source]
      +

      Bases: object

      +

      Retrieve and create sequence feature components (Part, Split, and Scale).

      +
      +

      Notes

      +
      +
      Part: Feature Component

      A continuous subset of a sequence like a protein domain (e.g, transmembrane domain of membrane proteins).

      +
      +
      Split: Feature Component

      Principle to obtain a distinct subset of amino acids from a sequence part like a segment or a pattern.

      +
      +
      Scale: Feature Component

      A physicochemical scale assigning each amino acid a numerical value between 0 and 1.

      +
      +
      Feature: Part + Split + Scale

      Physicochemical property (expressed as numerical scale) present at distinct amino acid +positions within a protein sequence. The positions are obtained by splitting sequence parts +into segments or patterns.

      +
      +
      Feature value: Realization of a Feature

      For a given sequence, a feature value is the average of a physicochemical scale over +all amino acids obtained by splitting a sequence part.

      +
      +
      List of valid sequence parts:

      [‘tmd’, ‘tmd_e’, ‘tmd_n’, ‘tmd_c’, ‘jmd_n’, ‘jmd_c’, ‘ext_c’, ‘ext_n’, +‘tmd_jmd’, ‘jmd_n_tmd_n’, ‘tmd_c_jmd_c’, ‘ext_n_tmd_n’, ‘tmd_c_ext_c’]

      +
      +
      +
      +
      +
      +__init__()
      +
      + +

      Methods

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      __init__()

      add_dif([df_feat, df_seq, labels, ...])

      Add feature value difference between sample and reference group to DataFrame.

      add_feat_value([df_parts, split, ...])

      Create feature values for all sequence parts by combining Part, Split, and Scale.

      add_position([df_feat, features, start, ...])

      Create list with positions for given feature names

      feat_matrix([features, df_parts, df_scales, ...])

      Create feature matrix for given feature ids and sequence parts.

      feat_names([features, df_cat, tmd_len, ...])

      Convert feature ids (PART-SPLIT-SCALE) into feature names (scale name [positions]).

      get_df_parts([df_seq, list_parts, ...])

      Create DataFrane with sequence parts.

      get_features([list_parts, split_kws, ...])

      Create list of all feature ids for given Parts, Splits, and Scales

      get_split_kws([n_split_min, n_split_max, ...])

      Create dictionary with kwargs for three split types: Segment, Pattern, PeriodicPattern

      +
      +
      +static get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, ext_len=None, all_parts=False)[source]
      +

      Create DataFrane with sequence parts.

      +
      +
      Parameters:
      +
        +
      • df_seq (pandas.DataFrame) – DataFrame with sequence information comprising either sequence (‘sequence’, ‘tmd_start’, ‘tmd_stop’) +or sequence part (‘jmd_n’, ‘tmd’, ‘jmd_c’) columns.

      • +
      • list_parts (list of string, len>=1) – Names of sequence parts which should be created (e.g., ‘tmd’).

      • +
      • jmd_n_len (int, default = None, optional) – Length of JMD-N in number of amino acids. If None, ‘jmd_n’ column must be given in df_seq.

      • +
      • jmd_c_len (int, default = None, optional) – Length of JMD-N in number of amino acids. If None, ‘jmd_c’ column must be given in df_seq.

      • +
      • ext_len (int, default = 4) – Lenght of N- resp. C-terminal extra part of TMD.

      • +
      • all_parts (bool, default = False) – Whether to create DataFrame with all possible sequence parts (if True) or parts given by list_parts.

      • +
      +
      +
      Returns:
      +

      df_parts – DataFrame with sequence parts.

      +
      +
      Return type:
      +

      pandas.DataFrame

      +
      +
      +
      +

      Notes

      +

      List of valid sequence parts can be found in :class: ´aaanalysis.SequenceFeature´.

      +
      +
      +

      Examples

      +

      Get sequence parts from df_seq with ‘tmd_e’, and ‘tmd_jmd’ as parts and jmd length of 10:

      +
      >>> import aaanalysis as aa
      +>>> sf = aa.SequenceFeature()
      +>>> df_seq = aa.load_dataset(name='DOM_GSEC')
      +>>> df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_e", "tmd_jmd"], jmd_n_len=10, jmd_c_len=10)
      +
      +
      +
      +
      + +
      +
      +static get_split_kws(n_split_min=1, n_split_max=15, steps_pattern=None, n_min=2, n_max=4, len_max=15, steps_periodicpattern=None, split_types=None)[source]
      +

      Create dictionary with kwargs for three split types: Segment, Pattern, PeriodicPattern

      +
      +
      Parameters:
      +
        +
      • n_split_min (int, default = 1) – Number greater 0 to specify the greatest Segment (e.g., 1/1 TMD alias whole TMD sequence).

      • +
      • n_split_max (int, default = 15,) – Number greater n_split_min to specfiy the smallest Segment (e.g., 1/15 TMD).

      • +
      • steps_pattern (list of integers, default = [3, 4, 6, 7, 8]) – Possible steps sizes for Pattern.

      • +
      • n_min (int, default = 2) – Minimum number of steps for Pattern.

      • +
      • n_max (int, default = 4) – Maximum number of steps for Pattern.

      • +
      • len_max (int, default = 10) – Maximum length in amino acid position for Pattern by varying start position.

      • +
      • steps_periodicpattern (list of integers, default = [3, 4]) – Step sizes for PeriodicPattern.

      • +
      • split_types (list of strings, default = ["Segment", "Pattern" "PeriodicPattern"]) – Split types for which paramter dictionary should be generated.

      • +
      +
      +
      Returns:
      +

      split_kws – Nested dictionary with parameters for chosen split_types:

      +
        +
      1. Segment: {n_split_min:1, n_split_max=15}

      2. +
      3. Pattern: {steps=[3, 4], n_min=2, n_max=4, len_max=15}

      4. +
      5. PeriodicPattern: {steps=[3, 4]}

      6. +
      +

      +
      +
      Return type:
      +

      dict

      +
      +
      +
      +

      Examples

      +

      Get default arguments for all splits types (Segment, Pattern, PeriodicPattern):

      +
      >>> import aaanalysis as aa
      +>>> sf = aa.SequenceFeature()
      +>>> split_kws = sf.get_split_kws()
      +
      +
      +

      Get default argumetns for Segment split:

      +
      >>> import aaanalysis as aa
      +>>> sf = aa.SequenceFeature()
      +>>> split_kws = sf.get_split_kws(split_types="Segment")
      +
      +
      +
      +
      + +
      +
      +get_features(list_parts=None, split_kws=None, df_scales=None, all_parts=False)[source]
      +

      Create list of all feature ids for given Parts, Splits, and Scales

      +
      +
      Parameters:
      +
        +
      • list_parts (list of strings (n>=1 parts), default = ["tmd_e", "jmd_n_tmd_n", "tmd_c_jmd_c"]) – Names of sequence parts which should be created (e.g., ‘tmd’).

      • +
      • split_kws (dict, default = SequenceFeature.get_split_kws) – Nested dictionary with parameter dictionary for each chosen split_type.

      • +
      • df_scales (pandas.DataFrame, default = SequenceFeature.load_scales) – DataFrame with default amino acid scales.

      • +
      • all_parts (bool, default = False) – Whether to create DataFrame with all possible sequence parts (if True) or parts given by list_parts.

      • +
      +
      +
      Returns:
      +

      features – Ids of all possible features for combination of Parts, Splits, and Scales with form: PART-SPLIT-SCALE

      +
      +
      Return type:
      +

      list of strings

      +
      +
      +
      + +
      +
      +static feat_matrix(features=None, df_parts=None, df_scales=None, accept_gaps=False, n_jobs=None, verbose=False, return_labels=False)[source]
      +

      Create feature matrix for given feature ids and sequence parts.

      +
      +
      Parameters:
      +
        +
      • features (str, list of strings, pd.Series) – Ids of features for which matrix of feature values should be created.

      • +
      • df_parts (pandas.DataFrame) – DataFrame with sequence parts.

      • +
      • df_scales (pandas.DataFrame, optional) – DataFrame with default amino acid scales.

      • +
      • accept_gaps (bool, default = False) – Whether to accept missing values by enabling omitting for computations (if True).

      • +
      • n_jobs (int, default = None,) – The number of jobs to run in parallel. If None, it will be set to the maximum.

      • +
      • verbose (bool, default = True) – Whether to print size of to be created feature matrix (if True) or not otherwise.

      • +
      • return_labels (bool, default = False) – Whether to return sample labels in addition to feature matrix.

      • +
      +
      +
      Returns:
      +

      feat_matrix – Feature values of samples.

      +
      +
      Return type:
      +

      array-like or sparse matrix, shape (n_samples, n_features)

      +
      +
      +
      + +
      +
      +static feat_names(features=None, df_cat=None, tmd_len=20, jmd_c_len=10, jmd_n_len=10, ext_len=0, start=1)[source]
      +

      Convert feature ids (PART-SPLIT-SCALE) into feature names (scale name [positions]).

      +
      +
      Parameters:
      +
        +
      • features (str, list of strings, pd.Series) – Ids of features for which feature names should be created.

      • +
      • df_cat (pandas.DataFrame, default = SequenceFeature.load_categories) – DataFrame with default categories for physicochemical amino acid scales

      • +
      • tmd_len (int, >0) – Length of TMD.

      • +
      • jmd_n_len (int, >0) – Length of JMD-N.

      • +
      • jmd_c_len (int, >0) – Length of JMD-C.

      • +
      • ext_len (int, >0) – Length of TMD-extending part (starting from C and N terminal part of TMD). +Conditions: ext_len<jmd_m_len and ext_len<jmd_c_len

      • +
      • start (int, >=0) – Position label of first amino acid position (starting at N-terminus).

      • +
      +
      +
      Returns:
      +

      feat_names – Names of features.

      +
      +
      Return type:
      +

      list of strings

      +
      +
      +
      +

      Notes

      +
      +
      Positions are given depending on the three split types:
        +
      • Segment: [first…last]

      • +
      • Pattern: [all positions]

      • +
      • PeriodicPattern: [first..step1/step2..last]

      • +
      +
      +
      +
      +
      + +
      +
      +static add_feat_value(df_parts=None, split=None, dict_scale=None, accept_gaps=False)[source]
      +

      Create feature values for all sequence parts by combining Part, Split, and Scale.

      +
      +
      Parameters:
      +
        +
      • df_parts (pandas.DataFrame) – DataFrame with sequence parts.

      • +
      • split (str) – Name of Split following given convention.

      • +
      • dict_scale (dict) – Dictionary mapping a numerical value to each letter of given sequences

      • +
      • accept_gaps (bool, default = False) – Whether to accept missing values by enabling omitting for computations (if True).

      • +
      +
      +
      Returns:
      +

      feature_value – Average scale values over sequence parts.

      +
      +
      Return type:
      +

      array-like, shape (n_samples, n_parts)

      +
      +
      +
      +

      Notes

      +

      A split name should has the form of PART-SPLIT-SCALE, where following structures +are given for the three split types:

      +
        +
      • +
        Segment(i-th,n_split)

        with i-th<=n_split and +where ‘i-th’ and ‘n_split’ indicate the i-th Segment resp. the number of Segments.

        +
        +
        +
      • +
      • +
        Pattern(N/C,p1,p2,…,pn)

        with p1<p2<…<pn indicating amino acid positions and +‘N/C’ whether the splits starts from the N resp. C-terminal sequence end.

        +
        +
        +
      • +
      • +
        PeriodicPattern(N/C,i+step1/step2,start)

        where ‘step1/step2’ indicates the step size of each odd resp. even step and +‘start’ gives the first position starting from the N- or C-terminal sequence end.

        +
        +
        +
      • +
      +

      All numbers should be non-negative integers. Examples for each split type +are as follows: ‘Segment(5,7)’, ‘Pattern(C,1,2)’, ‘PeriodicPattern(N,i+2/3,1)’.

      +
      +
      + +
      +
      +static add_dif(df_feat=None, df_seq=None, labels=None, sample_name=<class 'str'>, ref_group=0, accept_gaps=False, jmd_n_len=10, jmd_c_len=10, df_parts=None, df_scales=None)[source]
      +

      Add feature value difference between sample and reference group to DataFrame.

      +
      +
      Parameters:
      +
        +
      • df_feat (pandas.DataFrame) – Feature DataFrame (CPP output) to add sample difference.

      • +
      • df_seq (pandas.DataFrame) – DataFrame with sequences and sample names, in which the given sample name is included.

      • +
      • labels (array-like, shape (n_samples)) – Class labels for samples in sequence DataFrame.

      • +
      • sample_name (str) – Name of sample for which the feature value difference to a given reference group should be computed.

      • +
      • ref_group (int, default = 0) – Class label of reference group.

      • +
      • accept_gaps (bool, default = False) – Whether to accept missing values by enabling omitting for computations (if True).

      • +
      +
      +
      Returns:
      +

      df_feat – Feature DataFrame including feature value difference.

      +
      +
      Return type:
      +

      pandas.DataFrame

      +
      +
      +
      + +
      +
      +static add_position(df_feat=None, features=None, start=1, tmd_len=20, jmd_n_len=10, jmd_c_len=10, ext_len=4, part_split=False)[source]
      +

      Create list with positions for given feature names

      +
      +
      Parameters:
      +
        +
      • df_feat (pandas.DataFrame) – Feature DataFrame (CPP output) to add sample difference.

      • +
      • features (str, list of strings, pd.Series) – Ids of features for which feature names should be created.

      • +
      • start (int, >=0, default = 1) – Position label of first amino acid position (starting at N-terminus).

      • +
      • tmd_len (int, >0, default = 20) – Length of TMD.

      • +
      • jmd_n_len (int, >=0, default = 10) – Length of JMD-N.

      • +
      • jmd_c_len (int, >=0, default = 10) – Length of JMD-C.

      • +
      • ext_len (int, >=0, default = 4) – Length of TMD-extending part (starting from C and N terminal part of TMD). +Conditions: ext_len < jmd_m_len and ext_len < jmd_c_len.

      • +
      +
      +
      Returns:
      +

      feat_positions – list with positions for each feature in feat_names

      +
      +
      Return type:
      +

      list

      +
      +
      +
      +

      Notes

      +

      The length parameters define the total number of positions (jmd_n_len + tmd_len + jmd_c_len).

      +
      +
      + +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.dPULearn.html b/docs/build/html/generated/aaanalysis.dPULearn.html new file mode 100644 index 00000000..fff95248 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.dPULearn.html @@ -0,0 +1,281 @@ + + + + + + + + + + + + + + + + aaanalysis.dPULearn — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.dPULearn

      +
      +
      +class aaanalysis.dPULearn(verbose=False, n_components=0.8, pca_kwargs=None, metric=None)[source]
      +

      Bases: object

      +

      Deterministic Positive-Unlabeled (dPULearn) model.

      +

      dPULearn offers a deterministic approach for Positive-Unlabeled (PU) learning. The model primarily employs +Principal Component Analysis (PCA) to reduce the dimensionality of the feature space. Based on the most +informative principal components (PCs), it then iteratively identifies reliable negatives from the set of +unlabeled samples. These reliable negatives are those that are most distant from the positive samples in +the feature space. Alternatively, reliable negatives can also be identified using distance metrics like +Euclidean, Manhattan, or Cosine distance if specified.

      +
      +
      Parameters:
      +
        +
      • verbose (bool, default=False) – Enable verbose output.

      • +
      • n_components (float or int, default=0.80) – Number of components to cover a maximum percentage of total variance when PCA is applied.

      • +
      • pca_kwargs (dict, default=None) – Additional keyword arguments to pass to PCA.

      • +
      • metric ({'euclidean', 'manhattan', 'cosine'} or None, default=None) – The distance metric to use. If None, PCA-based identification is used. +If a metric is specified, distance-based identification is performed.

      • +
      +
      +
      +
      +
      +labels_
      +

      Labels of each datapoint.

      +
      +
      Type:
      +

      array-like, shape (n_samples,)

      +
      +
      +
      + +
      +

      Notes

      +
        +
      • +
        The method is inspired by deterministic PU learning techniques and follows

        an information-theoretic PU learning approach.

        +
        +
        +
      • +
      • +
        If metric is specified, distance-based identification of reliable negatives is performed.

        Otherwise, PCA-based identification is used.

        +
        +
        +
      • +
      • Cosine metric is recommended in high-dimensional spaces.

      • +
      +
      +
      +
      +__init__(verbose=False, n_components=0.8, pca_kwargs=None, metric=None)[source]
      +
      + +

      Methods

      + + + + + + + + + + + + +

      __init__([verbose, n_components, ...])

      eval()

      fit(X[, labels, n_neg, label_pos, name_neg, ...])

      Fit the dPULearn model to identify reliable negative samples from the provided feature matrix and labels.

      +
      +
      +fit(X, labels=None, n_neg=0, label_pos=1, name_neg='REL_NEG', df_seq=None, col_class='class')[source]
      +

      Fit the dPULearn model to identify reliable negative samples +from the provided feature matrix and labels.

      +
      +
      Parameters:
      +
        +
      • X (array-like, shape (n_samples, n_features)) – Feature matrix where n_samples is the number of samples and n_features is the number of features.

      • +
      • labels (array-like, shape (n_samples,), default=None) – Array of labels; positive samples should be indicated by label_pos.

      • +
      • n_neg (int, default=0) – Number of negative samples to identify.

      • +
      • label_pos (int or str, default=1) – Label indicating positive samples in the labels array.

      • +
      • name_neg (str, default="REL_NEG") – Name to assign to the newly identified negative samples.

      • +
      • df_seq (DataFrame, default=None, optional) – DataFrame containing sequences; will be updated with new negative samples.

      • +
      • col_class (str, default="class") – Column name in df_seq where the class labels are stored.

      • +
      +
      +
      Returns:
      +

      df_seq – DataFrame with the newly identified reliable negatives. Will be None if not provided.

      +
      +
      Return type:
      +

      DataFrame

      +
      +
      +
      +

      Notes

      +

      Distance-based identification is used if metric is specified during class initialization.

      +
      +
      +

      Examples

      +

      Create small example datafor dPUlearn containg positive (‘pos’, 1) and unlabeled (‘unl’, 2) data

      +
      >>> import aaanalysis as aa
      +>>> import pandas as pd
      +>>> import numpy as np
      +>>> X = np.array([[0.2, 0.1], [0.3, 0.2], [0.2, 0.3], [0.5, 0.7]])
      +>>> labels = np.array([1, 2, 2, 2])
      +>>> df_seq = pd.DataFrame({
      +...     'sequence': ['ATGC', 'GCTA', 'ACTG', 'TACG'],
      +...     'class': ['pos', 'unl', 'unl', 'unl']})
      +
      +
      +

      Use dPULearn in default mode (PC-based identification) and modify df_seq automatically

      +
      >>> dpul = aa.dPULearn()
      +>>> n_neg = 2
      +>>> df_seq = dpul.fit(X=X, df_seq=df_seq, labels=labels, n_neg=n_neg)
      +>>> labels = dpul.labels_   # Updated labels
      +
      +
      +
      +
      + +
      +
      +eval()[source]
      +
      + +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.load_dataset.html b/docs/build/html/generated/aaanalysis.load_dataset.html new file mode 100644 index 00000000..3a7d55fe --- /dev/null +++ b/docs/build/html/generated/aaanalysis.load_dataset.html @@ -0,0 +1,201 @@ + + + + + + + + + + + + + + + + aaanalysis.load_dataset — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.load_dataset

      +
      +
      +aaanalysis.load_dataset(name='INFO', n=None, random=False, non_canonical_aa='remove', min_len=None, max_len=None, aa_window_size=9)[source]
      +

      Load protein benchmarking datasets.

      +

      The benchmarks are categorized into amino acid (‘AA’), domain (‘DOM’), and sequence (‘SEQ’) level +datasets. Use default settings (name='INFO') for an overview table. Detailed analysis is in [Breimann23a].

      +
      +
      Parameters:
      +
        +
      • name (str) – Name of the dataset. See ‘Dataset’ column in overview table.

      • +
      • n (Optional[int]) – Number of proteins per class, selected by index. If None, the whole dataset will be returned.

      • +
      • random (bool) – If True, n random selected proteins per class will be chosen.

      • +
      • non_canonical_aa (Literal[‘remove’, ‘keep’, ‘gap’]) –

        Options for modifying non-canonical amino acids:

        +
          +
        • ’remove’: Remove sequences containing non-canonical amino acids.

        • +
        • ’keep’: Dont remove sequences containing non-canonical amino acids.

        • +
        • ’gap’: Non-canonical amino acids are replaced by gap symbol (‘X’).

        • +
        +

      • +
      • min_len (Optional[int]) – Minimum length of sequences for filtering, disabled by default.

      • +
      • max_len (Optional[int]) – Maximum length of sequences for filtering, disabled by default.

      • +
      • aa_window_size (Optional[int]) – Length of amino acid window, only used for amino acid dataset level (name='AA_') and if n given.

      • +
      +
      +
      Returns:
      +

      Dataframe (df_seq) containing the selected sequence dataset.

      +
      +
      Return type:
      +

      DataFrame

      +
      +
      +
      +

      See also

      +

      See an overview of all benchmarks in Amino acid scale datasets and a detailed usage tutorial in the +data loader tutorial.

      +
      +
      +

      Examples

      +
      >>> import aaanalysis as aa
      +>>> df_seq = aa.load_dataset(name="SEQ_AMYLO", n=100)
      +
      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.load_scales.html b/docs/build/html/generated/aaanalysis.load_scales.html new file mode 100644 index 00000000..d1e129e0 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.load_scales.html @@ -0,0 +1,181 @@ + + + + + + + + + + + + + + + + aaanalysis.load_scales — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.load_scales

      +
      +
      +aaanalysis.load_scales(name='scales', just_aaindex=False, unclassified_in=True)[source]
      +

      Load amino acid scales, scale classification (AAontology), or scale evaluation.

      +

      A thorough analysis of the residue and sequence datasets can be found in [Breimann23a].

      +
      +
      Parameters:
      +
        +
      • name (str, default = 'scales') – Name of the dataset to load. Options are ‘scales’, ‘scales_raw’, ‘scales_cat’, +‘scales_pc’, ‘top60’, and ‘top60_eval’.

      • +
      • unclassified_in (bool, optional) – Whether unclassified scales should be included. The ‘Others’ category counts as unclassified. +Only relevant if name is ‘scales’, ‘scales_raw’, or ‘scales_cat’.

      • +
      • just_aaindex (bool, optional) – Whether only scales provided from AAindex should be given. +Only relevant if name is ‘scales’, ‘scales_raw’, or ‘scales_cat’.

      • +
      +
      +
      Returns:
      +

      Dataframe for the selected scale dataset.

      +
      +
      Return type:
      +

      DataFrame

      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.plot_gcfs.html b/docs/build/html/generated/aaanalysis.plot_gcfs.html new file mode 100644 index 00000000..db0dae3d --- /dev/null +++ b/docs/build/html/generated/aaanalysis.plot_gcfs.html @@ -0,0 +1,165 @@ + + + + + + + + + + + + + + + + aaanalysis.plot_gcfs — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.plot_gcfs

      +
      +
      +aaanalysis.plot_gcfs()[source]
      +

      Get current font size, which is set by ut.plot_settings function

      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.plot_get_cdict.html b/docs/build/html/generated/aaanalysis.plot_get_cdict.html new file mode 100644 index 00000000..cc0594e7 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.plot_get_cdict.html @@ -0,0 +1,177 @@ + + + + + + + + + + + + + + + + aaanalysis.plot_get_cdict — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.plot_get_cdict

      +
      +
      +aaanalysis.plot_get_cdict(name=None)[source]
      +

      Retrieve color dictionaries specified for AAanalysis.

      +
      +
      Parameters:
      +

      name (str, {'DICT_COLOR', 'DICT_SCALE_CAT'}) – The name of default color dictionaries for plots (e.g., bars in CPPPlot.profile) +and scale categories (e.g., CPPPlot.heatmap), respectively.

      +
      +
      Returns:
      +

      cmap – Specific AAanalysis color dictionary.

      +
      +
      Return type:
      +

      dict

      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.plot_get_cmap.html b/docs/build/html/generated/aaanalysis.plot_get_cmap.html new file mode 100644 index 00000000..835447b5 --- /dev/null +++ b/docs/build/html/generated/aaanalysis.plot_get_cmap.html @@ -0,0 +1,212 @@ + + + + + + + + + + + + + + + + aaanalysis.plot_get_cmap — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.plot_get_cmap

      +
      +
      +aaanalysis.plot_get_cmap(name=None, n_colors=100, facecolor_dark=False, color_pos=None, color_neg=None, color_center=None, input='hex', pct_gap=10, pct_center=None)[source]
      +

      Retrieve color maps or color dictionaries specified for AAanalysis.

      +
      +
      Parameters:
      +
        +
      • name (str, optional) –

        +
        The name of the color palette to use in AAanalysis. Options include:
          +
        • +
          ’SHAP’, ‘FEAT’, ‘GGPLOT’: Return color maps for SHAP plots, CPP feature maps/heatmaps,

          and datagrouping as in GGplot, respectively.

          +
          +
          +
        • +
        • +
          ’DICT_COLOR’, ‘DICT_SCALE_CAT’: Return default color dictionaries for plots (e.g., bars in CPPPlot.profile)

          and scale categories (e.g., CPPPlot.heatmap), respectively.

          +
          +
          +
        • +
        +
        +
        +

      • +
      • n_colors (int, default=100) – Number of colors in the color map.

      • +
      • facecolor_dark (bool, default=False) – Whether to use a dark face color for ‘SHAP’ and ‘FEAT’.

      • +
      • color_pos (str, optional) – Hex code for the positive color.

      • +
      • color_neg (str, optional) – Hex code for the negative color.

      • +
      • color_center (str or list, optional) – Hex code or list for the center color.

      • +
      • input (str, {'rgb', 'hls', 'husl', 'xkcd'}) – Color space to interpret the input color. The first three options +apply to tuple inputs and the latter applies to string inputs.

      • +
      • pct_gap (int, default=10) – Percentage size of the gap between color ranges.

      • +
      • pct_center (float, optional) – Percentage size of the center color in the map.

      • +
      +
      +
      Returns:
      +

      cmap – If ‘name’ parameter is ‘SHAP’, ‘FEAT’, or ‘GGPLOT’, a list of colors specified for AAanalysis will be returned. +If ‘name’ parameter is None, a list of colors based on provided colors

      +
      +
      Return type:
      +

      list or dict

      +
      +
      +
      +

      See also

      +
      +
      sns.color_palette

      Function to generate a color palette in seaborn.

      +
      +
      sns.light_palette

      Function to generate a lighter color palette in seaborn.

      +
      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.plot_set_legend.html b/docs/build/html/generated/aaanalysis.plot_set_legend.html new file mode 100644 index 00000000..31f8e61a --- /dev/null +++ b/docs/build/html/generated/aaanalysis.plot_set_legend.html @@ -0,0 +1,215 @@ + + + + + + + + + + + + + + + + aaanalysis.plot_set_legend — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.plot_set_legend

      +
      +
      +aaanalysis.plot_set_legend(ax=None, handles=None, dict_color=None, list_cat=None, labels=None, y=-0.2, x=0.5, ncol=3, fontsize=11, weight='normal', lw=0, edgecolor=None, return_handles=False, loc='upper left', labelspacing=0.2, columnspacing=1, title=None, fontsize_legend=None, title_align_left=True, fontsize_weight='normal', shape=None, **kwargs)[source]
      +

      Set a customizable legend for a plot.

      +
      +
      Parameters:
      +
        +
      • ax (matplotlib.axes.Axes, default=None) – The axes to attach the legend to.

      • +
      • handles (list, default=None) – Handles for legend items.

      • +
      • dict_color (dict, default=None) – A dictionary mapping categories to colors.

      • +
      • list_cat (list, default=None) – List of categories to include in the legend.

      • +
      • labels (list, default=None) – Labels for legend items.

      • +
      • y (float, default=-0.2) – The y-coordinate for the legend’s anchor point.

      • +
      • x (float, default=0.5) – The x-coordinate for the legend’s anchor point.

      • +
      • ncol (int, default=3) – Number of columns in the legend.

      • +
      • fontsize (int, default=11) – Font size for the legend text.

      • +
      • weight (str, default='normal') – Weight of the font.

      • +
      • lw (float, default=0) – Line width for legend items.

      • +
      • edgecolor (color, default=None) – Edge color for legend items.

      • +
      • return_handles (bool, default=False) – Whether to return handles and labels.

      • +
      • loc (str, default='upper left') – Location for the legend.

      • +
      • labelspacing (float, default=0.2) – Vertical spacing between legend items.

      • +
      • columnspacing (int, default=1) – Horizontal spacing between legend columns.

      • +
      • title (str, default=None) – Title for the legend.

      • +
      • fontsize_legend (int, default=None) – Font size for the legend title.

      • +
      • title_align_left (bool, default=True) – Whether to align the title to the left.

      • +
      • fontsize_weight (str, default='normal') – Font weight for the legend title.

      • +
      • shape (str, default=None) – Marker shape for legend items.

      • +
      • **kwargs (dict) – Additional arguments passed directly to ax.legend() for finer control.

      • +
      +
      +
      Returns:
      +

      ax – The axes with the legend applied.

      +
      +
      Return type:
      +

      matplotlib.axes.Axes

      +
      +
      +
      +

      See also

      +
      +
      matplotlib.pyplot.legend

      For additional details on how the ‘loc’ parameter can be customized.

      +
      +
      matplotlib.lines.Line2D

      For additional details on the different types of marker shapes (‘shape’ parameter).

      +
      +
      +
      +
      +

      Examples

      +
      >>> import aaanalysis as aa
      +>>> aa.plot_set_legend(ax=ax, dict_color={'Cat1': 'red', 'Cat2': 'blue'}, shape='o')
      +
      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/generated/aaanalysis.plot_settings.html b/docs/build/html/generated/aaanalysis.plot_settings.html new file mode 100644 index 00000000..18e9494d --- /dev/null +++ b/docs/build/html/generated/aaanalysis.plot_settings.html @@ -0,0 +1,197 @@ + + + + + + + + + + + + + + + + aaanalysis.plot_settings — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + +
      + +
      +
      +
      + +
      +
      +
      +
      + +
      +

      aaanalysis.plot_settings

      +
      +
      +aaanalysis.plot_settings(fig_format='pdf', verbose=False, grid=False, grid_axis='y', font_scale=0.7, font='Arial', change_size=True, weight_bold=True, adjust_elements=True, short_ticks=False, no_ticks=False, no_ticks_y=False, short_ticks_y=False, no_ticks_x=False, short_ticks_x=False)[source]
      +

      Configure general settings for plot visualization with various customization options.

      +
      +
      Parameters:
      +
        +
      • fig_format (str, default='pdf') – Specifies the file format for saving the plot.

      • +
      • verbose (bool, default=False) – If True, enables verbose output.

      • +
      • grid (bool, default=False) – If True, makes the grid visible.

      • +
      • grid_axis (str, default='y') – Choose the axis (‘y’, ‘x’, ‘both’) to apply the grid to.

      • +
      • font_scale (float, default=0.7) – Sets the scale for font sizes in the plot.

      • +
      • font (str, default='Arial') – Name of sans-serif font (e.g., ‘Arial’, ‘Verdana’, ‘Helvetica’, ‘DejaVu Sans’)

      • +
      • change_size (bool, default=True) – If True, adjusts the size of plot elements.

      • +
      • weight_bold (bool, default=True) – If True, text elements appear in bold.

      • +
      • adjust_elements (bool, default=True) – If True, makes additional visual and layout adjustments to the plot.

      • +
      • short_ticks (bool, default=False) – If True, uses short tick marks.

      • +
      • no_ticks (bool, default=False) – If True, removes all tick marks.

      • +
      • no_ticks_y (bool, default=False) – If True, removes tick marks on the y-axis.

      • +
      • short_ticks_y (bool, default=False) – If True, uses short tick marks on the y-axis.

      • +
      • no_ticks_x (bool, default=False) – If True, removes tick marks on the x-axis.

      • +
      • short_ticks_x (bool, default=False) – If True, uses short tick marks on the x-axis.

      • +
      +
      +
      +
      +

      Notes

      +

      This function modifies the global settings of Matplotlib and Seaborn libraries.

      +
      +
      +

      Examples

      +
      >>> import aaanalysis as aa
      +>>> aa.plot_settings(fig_format="pdf", font_scale=1.0, weight_bold=False)
      +
      +
      +
      +
      + +
      + + +
      +
      +
      + +
      + +
      +

      © Copyright 2023, Stephan Breimann.

      +
      + + + +
      +
      +
      +
      +
      + + + + \ No newline at end of file diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index f122c905..c44ef2f0 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -56,13 +56,19 @@

      OVERVIEW

      +

      EXAMPLES

      +

      REFERENCES

      @@ -94,8 +100,227 @@

      Index

      + _ + | A + | C + | D + | E + | F + | G + | H + | L + | M + | N + | P + | R + | S + | U
      +

      _

      + + +
      + +

      A

      + + + +
      + +

      C

      + + + +
      + +

      D

      + + +
      + +

      E

      + + +
      + +

      F

      + + + +
      + +

      G

      + + + +
      + +

      H

      + + +
      + +

      L

      + + + +
      + +

      M

      + + + +
      + +

      N

      + + +
      + +

      P

      + + + +
      + +

      R

      + + +
      + +

      S

      + + +
      + +

      U

      + + +
      + diff --git a/docs/build/html/index.html b/docs/build/html/index.html index ecb0939f..c4eddf9b 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -6,12 +6,12 @@ - + - - - + + + @@ -69,13 +69,19 @@

      OVERVIEW

      +

      EXAMPLES

      +

      REFERENCES

      @@ -105,6 +111,15 @@

      Welcome to the AAanalysis documentation

      +Build Status +Python-check +PyPI - Status +Supported Python Versions +PyPI - Package Version +Conda - Package Version +Documentation Status +License +Downloads

      AAanalysis (Amino Acid analysis) is a Python framework for interpretable sequence-based protein prediction, providing the following algorithms:

      +

      EXAMPLES

      +

      REFERENCES

      @@ -128,7 +134,7 @@

      Workflow - +
      diff --git a/docs/build/html/index/overview.html b/docs/build/html/index/overview.html index bbcc3f02..d8340694 100644 --- a/docs/build/html/index/overview.html +++ b/docs/build/html/index/overview.html @@ -68,13 +68,19 @@

      OVERVIEW

      +

      EXAMPLES

      +

      REFERENCES

      diff --git a/docs/build/html/index/references.html b/docs/build/html/index/references.html index aa638409..524dd775 100644 --- a/docs/build/html/index/references.html +++ b/docs/build/html/index/references.html @@ -40,7 +40,7 @@ - + @@ -69,13 +69,19 @@

      OVERVIEW

      +

      EXAMPLES

      +

      REFERENCES

        +
      • Tables
      • References
        • Algorithms
        • Datasets and Benchmarks
        • @@ -205,7 +211,7 @@

          Further Information - +
          diff --git a/docs/build/html/index/tables_template.html b/docs/build/html/index/tables_template.html new file mode 100644 index 00000000..5debfc46 --- /dev/null +++ b/docs/build/html/index/tables_template.html @@ -0,0 +1,168 @@ + + + + + + + + + + + + + + + + + + Tables — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + +
          + + +
          + +
          +
          +
          + +
          +
          +
          +
          + +
          +

          Tables

          + +
          +

          Overview Table

          +

          All tables from the AAanalysis documentation are given here in chronological order of the project history.

          +
          +
          +

          Protein benchmark datasets

          +

          Three types of benchmark datasets are provided:

          +
            +
          • Residue prediction (AA): Datasets used to predict residue (amino acid) specific properties.

          • +
          • Domain prediction (DOM): Dataset used to predict domain specific properties.

          • +
          • Sequence prediction (SEQ): Datasets used to predict sequence specific properties.

          • +
          +

          The classification of each dataset is indicated as first part of their name followed by an abbreviation for the +specific dataset (e.g., ‘AA_LDR’, ‘DOM_GSEC’, ‘SEQ_AMYLO’). For some datasets, an additional version of it is provided +for positive-unlabeled (PU) learning containing only positive (1) and unlabeled (2) data samples, as indicated by +dataset_name_PU (e.g., ‘DOM_GSEC_PU’).

          +
          +
          +

          Amino acid scale datasets

          +

          Different amino acid scale datasets are provided

          +
          +
          + + +
          +
          +
          + +
          + +
          +

          © Copyright 2023, Stephan Breimann.

          +
          + + + +
          +
          +
          +
          +
          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles.html b/docs/build/html/index/usage_principles.html new file mode 100644 index 00000000..712dd6e5 --- /dev/null +++ b/docs/build/html/index/usage_principles.html @@ -0,0 +1,165 @@ + + + + + + + + + + + + + + + + + + Usage Principles — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
          + + +
          + +
          +
          +
          + +
          +
          + +
          + +
          + +
          +

          © Copyright 2023, Stephan Breimann.

          +
          + + + +
          +
          +
          +
          +
          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles/aaontology.html b/docs/build/html/index/usage_principles/aaontology.html new file mode 100644 index 00000000..b5a32ba8 --- /dev/null +++ b/docs/build/html/index/usage_principles/aaontology.html @@ -0,0 +1,154 @@ + + + + + + + + + + + + + + + + + + AAontology: Classification of amino acid scales — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
          + + +
          + +
          +
          +
          + +
          +
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          AAontology: Classification of amino acid scales

          +

          AAontology is a two-level classification of amino acid scale, introduced in.

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          © Copyright 2023, Stephan Breimann.

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          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles/data_flow_entry_points.html b/docs/build/html/index/usage_principles/data_flow_entry_points.html new file mode 100644 index 00000000..1a93b287 --- /dev/null +++ b/docs/build/html/index/usage_principles/data_flow_entry_points.html @@ -0,0 +1,157 @@ + + + + + + + + + + + + + + + + + + Data Flow and Enry Points — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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          Data Flow and Enry Points

          +

          The AAanalysis toolkit uses different DataFrames starting from DataFrames containing amino acid scales information +(df_scales, df_cat) or sequence information (df_seq), which can be modified to obtain specific sequence parts (df_parts). +Amino acid scales and sequence parts together with split settings are the input for the CPP algorithm, creating +various physicochemical features (df_feat) by comparing two sets of protein sequences.

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          © Copyright 2023, Stephan Breimann.

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          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles/feature_identification.html b/docs/build/html/index/usage_principles/feature_identification.html new file mode 100644 index 00000000..31ece1c9 --- /dev/null +++ b/docs/build/html/index/usage_principles/feature_identification.html @@ -0,0 +1,155 @@ + + + + + + + + + + + + + + + + + + Identifying Physicochemical Signatures using CPP — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
          + + +
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          Identifying Physicochemical Signatures using CPP

          +

          The central algorithm of the AAanalysis platform is Comparative Physicochemical Profiling (CPP), a novel sequence-based +feature engineering algorithm, designed to enable interpretable protein prediction.

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          © Copyright 2023, Stephan Breimann.

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          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles/pu_learning.html b/docs/build/html/index/usage_principles/pu_learning.html new file mode 100644 index 00000000..3efe3a06 --- /dev/null +++ b/docs/build/html/index/usage_principles/pu_learning.html @@ -0,0 +1,169 @@ + + + + + + + + + + + + + + + + + + Learning from unbalanced and small data — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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          Learning from unbalanced and small data

          +

          Unbalanced and small datasets are everywhere in life science ….

          +

          In a standard binary classification setup, data with positive (1) and negative (0) labels are provided, which can be +used for training by machine learning models. If only a view samples of the negative class exist, data augmentation +techniques (e.g., SMOTE) can be used to extend the negative dataset by artificially generated sequences. Such approaches +are very popular for deep learning-based image recognition, but not feasible for protein sequence prediction tasks +because slight amino acid mutations (sequence alterations or perturbations) can already have dramatic biological effects. +Alternatively, negatives samples can be identified from unlabeled samples (2), which often exist in great quantities. +These unlabeled samples should be biologically as similar as possible to the positive class, beside not containing +the features distinguishing the positive from the negative class. For example, .

          +
          +

          What is PU learning?

          +

          Positive Unlabeled (PU) learning is a subfield of machine learning …

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          + +
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          © Copyright 2023, Stephan Breimann.

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          + + + + \ No newline at end of file diff --git a/docs/build/html/index/usage_principles/xai.html b/docs/build/html/index/usage_principles/xai.html new file mode 100644 index 00000000..43aaa8b4 --- /dev/null +++ b/docs/build/html/index/usage_principles/xai.html @@ -0,0 +1,160 @@ + + + + + + + + + + + + + + + + + + Explainable AI at Sequence Level — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + + + +
          + + +
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          + +
          +

          Explainable AI at Sequence Level

          +

          Unbalanced and small datasets are everywhere in life science …

          +
          +

          What is explainable AI?

          +
          +
          + + +
          +
          +
          + +
          + +
          +

          © Copyright 2023, Stephan Breimann.

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          + + + + \ No newline at end of file diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 8308615571be1b69175d6419188346513a76c387..5f44df3b2cee99d6e7f897e700261f4f9029eb44 100644 GIT binary patch delta 1394 zcmV-&1&#XP1m6pget)%D+iv4F5Pj!Y5ZJzYb16`?i=u#=W{UvZU1O7==#xN8luU#o zwIY?o`}G}?q9s{3dLut1kvV71j5ySskwXX(gzpto@7L>?S4!hz{~_i?DSU-9DriYG zO)f|e_kO2y{_ZEdLIbuaVuI-?GGsZ`JDI$NEv7=#GU8-Qxqq^pMC+1mD^1@{yCk9c z4T%bpGm$NH@3Jf@Cz?sI3?Y3cd>N*c>1BvAsw2O5by1cl&S@f9vJmmoN6{q*4^6T- z=Xp|HkWly%sW(&JY&Ns)3ov88C$z08mar#milMB;n+P^s>X3~vxG9d)He{A9>lCn0|!Ytx7suh$DOm=Lv=R5q6R!QNq&m zk*9DS!DCX<^TjBnVqvoRBN6N!lp*UBtg}A_>DFuX;hF>pD{w}uFn`j6&xOf zrqORw7 zk6WFx_Y+OC6FJ#Dexjrlv#rzv2TOY}hBwbJzA(WL;|dz8!>CS~{H7U;id$oI(JRO* zhHrynt8ey6M#LO(`{H0&k(~>~c~*;rbXUbQQ^GT0S%S5*@56nBV)Z zTE6R?35iL}5^N7w6YIK(RiDz60q(cv zoiVSB`7gLYwID{25fhY_a5yJ?YxJsxwp3pAZku(K5zgrMtKi$5no8O=o}FHBe;tiY z4}Y`7et5y%H8eGjgpeM_7TjM)Arr`KkfDhMkK56t{LMz|=p-I6pjrK!jnNZJJYqz1 z+87q9rK5P*hNg9MHcVGx@sI@tNNHf4j`HGBGnyCL69Rf!#$#3#BEew+k0cw9+tIv4 z2Zw9~9gkT}INSNU&Cstp=R0$W8dx2>g@5>$@>u2+Yl-!a;`Q1`!+@=FDx4NLj*FWw zpC7*7{rUKC_xbPW=F8^49%~=s;@n8#=55o*9db*Otoz)w*PYw%Fe7NDIsF42iBsRN zt6P0n1{uhtauZe0yKVKG*R0eOe!%Fvd&@}dMxFYuN6Ll29zLVNS*Oo1Gz|H1#(yCy zMKT%{prSD=_)r$d2uJd0R$(yot0Q!9hM~Ta%WOZ-+yd8=)!KjlfN#vJ6H8g_{=Xwr zOpXS{(H!iMl5+V1lZP(%Q6ZtG0e3p$=yEd?4gLr=OrT}ligJ%P);yMlpMF>ZWX+At zXEE$pA4RveH~&N1?BMZ?B}{xdV1GEiXPKzKQL2;ym|0>ZMo~rfZPmc$Gisq-8lSvx zTFM+(VoNxIbDG$_Q90q^&KV}`}qPAd8Qmakr$$N zFe+dE8aff?1U5@t{uYdv$v~4W_A762ss00f0eXt130O5_ zWSb=0r?0^rmt;fk@XYtm(FRJ+v`IguGGTtepyY3JQwX++LN8Xx3m{s*gN6|hqo^rP zDL$SnyhDYQpl&2*ZB*CF!RGy?=AyQ!ucHb}&8OjpnR& z`?dR<{qXJ(Or>ydE8>KxIAtfaE$xr!Ce(LlXBUIr%Rq4a7c6cwLu#B;zO%ZuhV`d6 zVC~vR#BUUdERY$=Opa``0RnN*Tr8oZZPjldX#zfnU6%{mpgL)y}2dRv^#aJ45g6f#2cyI&7-(@ECq0a*yh=UUS(PB1%jf}FRZNmj%tELx9n$o z_C`&-&|P#exxEyVCbbQdOVERVIEW

          +

          EXAMPLES

          +

          REFERENCES

          diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 998f9fd7..10576203 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "index", "index/CONTRIBUTING_COPY", "index/badges", "index/citations", "index/introduction", "index/overview", "index/references", "tutorials"], "filenames": ["api.rst", "index.rst", "index/CONTRIBUTING_COPY.rst", "index/badges.rst", "index/citations.rst", "index/introduction.rst", "index/overview.rst", "index/references.rst", "tutorials.rst"], "titles": ["API", "Welcome to the AAanalysis documentation", "Contributing", "<no title>", "<no title>", "Introduction", "<no title>", "References", "Tutorials"], "terms": {"thi": [0, 2], "page": [0, 1], "contain": [0, 2], "refer": [0, 2], "public": [0, 1, 2, 4], "object": 0, "function": [0, 1, 6], "aaanalysi": [0, 2, 4, 5, 6], "For": [0, 2], "more": [0, 2], "exampl": [0, 2], "practic": 0, "usag": [0, 2], 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null]], "aaanalysis.CPP": [[3, "aaanalysis-cpp"]], "aaanalysis.CPPPlot": [[4, "aaanalysis-cppplot"]], "Examples": [[4, null], [5, null], [5, null], [6, null], [7, null], [12, null], [13, null]], "aaanalysis.SequenceFeature": [[5, "aaanalysis-sequencefeature"]], "aaanalysis.dPULearn": [[6, "aaanalysis-dpulearn"]], "aaanalysis.load_dataset": [[7, "aaanalysis-load-dataset"]], "aaanalysis.load_scales": [[8, "aaanalysis-load-scales"]], "aaanalysis.plot_gcfs": [[9, "aaanalysis-plot-gcfs"]], "aaanalysis.plot_get_cdict": [[10, "aaanalysis-plot-get-cdict"]], "aaanalysis.plot_get_cmap": [[11, "aaanalysis-plot-get-cmap"]], "aaanalysis.plot_set_legend": [[12, "aaanalysis-plot-set-legend"]], "aaanalysis.plot_settings": [[13, "aaanalysis-plot-settings"]], "Welcome to the AAanalysis documentation": [[14, "welcome-to-the-aaanalysis-documentation"]], "Install": [[14, "install"]], "OVERVIEW": [[14, null]], "EXAMPLES": [[14, null]], "REFERENCES": [[14, null]], "Indices and tables": [[14, "indices-and-tables"]], "Citation": [[14, "citation"]], "Contributing": [[15, "contributing"]], "Introduction": [[15, "introduction"], [18, "introduction"]], "Vision": [[15, "vision"]], "Objectives": [[15, "objectives"]], "Non-goals": [[15, "non-goals"]], "Principles": [[15, "principles"]], "Bug Reports": [[15, "bug-reports"]], "Installation": [[15, "installation"]], "Latest Version": [[15, "latest-version"]], "Local Development Environment": [[15, "local-development-environment"]], "Fork and Clone": [[15, "fork-and-clone"]], "Install Dependencies": [[15, "install-dependencies"]], "Run Unit Tests": [[15, "run-unit-tests"]], "Pull Requests": [[15, "pull-requests"]], "Preview Changes": [[15, "preview-changes"]], "Documentation": [[15, "documentation"]], "Naming Conventions": [[15, "naming-conventions"]], "Class Templates": [[15, "class-templates"]], "Function and Method Naming": [[15, "function-and-method-naming"]], "Code Philosophy": [[15, "code-philosophy"]], "Documentation Style": [[15, "documentation-style"]], "Documentation Layers": [[15, "documentation-layers"]], "Building the Docs": [[15, "building-the-docs"]], "Workflow": [[18, "workflow"]], "References": [[20, "references"]], "Algorithms": [[20, "algorithms"]], "Datasets and Benchmarks": [[20, "datasets-and-benchmarks"]], "Use Cases": [[20, "use-cases"]], "Further Information": [[20, "further-information"]], "Usage Principles": [[22, "usage-principles"]], "AAontology: Classification of amino acid scales": [[23, "aaontology-classification-of-amino-acid-scales"]], "Data Flow and Enry Points": [[24, "data-flow-and-enry-points"]], "Identifying Physicochemical Signatures using CPP": [[25, "identifying-physicochemical-signatures-using-cpp"]], "Learning from unbalanced and small data": [[26, "learning-from-unbalanced-and-small-data"]], "What is PU learning?": [[26, "what-is-pu-learning"]], "Explainable AI at Sequence Level": [[27, "explainable-ai-at-sequence-level"]], "What is explainable AI?": [[27, "what-is-explainable-ai"]], "Tutorials": [[28, "tutorials"]], "Quick start": [[28, "quick-start"]], "Data loading": [[28, "data-loading"]], "Redundancy-reduction": [[28, "redundancy-reduction"]], "Feature engineering": [[28, "feature-engineering"]], "Identification of negatives": [[28, "identification-of-negatives"]], "Protein prediction": [[28, "protein-prediction"]]}, "indexentries": {"aaclust (class in aaanalysis)": [[2, "aaanalysis.AAclust"]], "__init__() (aaanalysis.aaclust method)": [[2, "aaanalysis.AAclust.__init__"]], "center_labels_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.center_labels_"]], "centers_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.centers_"]], "cluster_naming() (aaanalysis.aaclust method)": [[2, "aaanalysis.AAclust.cluster_naming"]], "correlation() (aaanalysis.aaclust static method)": [[2, "aaanalysis.AAclust.correlation"]], "eval() (aaanalysis.aaclust method)": [[2, "aaanalysis.AAclust.eval"]], "fit() (aaanalysis.aaclust method)": [[2, "aaanalysis.AAclust.fit"]], "get_cluster_centers() (aaanalysis.aaclust static method)": [[2, "aaanalysis.AAclust.get_cluster_centers"]], "get_cluster_medoids() (aaanalysis.aaclust static method)": [[2, "aaanalysis.AAclust.get_cluster_medoids"]], "labels_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.labels_"]], "medoid_ind_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.medoid_ind_"]], "medoid_labels_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.medoid_labels_"]], "medoids_ (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.medoids_"]], "n_clusters (aaanalysis.aaclust attribute)": [[2, "aaanalysis.AAclust.n_clusters"]], "cpp (class in aaanalysis)": [[3, "aaanalysis.CPP"]], "__init__() (aaanalysis.cpp method)": [[3, "aaanalysis.CPP.__init__"]], "eval() (aaanalysis.cpp static method)": [[3, "aaanalysis.CPP.eval"]], "run() (aaanalysis.cpp method)": [[3, "aaanalysis.CPP.run"]], "cppplot (class in aaanalysis)": [[4, "aaanalysis.CPPPlot"]], 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[[5, "aaanalysis.SequenceFeature.feat_names"]], "get_df_parts() (aaanalysis.sequencefeature static method)": [[5, "aaanalysis.SequenceFeature.get_df_parts"]], "get_features() (aaanalysis.sequencefeature method)": [[5, "aaanalysis.SequenceFeature.get_features"]], "get_split_kws() (aaanalysis.sequencefeature static method)": [[5, "aaanalysis.SequenceFeature.get_split_kws"]], "__init__() (aaanalysis.dpulearn method)": [[6, "aaanalysis.dPULearn.__init__"]], "dpulearn (class in aaanalysis)": [[6, "aaanalysis.dPULearn"]], "eval() (aaanalysis.dpulearn method)": [[6, "aaanalysis.dPULearn.eval"]], "fit() (aaanalysis.dpulearn method)": [[6, "aaanalysis.dPULearn.fit"]], "labels_ (aaanalysis.dpulearn attribute)": [[6, "aaanalysis.dPULearn.labels_"]], "load_dataset() (in module aaanalysis)": [[7, "aaanalysis.load_dataset"]], "load_scales() (in module aaanalysis)": [[8, "aaanalysis.load_scales"]], "plot_gcfs() (in module aaanalysis)": [[9, "aaanalysis.plot_gcfs"]], "plot_get_cdict() (in module aaanalysis)": [[10, "aaanalysis.plot_get_cdict"]], "plot_get_cmap() (in module aaanalysis)": [[11, "aaanalysis.plot_get_cmap"]], "plot_set_legend() (in module aaanalysis)": [[12, "aaanalysis.plot_set_legend"]], "plot_settings() (in module aaanalysis)": [[13, "aaanalysis.plot_settings"]]}}) \ No newline at end of file diff --git a/docs/build/html/tutorials.html b/docs/build/html/tutorials.html index 2a344d28..fc327427 100644 --- a/docs/build/html/tutorials.html +++ b/docs/build/html/tutorials.html @@ -39,7 +39,9 @@ - + + + @@ -68,13 +70,27 @@

          OVERVIEW

          +

          EXAMPLES

          +

          REFERENCES

          @@ -132,7 +148,10 @@

          Protein prediction + + +
          diff --git a/docs/build/plot_directive/generated/aaanalysis-CPPPlot-1.py b/docs/build/plot_directive/generated/aaanalysis-CPPPlot-1.py new file mode 100644 index 00000000..b2eede39 --- /dev/null +++ b/docs/build/plot_directive/generated/aaanalysis-CPPPlot-1.py @@ -0,0 +1,6 @@ +import matplotlib.pyplot as plt +import aaanalysis as aa +sf = aa.SequenceFeature() +df_seq = aa.load_dataset(name='SEQ_DISULFIDE', min_len=100) +labels = list(df_seq["label"]) +df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10) diff --git a/docs/source/__pycache__/create_tables_doc.cpython-39.pyc b/docs/source/__pycache__/create_tables_doc.cpython-39.pyc index ef5a8454d7ee4e2062d7c7644790c94484f4bebc..fef4433993f773f302ea1d91a38c96650a66db02 100644 GIT binary patch delta 84 zcmewsR}7_*-R4{ zihiWZH#0CYq%a0EXew@&Va(-`y2TM6pPQJO7ay<59O73b1T;&N+0V~UQ*rW4-ltq3 b>Ab|;)cE+xb$rT<29p=@>9cU?a4`Y^$lN0K diff --git a/docs/source/api.rst b/docs/source/api.rst index 7365fec0..07ed079c 100755 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -21,6 +21,8 @@ Data .. autosummary:: :toctree: generated/ + aaanalysis.load_dataset + aaanalysis.load_scales .. _feature_engineering_api: @@ -29,6 +31,10 @@ Feature Engineering .. autosummary:: :toctree: generated/ + aaanalysis.AAclust + aaanalysis.SequenceFeature + aaanalysis.CPP + aaanalysis.CPPPlot .. _pu_learning_api: @@ -37,6 +43,7 @@ PU Learning .. autosummary:: :toctree: generated/ + aaanalysis.dPULearn .. _plot_api: @@ -54,3 +61,9 @@ Plot Utilities .. autosummary:: :toctree: generated/ + aaanalysis.plot_settings + aaanalysis.plot_set_legend + aaanalysis.plot_gcfs + aaanalysis.plot_get_cmap + aaanalysis.plot_get_cdict + diff --git a/docs/source/generated/aaanalysis.AAclust.rst b/docs/source/generated/aaanalysis.AAclust.rst new file mode 100644 index 00000000..f6e6666c --- /dev/null +++ b/docs/source/generated/aaanalysis.AAclust.rst @@ -0,0 +1,28 @@ +aaanalysis.AAclust +================== + +.. currentmodule:: aaanalysis + +.. autoclass:: AAclust + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~AAclust.__init__ + ~AAclust.cluster_naming + ~AAclust.correlation + ~AAclust.eval + ~AAclust.fit + ~AAclust.get_cluster_centers + ~AAclust.get_cluster_medoids + + + + + + \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.CPP.rst b/docs/source/generated/aaanalysis.CPP.rst new file mode 100644 index 00000000..cdb62bd1 --- /dev/null +++ b/docs/source/generated/aaanalysis.CPP.rst @@ -0,0 +1,24 @@ +aaanalysis.CPP +============== + +.. currentmodule:: aaanalysis + +.. autoclass:: CPP + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CPP.__init__ + ~CPP.eval + ~CPP.run + + + + + + \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.CPPPlot.rst b/docs/source/generated/aaanalysis.CPPPlot.rst new file mode 100644 index 00000000..a4dc39ae --- /dev/null +++ b/docs/source/generated/aaanalysis.CPPPlot.rst @@ -0,0 +1,25 @@ +aaanalysis.CPPPlot +================== + +.. currentmodule:: aaanalysis + +.. autoclass:: CPPPlot + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CPPPlot.__init__ + ~CPPPlot.heatmap + ~CPPPlot.profile + ~CPPPlot.update_seq_size + + + + + + \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.SequenceFeature.rst b/docs/source/generated/aaanalysis.SequenceFeature.rst new file mode 100644 index 00000000..336e5607 --- /dev/null +++ b/docs/source/generated/aaanalysis.SequenceFeature.rst @@ -0,0 +1,30 @@ +aaanalysis.SequenceFeature +========================== + +.. currentmodule:: aaanalysis + +.. autoclass:: SequenceFeature + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~SequenceFeature.__init__ + ~SequenceFeature.add_dif + ~SequenceFeature.add_feat_value + ~SequenceFeature.add_position + ~SequenceFeature.feat_matrix + ~SequenceFeature.feat_names + ~SequenceFeature.get_df_parts + ~SequenceFeature.get_features + ~SequenceFeature.get_split_kws + + + + + + \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.dPULearn.rst b/docs/source/generated/aaanalysis.dPULearn.rst new file mode 100644 index 00000000..fb647fa9 --- /dev/null +++ b/docs/source/generated/aaanalysis.dPULearn.rst @@ -0,0 +1,24 @@ +aaanalysis.dPULearn +=================== + +.. currentmodule:: aaanalysis + +.. autoclass:: dPULearn + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~dPULearn.__init__ + ~dPULearn.eval + ~dPULearn.fit + + + + + + \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.load_dataset.rst b/docs/source/generated/aaanalysis.load_dataset.rst new file mode 100644 index 00000000..2b40842c --- /dev/null +++ b/docs/source/generated/aaanalysis.load_dataset.rst @@ -0,0 +1,6 @@ +aaanalysis.load\_dataset +======================== + +.. currentmodule:: aaanalysis + +.. autofunction:: load_dataset \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.load_scales.rst b/docs/source/generated/aaanalysis.load_scales.rst new file mode 100644 index 00000000..93d4717b --- /dev/null +++ b/docs/source/generated/aaanalysis.load_scales.rst @@ -0,0 +1,6 @@ +aaanalysis.load\_scales +======================= + +.. currentmodule:: aaanalysis + +.. autofunction:: load_scales \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.plot_gcfs.rst b/docs/source/generated/aaanalysis.plot_gcfs.rst new file mode 100644 index 00000000..dc10a3bc --- /dev/null +++ b/docs/source/generated/aaanalysis.plot_gcfs.rst @@ -0,0 +1,6 @@ +aaanalysis.plot\_gcfs +===================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_gcfs \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.plot_get_cdict.rst b/docs/source/generated/aaanalysis.plot_get_cdict.rst new file mode 100644 index 00000000..b00a8b2e --- /dev/null +++ b/docs/source/generated/aaanalysis.plot_get_cdict.rst @@ -0,0 +1,6 @@ +aaanalysis.plot\_get\_cdict +=========================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_get_cdict \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.plot_get_cmap.rst b/docs/source/generated/aaanalysis.plot_get_cmap.rst new file mode 100644 index 00000000..6ffe21f7 --- /dev/null +++ b/docs/source/generated/aaanalysis.plot_get_cmap.rst @@ -0,0 +1,6 @@ +aaanalysis.plot\_get\_cmap +========================== + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_get_cmap \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.plot_set_legend.rst b/docs/source/generated/aaanalysis.plot_set_legend.rst new file mode 100644 index 00000000..8c082d21 --- /dev/null +++ b/docs/source/generated/aaanalysis.plot_set_legend.rst @@ -0,0 +1,6 @@ +aaanalysis.plot\_set\_legend +============================ + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_set_legend \ No newline at end of file diff --git a/docs/source/generated/aaanalysis.plot_settings.rst b/docs/source/generated/aaanalysis.plot_settings.rst new file mode 100644 index 00000000..b27c5b2f --- /dev/null +++ b/docs/source/generated/aaanalysis.plot_settings.rst @@ -0,0 +1,6 @@ +aaanalysis.plot\_settings +========================= + +.. currentmodule:: aaanalysis + +.. autofunction:: plot_settings \ No newline at end of file

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