From 58741bb66df039060f8f4d78cc0adacc5b56940a Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Wed, 20 Sep 2023 15:48:18 +0200 Subject: [PATCH] Update Table generation, aa.load_dataset documenation (e2e) --- CONTRIBUTING.rst | 16 +- aaanalysis/__pycache__/utils.cpython-39.pyc | Bin 12087 -> 12094 bytes ...le_classification.xlsx => scales_cat.xlsx} | Bin .../_utils_constants.cpython-39.pyc | Bin 1915 -> 1975 bytes .../__pycache__/utils_cpp.cpython-39.pyc | Bin 10695 -> 10633 bytes aaanalysis/_utils/_utils_constants.py | 6 +- aaanalysis/_utils/utils_cpp.py | 27 ++- .../__pycache__/_feature_pos.cpython-39.pyc | Bin 3696 -> 3712 bytes .../cpp/__pycache__/_part.cpython-39.pyc | Bin 7889 -> 7531 bytes aaanalysis/cpp/__pycache__/cpp.cpython-39.pyc | Bin 12601 -> 12601 bytes .../cpp/__pycache__/feature.cpython-39.pyc | Bin 25244 -> 24955 bytes aaanalysis/cpp/_feature_pos.py | 2 +- aaanalysis/cpp/_part.py | 26 +-- aaanalysis/cpp/cpp.py | 4 +- aaanalysis/cpp/feature.py | 21 +-- 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tutorials/tutorial2_data_loader.ipynb diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index 7d536d61..eaa92c5d 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -215,7 +215,21 @@ Documentation Style `napoleon `_, and `sphinx-design `_ extensions. -- **Further Details**: See `docs/source/conf.py` for more. +- **Further Details**: See our `conf.py `_ + for more. + +Documentation Layers +--------------------- +This project's documentation is organized across four distinct layers, each with a specific focus and level of detail: + +- **Docstrings**: Concise code description, with minimal usage examples and references to other layers (in 'See also'). + +- **Usage Principles**: Bird's-eye view with background and key principles, reflecting by selected code examples. + +- **Tutorial**: Close-up on public interface, as step-by-step guide on essential usage with medium detail. + +- **Tables**: Close-up on data or other tabular overviews, with detailed explanation of columns and critical values. + Building the Docs ----------------- diff --git a/aaanalysis/__pycache__/utils.cpython-39.pyc b/aaanalysis/__pycache__/utils.cpython-39.pyc index a65b9c5ac7a3c681a83c7a8dda6eb6b40bc8b659..f07efe7f1af0af4b7a906bee685988598b4892a9 100644 GIT binary patch delta 48 zcmdlUw=a%2k(ZZ?0SLA(=1MJH$jc`x;_UAe93Si&7$4&65+C3n9PjNKxmlV|MiBr> Ccnwql delta 41 vcmdlNw>^$Gk(ZZ?0SJ~*eO5b~N?hhkqdC%o zt#CC^aUOW1C|}M*XayQ25T_(oXcY-cAxUYh(i&2fL7LXFMjOab7VDJ52IY~Z0&=v8 zJZ+&sIyT9`78RjW2?mu>qzXz@MVU-g$g+8nmw1_1c$J&nLiJIiZJ4mI4I4G=U>AGX ze_SMc_Vua=c`_W_@QFJdc>Wt}DPqaHg*)*EV^2IEM3Yfg^INfNCj6(EH5GEB({|i$ z{i5#(HPCMV{MdE+0So01J6#INah+z*J!@Wx)GvkkZpR5-O8<9*lArKTk<+NRo9^kc pkiyzVy&sfKYFipix7RL4+7W zm|;X1!3tNPQ%96h#27=IaTqj^U;;@dkzxvIrjcO=t6W8vS>%{Qo_Q2lK#@h1SVEa) zR76hXML`rrNt8u}m6;c7T+>-)jq9v)gPYvq_RNQ>qf2iw=;NL}6jpC&kH>@lpl448 zcO&OhY6Re)+|n9P?E4Ak2aAzKspfR#YYEP?T-}9o(!4OO)={TxLUq~dUY=TJ*Ubq0 z$7btiG%fS2ZJl2=n$EuGSi=0#vnMSZ?os!K^Sb<$AGs*|Wa-bIU-JEQiqrSDzX5qH BSWN%` diff --git 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accept_tmd_none # If tmd_seq is given, tmd_len can be None + tmd_seq_given = tmd_seq is not None or accept_tmd_none # If tmd_seq is given, tmd_len can be None ut_check.check_non_negative_number(name="tmd_len", val=tmd_len, accept_none=tmd_seq_given, min_val=1) ut_check.check_non_negative_number(name="jmd_n_len", val=jmd_n_len, accept_none=True, min_val=1) ut_check.check_non_negative_number(name="jmd_c_len", val=jmd_c_len, accept_none=True, min_val=1) ut_check.check_non_negative_number(name="ext_len", val=ext_len, accept_none=True) + # Check if lengths and sequences match + tmd_len = _check_seq(tmd_seq, tmd_len, "tmd_seq", "tmd_len") + jmd_n_len = _check_seq(jmd_n_seq, jmd_n_len, "jmd_n_seq", "jmd_n_len") + jmd_c_len = _check_seq(jmd_c_seq, jmd_c_len, "jmd_c_seq", "jmd_c_len") # Check if lengths are matching if ext_len is not None: if jmd_n_len is None: @@ -123,13 +125,10 @@ def check_args_len(tmd_len=None, jmd_n_len=None, jmd_c_len=None, ext_len=None, if jmd_c_len is None: raise ValueError(f"'jmd_c_len' should not be None if 'ext_len' ({ext_len}) is given") if jmd_n_len is not None and ext_len > jmd_n_len: - raise ValueError(f"'ext_len' ({ext_len}) must be <= jmd_n_len ({jmd_n_len})") + raise ValueError(f"'ext_len' ({ext_len}) must be <= length of jmd_n ({jmd_n_len})") if jmd_c_len is not None and ext_len > jmd_c_len: - raise ValueError(f"'ext_len' ({ext_len}) must be <= jmd_c_len ({jmd_c_len})") - # Check if lengths and sequences match - tmd_len = _check_seq(tmd_seq, tmd_len, "tmd_seq", "tmd_len", verbose) - jmd_n_len = _check_seq(jmd_n_seq, jmd_n_len, "jmd_n_seq", "jmd_n_len", verbose) - jmd_c_len = _check_seq(jmd_c_seq, jmd_c_len, "jmd_c_seq", "jmd_c_len", verbose) + raise ValueError(f"'ext_len' ({ext_len}) must be <= length of jmd_c ({jmd_c_len})") + args_len = dict(tmd_len=tmd_len, jmd_n_len=jmd_n_len, jmd_c_len=jmd_c_len) return args_len diff --git a/aaanalysis/cpp/__pycache__/_feature_pos.cpython-39.pyc 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b/aaanalysis/cpp/_part.py index 6e091bd4..9cf08447 100644 --- a/aaanalysis/cpp/_part.py +++ b/aaanalysis/cpp/_part.py @@ -18,16 +18,6 @@ def check_parts(tmd=None, jmd_n=None, jmd_c=None): raise ValueError("'tmd', 'jmd_n', and 'jmd_c' must be given (should not be None)") -def check_ext_len(jmd_n=None, jmd_c=None, ext_len=None): - """Check if lengths are matching""" - if jmd_n is None or jmd_c is None: - raise ValueError(f"'jmd_n' ({jmd_n}) and 'jmd_c' ({jmd_c}) should be given (not None)") - if ext_len > len(jmd_n): - raise ValueError(f"'ext_len' ({ext_len}) must be <= length of jmd_n ({len(jmd_n)})") - if ext_len > len(jmd_c): - raise ValueError(f"'ext_len' ({ext_len}) must be <= length of jmd_c ({len(jmd_c)})") - - # Part helper functions def _retrieve_string_starting_at_end(seq, start=None, end=None): """Reverse_string_start_end""" @@ -178,16 +168,16 @@ def create_parts(seq=None, tmd_start=None, tmd_stop=None, jmd_n_len=10, jmd_c_le return parts @staticmethod - def get_dict_part_seq(df=None, entry=None, tmd=None, jmd_n=None, jmd_c=None, ext_len=0): + def get_dict_part_seq(df=None, entry=None, tmd_seq=None, jmd_n_seq=None, jmd_c_seq=None, ext_len=None): """Get dictionary for part to sequence either (a) form df using entry or (b) from sequence. Parameters ---------- df: df with sequence features entry: entry for which dict_part_seq should be created - tmd: sequence of TMD - jmd_n: sequence of JMD-N - jmd_c: sequence of JMD-C + tmd_seq: sequence of TMD + jmd_n_seq: sequence of JMD-N + jmd_c_seq: sequence of JMD-C ext_len: length of extending part (starting from C and N terminal part of TMD) Returns @@ -195,8 +185,8 @@ def get_dict_part_seq(df=None, entry=None, tmd=None, jmd_n=None, jmd_c=None, ext dict_part_seq: dictionary with parts to sequence of parts for given entry """ if not (df is None or entry is None): - tmd, jmd_n, jmd_c = _get_parts_from_df(df=df, entry=entry) - check_parts(tmd=tmd, jmd_n=jmd_n, jmd_c=jmd_c) - check_ext_len(jmd_n=jmd_n, jmd_c=jmd_c, ext_len=ext_len) - dict_part_seq = _get_dict_part_seq_from_seq(tmd=tmd, jmd_n=jmd_n, jmd_c=jmd_c, ext_len=ext_len) + tmd_seq, jmd_n_seq, jmd_c_seq = _get_parts_from_df(df=df, entry=entry) + check_parts(tmd=tmd_seq, jmd_n=jmd_n_seq, jmd_c=jmd_c_seq) + ut.check_args_len(jmd_n_seq=jmd_n_seq, jmd_c_seq=jmd_c_seq, ext_len=ext_len, accept_tmd_none=True) + dict_part_seq = _get_dict_part_seq_from_seq(tmd=tmd_seq, jmd_n=jmd_n_seq, jmd_c=jmd_c_seq, ext_len=ext_len) return dict_part_seq diff --git a/aaanalysis/cpp/cpp.py b/aaanalysis/cpp/cpp.py index b3d4c09c..50bd8a8c 100644 --- a/aaanalysis/cpp/cpp.py +++ b/aaanalysis/cpp/cpp.py @@ -15,8 +15,8 @@ # Filtering functions def _filtering_info(df=None, df_scales=None, check_cat=True): - """Get datasets structures for filtering, two dictionaries with feature to scales category resp. - feature positions and one datasets frame with paired pearson correlations of all scales""" + """Get datasets structures for filtering, two dictionaries with feature to scale category resp. + feature positions and one data sets frame with paired pearson correlations of all scales""" if check_cat: dict_c = dict(zip(df[ut.COL_FEATURE], df["category"])) else: diff --git a/aaanalysis/cpp/feature.py b/aaanalysis/cpp/feature.py index d73ec207..24b0dfd0 100644 --- a/aaanalysis/cpp/feature.py +++ b/aaanalysis/cpp/feature.py @@ -196,7 +196,7 @@ class SequenceFeature: # Basic datastructures for features @staticmethod - def get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, ext_len=4, all_parts=False): + def get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, ext_len=None, all_parts=False): """Create DataFrane with sequence parts. Parameters @@ -226,36 +226,29 @@ def get_df_parts(df_seq=None, list_parts=None, jmd_n_len=None, jmd_c_len=None, e Examples -------- - Get sequence parts based on parts columns in df_seq with with 'tmd_e', and 'tmd_jmd' as parts: + 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='GSEC_SUB_SEQ') - >>> df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_e", "tmd_jmd"]) - - Get sequence parts based on sequence column in df_seq and jmd_n_len and jmd_c_len with default parts: - - >>> import aaanalysis as aa - >>> sf = aa.SequenceFeature() - >>> df_seq = aa.load_dataset(name='GSEC_SUB_SEQ') - >>> df_parts = sf.get_df_parts(df_seq=df_seq, jmd_n_len=10, jmd_c_len=10) + >>> df_seq = aa.load_dataset(name='DOM_GSE') + >>> df_parts = sf.get_df_parts(df_seq=df_seq, list_parts=["tmd_e", "tmd_jmd"], jmd_n_len=10, jmd_c_len=10) """ ut.check_args_len(jmd_n_len=jmd_n_len, jmd_c_len=jmd_c_len, ext_len=ext_len, accept_tmd_none=True) df_seq = ut.check_df_seq(df_seq=df_seq, jmd_n_len=jmd_n_len, jmd_c_len=jmd_c_len) list_parts = ut.check_list_parts(list_parts=list_parts, all_parts=all_parts) - seq_info_in_df = set(ut.COLS_SEQ_INFO).issubset(set(df_seq)) + seq_info_in_df = set(ut.COLS_SEQ_TMD_POS_KEY).issubset(set(df_seq)) pa = Parts() dict_parts = {} for i, row in df_seq.iterrows(): entry = row[ut.COL_ENTRY] if jmd_c_len is not None and jmd_n_len is not None and seq_info_in_df: - seq, start, stop = row[ut.COLS_SEQ_INFO].values + seq, start, stop = row[ut.COLS_SEQ_TMD_POS_KEY].values parts = pa.create_parts(seq=seq, tmd_start=start, tmd_stop=stop, jmd_n_len=jmd_n_len, jmd_c_len=jmd_c_len) jmd_n, tmd, jmd_c = parts.jmd_n, parts.tmd, parts.jmd_c else: jmd_n, tmd, jmd_c = row[ut.COLS_PARTS].values - dict_part_seq = pa.get_dict_part_seq(tmd=tmd, jmd_n=jmd_n, jmd_c=jmd_c, ext_len=ext_len) + dict_part_seq = 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STR_AA_GAP = "-" LIST_CANONICAL_AA = ['N', 'A', 'I', 'V', 'K', 'Q', 'R', 'M', 'H', 'F', 'E', 'D', 'C', 'G', 'L', 'T', 'S', 'Y', 'W', 'P'] NAME_SCALE_SETS_BASE = [ut.STR_SCALES, ut.STR_SCALES_RAW] NAMES_SCALE_SETS = NAME_SCALE_SETS_BASE + [ut.STR_SCALE_CAT, ut.STR_SCALES_PC, ut.STR_TOP60, ut.STR_TOP60_EVAL] FOLDER_BENCHMARKS = folder_in = ut.FOLDER_DATA + "benchmarks" + ut.SEP +LIST_NON_CANONICAL_OPTIONS = ["remove", "keep", "gap"] # I Helper Functions -# For load_dataset +# Check functions for load_dataset def check_name_of_dataset(name="INFO", folder_in=None): """""" if name == "INFO": @@ -38,11 +40,52 @@ def check_name_of_dataset(name="INFO", folder_in=None): f"\n Domain datasets: {list_dom}") +def check_min_max_val(min_len=None, max_len=None): + """Check if min_val and max_val are valid and match""" + ut.check_non_negative_number(name="min_len", val=min_len, min_val=1, accept_none=True, just_int=True) + ut.check_non_negative_number(name="max_len", val=max_len, min_val=1, accept_none=True, just_int=True) + if min_len is None or max_len is None: + return + if isinstance(min_len, int) and isinstance(max_len, int) and min_len > max_len: + raise ValueError(f"'min_len' ({min_len}) should not be smaller than 'max_len' ({max_len})") + + +def check_non_canonical_aa(non_canonical_aa="remove"): + """Check if non_canonical_aa is valid""" + if non_canonical_aa not in LIST_NON_CANONICAL_OPTIONS: + raise ValueError(f"'non_canonical_aa' ({non_canonical_aa}) should be on of following:" + f" {LIST_NON_CANONICAL_OPTIONS }") + + +def check_aa_window_size(aa_window_size=None): + """Check if aa_window size is a positive odd integer""" + if aa_window_size is None: + return + ut.check_non_negative_number(name="aa_window_size", val=aa_window_size, min_val=1) + if aa_window_size % 2 == 0: + raise ValueError(f"'aa_window_size' ({aa_window_size}) must be an odd number.") + + +def post_check_df_seq(df_seq=None, n=None, name=None, n_match=True): + """Check if length of df_seq is valid""" + max_n = df_seq["label"].value_counts().min() + error_message = f"'n' ({n}) is too high since smaller class for '{name}' contains {max_n} samples." \ + f" This maximum value depends on filtering settings." + # Validation of sequence and domain datasets + if n is not None: + if n_match and len(df_seq) != n*2: + raise ValueError(error_message) + elif len(df_seq) < n*2: + raise ValueError(error_message) + + +# Helper functions for load_datasets +def is_aa_level(name=None): + return name.split("_")[0] == "AA" + + def _adjust_non_canonical_aa(df=None, non_canonical_aa="remove"): """""" - list_options = ["remove", "keep", "gap"] - if non_canonical_aa not in list_options: - raise ValueError(f"'non_canonical_aa' ({non_canonical_aa}) should be on of following: {list_options}") if non_canonical_aa == "keep": return df # Get all non-canonical amino acids @@ -58,6 +101,31 @@ def _adjust_non_canonical_aa(df=None, non_canonical_aa="remove"): return df +def _get_aa_window(df_seq=None, aa_window_size=9): + """Get amino acid windows from df_seq""" + min_seq_len = df_seq[ut.COL_SEQ].apply(len).min() + if df_seq[ut.COL_SEQ].apply(len).min() <= aa_window_size: + raise ValueError(f"'aa_window_size' ({aa_window_size}) should be smaller than shortest sequence ({min_seq_len})") + n_pre = n_post = int((aa_window_size - 1) / 2) # checked to be an odd number before + list_aa = [] + list_labels = [] + list_entries = [] + for entry, seq, labels in zip(df_seq[ut.COL_ENTRY], df_seq[ut.COL_SEQ], df_seq[ut.COL_LABEL]): + for i in range(len(seq)): + start_pre, end_pre = max(i - n_pre, 0), i + start_post, end_post = i + 1, i + n_post + 1 + aa_window = seq[start_pre:end_pre] + seq[i] + seq[start_post:end_post] + list_aa.append(aa_window) + entry_pos = f"{entry}_pos{i}" + list_entries.append(entry_pos) + list_labels.extend(labels.split(",")) + df_seq = pd.DataFrame({ut.COL_ENTRY: list_entries, ut.COL_SEQ: list_aa, ut.COL_LABEL: list_labels}) + df_seq = df_seq[df_seq[ut.COL_SEQ].apply(len) == aa_window_size] # Remove too short windows at sequence edges + return df_seq + +# Check functions for load_scales + + # For load_scales def _filter_scales(df_cat=None, unclassified_in=False, just_aaindex=False): """Filter scales for unclassified and aaindex scales""" @@ -76,9 +144,12 @@ def _filter_scales(df_cat=None, unclassified_in=False, just_aaindex=False): # II Main Functions def load_dataset(name: str = "INFO", n: Optional[int] = None, + random: bool = False, non_canonical_aa: Literal["remove", "keep", "gap"] = "remove", min_len: Optional[int] = None, - max_len: Optional[int] = None) -> DataFrame: + max_len: Optional[int] = None, + aa_window_size: Optional[int] = 9, + ) -> DataFrame: """ Load protein benchmarking datasets. @@ -90,51 +161,80 @@ def load_dataset(name: str = "INFO", name Name of the dataset. See 'Dataset' column in overview table. n - Number of proteins per class. If None, the whole dataset will be returned. + Number of proteins per class, selected by index. If None, the whole dataset will be returned. + random + If True, ``n`` random selected proteins per class will be chosen. non_canonical_aa Options for modifying non-canonical amino acids: - 'remove': Remove sequences containing non-canonical amino acids. - - 'keep': Do not 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 - Minimum length of sequences for filtering (disabled by default). + Minimum length of sequences for filtering, disabled by default. max_len - Maximum length of sequences for filtering (disabled by default). + Maximum length of sequences for filtering, disabled by default. + aa_window_size + Length of amino acid window, only used for amino acid dataset level (``name='AA_'``) and if ``n`` given. Returns ------- DataFrame Dataframe (df_seq) containing the selected sequence dataset. - Notes - ----- - See further information on the benchmark datasets in ref table. + See also + -------- + See an overview of all benchmarks in :ref:`1_overview_benchmarks` 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) """ - ut.check_non_negative_number(name="n", val=n, accept_none=True) - ut.check_non_negative_number(name="min_len", val=min_len, accept_none=True) check_name_of_dataset(name=name, folder_in=FOLDER_BENCHMARKS) + ut.check_non_negative_number(name="n", val=n, min_val=1, accept_none=True) + check_non_canonical_aa(non_canonical_aa=non_canonical_aa) + check_min_max_val(min_len=min_len, max_len=max_len) + check_aa_window_size(aa_window_size=aa_window_size) # Load overview table if name == "INFO": return pd.read_excel(FOLDER_BENCHMARKS + "INFO_benchmarks.xlsx") df = pd.read_csv(FOLDER_BENCHMARKS + name + ".tsv", sep="\t") - # Filter Rdata + # Filter data if min_len is not None: mask = [len(x) >= min_len for x in df[ut.COL_SEQ]] df = df[mask] + if len(df) == 0: + raise ValueError(f"'min_len' ({min_len}) is too high and removed all sequences.") if max_len is not None: mask = [len(x) <= max_len for x in df[ut.COL_SEQ]] df = df[mask] + if len(df) == 0: + raise ValueError(f"'max_len' ({max_len}) is too low and removed all sequences.") # Adjust non-canonical amino acid (keep, remove, or replace by gap) df_seq = _adjust_non_canonical_aa(df=df, non_canonical_aa=non_canonical_aa) + # Adjust amino acid windows for 'AA' datasets + if is_aa_level(name=name): + # Special case that unfiltered df_seq is returned + if aa_window_size is None: + return df_seq.reset_index(drop=True) + df_seq = _get_aa_window(df_seq=df_seq, aa_window_size=aa_window_size) # Select balanced groups + post_check_df_seq(df_seq=df_seq, n=n, name=name, n_match=False) if n is not None: labels = set(df_seq[ut.COL_LABEL]) - df_seq = pd.concat([df_seq[df_seq[ut.COL_LABEL] == l].head(n) for l in labels]) + if random: + df_seq = pd.concat([df_seq[df_seq[ut.COL_LABEL] == l].sample(n) for l in labels]) + else: + df_seq = pd.concat([df_seq[df_seq[ut.COL_LABEL] == l].head(n) for l in labels]) + # Adjust index + df_seq = df_seq.reset_index(drop=True) + post_check_df_seq(df_seq=df_seq, n=n, name=name) return df_seq @@ -143,23 +243,23 @@ def load_scales(name="scales", just_aaindex=False, unclassified_in=True): """ Load amino acid scales, scale classification (AAontology), or scale evaluation. - A through analysis of the residue and sequence datasets can be found in [Breimann23a]_. + 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', 'scale_cat', + 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 'scale_classification'. + 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 'scale_classification'. + Only relevant if `name` is 'scales', 'scales_raw', or 'scales_cat'. Returns ------- - pd.DataFrame + DataFrame Dataframe for the selected scale dataset. """ if name not in NAMES_SCALE_SETS: diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py index 945c6f5a..761952f1 100644 --- a/aaanalysis/utils.py +++ b/aaanalysis/utils.py @@ -36,7 +36,7 @@ def check_df_seq(df_seq=None, jmd_n_len=None, jmd_c_len=None): raise ValueError("Type of 'df_seq' ({}) must be pd.DataFrame".format(type(df_seq))) if ut_c.COL_ENTRY not in list(df_seq): raise ValueError("'{}' must be in 'df_seq'".format(ut_c.COL_ENTRY)) - seq_info_in_df = set(ut_c.COLS_SEQ_INFO).issubset(set(df_seq)) + seq_info_in_df = set(ut_c.COLS_SEQ_TMD_POS_KEY).issubset(set(df_seq)) parts_in_df = set(ut_c.COLS_PARTS).issubset(set(df_seq)) seq_in_df = ut_c.COL_SEQ in set(df_seq) if "start" in list(df_seq): @@ -44,7 +44,7 @@ def check_df_seq(df_seq=None, jmd_n_len=None, jmd_c_len=None): if "stop" in list(df_seq): raise ValueError(f"'df_seq' should not contain 'stop' in columns. Change column to '{ut_c.COL_TMD_STOP}'.") if not (seq_info_in_df or parts_in_df or seq_in_df): - raise ValueError(f"'df_seq' should contain ['{ut_c.COL_SEQ}'], {ut_c.COLS_SEQ_INFO}, or {ut_c.COLS_PARTS}") + raise ValueError(f"'df_seq' should contain ['{ut_c.COL_SEQ}'], {ut_c.COLS_SEQ_TMD_POS_KEY}, or {ut_c.COLS_PARTS}") # Check data type in part or sequence columns else: if seq_info_in_df or seq_in_df: @@ -75,21 +75,21 @@ def check_df_seq(df_seq=None, jmd_n_len=None, jmd_c_len=None): tmd_stop = [x - jmd_c_len for x in tmd_stop] df_seq[ut_c.COL_TMD_START] = tmd_start df_seq[ut_c.COL_TMD_STOP] = tmd_stop - seq_info_in_df = set(ut_c.COLS_SEQ_INFO).issubset(set(df_seq)) + seq_info_in_df = set(ut_c.COLS_SEQ_TMD_POS_KEY).issubset(set(df_seq)) # Check parameter combinations if [jmd_n_len, jmd_c_len].count(None) == 1: raise ValueError("'jmd_n_len' and 'jmd_c_len' should both be given (not None) or None") if not parts_in_df and seq_info_in_df and jmd_n_len is None and jmd_c_len is None: error = f"'jmd_n_len' and 'jmd_c_len' should not be None if " \ - f"sequence information ({ut_c.COLS_SEQ_INFO}) are given." + f"sequence information ({ut_c.COLS_SEQ_TMD_POS_KEY}) are given." raise ValueError(error) if not seq_info_in_df and jmd_n_len is not None and jmd_c_len is not None: - error = f"If not all sequence information ({ut_c.COLS_SEQ_INFO}) are given," \ + error = f"If not all sequence information ({ut_c.COLS_SEQ_TMD_POS_KEY}) are given," \ f"'jmd_n_len' and 'jmd_c_len' should be None." raise ValueError(error) if not parts_in_df and seq_info_in_df and (jmd_c_len is None or jmd_n_len is None): error = "If part columns ({}) are not in 'df_seq' but sequence information ({}), " \ - "\n'jmd_n_len' and 'jmd_c_len' should be given (not None).".format(ut_c.COLS_PARTS, ut_c.COLS_SEQ_INFO) + "\n'jmd_n_len' and 'jmd_c_len' should be given (not None).".format(ut_c.COLS_PARTS, ut_c.COLS_SEQ_TMD_POS_KEY) raise ValueError(error) return df_seq diff --git 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Bases: object

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

@@ -235,7 +228,7 @@

aaanalysis.SequenceFeature
-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]
+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
@@ -282,7 +275,7 @@

aaanalysis.SequenceFeature
-get_features(list_parts=None, split_kws=None, df_scales=None, all_parts=False)[source]
+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
@@ -304,7 +297,7 @@

aaanalysis.SequenceFeature
-static feat_matrix(features=None, df_parts=None, df_scales=None, accept_gaps=False, n_jobs=None, verbose=False, return_labels=False)[source]
+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
@@ -329,7 +322,7 @@

aaanalysis.SequenceFeature
-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]
+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
@@ -366,7 +359,7 @@

aaanalysis.SequenceFeature
-static add_feat_value(df_parts=None, split=None, dict_scale=None, accept_gaps=False)[source]
+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
@@ -415,7 +408,7 @@

aaanalysis.SequenceFeature
-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]
+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
@@ -439,7 +432,7 @@

aaanalysis.SequenceFeature
-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]
+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
diff --git a/docs/build/html/generated/aaanalysis.dPULearn.html b/docs/build/html/generated/aaanalysis.dPULearn.html index 978ae3da..6d6d2b25 100644 --- a/docs/build/html/generated/aaanalysis.dPULearn.html +++ b/docs/build/html/generated/aaanalysis.dPULearn.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -86,7 +86,7 @@ diff --git a/docs/build/html/generated/aaanalysis.load_dataset.html b/docs/build/html/generated/aaanalysis.load_dataset.html index 13b696e9..7df59d0d 100644 --- a/docs/build/html/generated/aaanalysis.load_dataset.html +++ b/docs/build/html/generated/aaanalysis.load_dataset.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -87,7 +87,7 @@ @@ -120,7 +120,7 @@

aaanalysis.load_dataset

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

@@ -128,16 +128,18 @@

aaanalysis.load_datasetParameters
  • name (str) – Name of the dataset. See ‘Dataset’ column in overview table.

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

  • +
  • 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’: Do not 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).

  • +
  • 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
@@ -147,9 +149,17 @@

aaanalysis.load_dataset

DataFrame

-
-

Notes

-

See further information on the benchmark datasets in ref table.

+
+
+

Examples

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

diff --git a/docs/build/html/generated/aaanalysis.load_scales.html b/docs/build/html/generated/aaanalysis.load_scales.html index fefba7e0..67fb3968 100644 --- a/docs/build/html/generated/aaanalysis.load_scales.html +++ b/docs/build/html/generated/aaanalysis.load_scales.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -87,7 +87,7 @@ @@ -120,25 +120,25 @@

aaanalysis.load_scales

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

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

-

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

+

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’, ‘scale_cat’, +

  • 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 ‘scale_classification’.

  • +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 ‘scale_classification’.

  • +Only relevant if name is ‘scales’, ‘scales_raw’, or ‘scales_cat’.

Returns

Dataframe for the selected scale dataset.

Return type
-

pd.DataFrame

+

DataFrame

diff --git a/docs/build/html/generated/aaanalysis.plot_gcfs.html b/docs/build/html/generated/aaanalysis.plot_gcfs.html index 5230b66d..68e4dc99 100644 --- a/docs/build/html/generated/aaanalysis.plot_gcfs.html +++ b/docs/build/html/generated/aaanalysis.plot_gcfs.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -90,7 +90,7 @@ diff --git a/docs/build/html/generated/aaanalysis.plot_get_cdict.html b/docs/build/html/generated/aaanalysis.plot_get_cdict.html index 91e75d41..bd23fe94 100644 --- a/docs/build/html/generated/aaanalysis.plot_get_cdict.html +++ b/docs/build/html/generated/aaanalysis.plot_get_cdict.html @@ -37,7 +37,7 @@ - + @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -90,7 +90,7 @@ @@ -146,7 +146,7 @@

aaanalysis.plot_get_cdict - +
diff --git a/docs/build/html/generated/aaanalysis.plot_get_cmap.html b/docs/build/html/generated/aaanalysis.plot_get_cmap.html index 54b43d5b..c31941b0 100644 --- a/docs/build/html/generated/aaanalysis.plot_get_cmap.html +++ b/docs/build/html/generated/aaanalysis.plot_get_cmap.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -90,7 +90,7 @@ diff --git a/docs/build/html/generated/aaanalysis.plot_set_legend.html b/docs/build/html/generated/aaanalysis.plot_set_legend.html index cec903ec..f953df1d 100644 --- a/docs/build/html/generated/aaanalysis.plot_set_legend.html +++ b/docs/build/html/generated/aaanalysis.plot_set_legend.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -90,7 +90,7 @@ diff --git a/docs/build/html/generated/aaanalysis.plot_settings.html b/docs/build/html/generated/aaanalysis.plot_settings.html index e03533a3..6b7d48bd 100644 --- a/docs/build/html/generated/aaanalysis.plot_settings.html +++ b/docs/build/html/generated/aaanalysis.plot_settings.html @@ -63,7 +63,7 @@

OVERVIEW

EXAMPLES

@@ -90,7 +90,7 @@ diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 755857ba..670c267a 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -52,7 +52,7 @@

OVERVIEW

EXAMPLES

@@ -64,7 +64,7 @@
  • API
  • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 9c01ec3a..ef1f81d4 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -64,7 +64,7 @@

    OVERVIEW

    EXAMPLES

    @@ -76,7 +76,7 @@
  • API
  • @@ -138,7 +138,7 @@

    InstallOVERVIEW

    @@ -164,7 +164,7 @@

    Install diff --git a/docs/build/html/index/CONTRIBUTING_COPY.html b/docs/build/html/index/CONTRIBUTING_COPY.html index 952be13a..4f495b44 100644 --- a/docs/build/html/index/CONTRIBUTING_COPY.html +++ b/docs/build/html/index/CONTRIBUTING_COPY.html @@ -40,7 +40,7 @@ - + @@ -65,7 +65,7 @@

    OVERVIEW

    + +
    +

    Documentation Layers

    +

    This project’s documentation is organized across four distinct layers, each with a specific focus and level of detail:

    +
      +
    • Docstrings: Concise code description, with minimal usage examples and references to other layers (in ‘See also’).

    • +
    • Usage Principles: Bird’s-eye view with background and key principles, reflecting by selected code examples.

    • +
    • Tutorial: Close-up on public interface, as step-by-step guide on essential usage with medium detail.

    • +
    • Tables: Close-up on data or other tabular overviews, with detailed explanation of columns and critical values.

    @@ -348,7 +360,7 @@

    Building the Docs - +

    diff --git a/docs/build/html/_index/badges.html b/docs/build/html/index/badges.html similarity index 92% rename from docs/build/html/_index/badges.html rename to docs/build/html/index/badges.html index e1163bd3..ece9f398 100644 --- a/docs/build/html/_index/badges.html +++ b/docs/build/html/index/badges.html @@ -3,12 +3,12 @@ - + - + @@ -62,9 +62,9 @@ @@ -95,7 +95,7 @@
  • - Edit on GitHub + Edit on GitHub

  • diff --git a/docs/build/html/index/citations.html b/docs/build/html/index/citations.html index 657a06dc..a4e13c21 100644 --- a/docs/build/html/index/citations.html +++ b/docs/build/html/index/citations.html @@ -63,7 +63,7 @@

    OVERVIEW

    EXAMPLES

    @@ -75,7 +75,7 @@
  • API
  • diff --git a/docs/build/html/index/introduction.html b/docs/build/html/index/introduction.html index b632ff93..b6f0a69e 100644 --- a/docs/build/html/index/introduction.html +++ b/docs/build/html/index/introduction.html @@ -39,7 +39,7 @@ - + @@ -68,7 +68,7 @@
  • Workflow
  • -
  • Usage Principles
  • +
  • Usage Principles
  • Contributing
  • EXAMPLES

    @@ -80,7 +80,7 @@
  • API
  • @@ -129,7 +129,7 @@

    Workflow - +
    diff --git a/docs/build/html/_index/overview.html b/docs/build/html/index/overview.html similarity index 91% rename from docs/build/html/_index/overview.html rename to docs/build/html/index/overview.html index 0e1fda2a..16665824 100644 --- a/docs/build/html/_index/overview.html +++ b/docs/build/html/index/overview.html @@ -3,12 +3,12 @@ - + - + @@ -62,9 +62,9 @@ @@ -95,7 +95,7 @@
  • - Edit on GitHub + Edit on GitHub

  • diff --git a/docs/build/html/index/references.html b/docs/build/html/index/references.html index 469a9a17..eaf67efa 100644 --- a/docs/build/html/index/references.html +++ b/docs/build/html/index/references.html @@ -39,7 +39,7 @@ - + @@ -64,7 +64,7 @@

    OVERVIEW

    EXAMPLES

    @@ -76,7 +76,7 @@
  • API

  • @@ -114,14 +114,15 @@

    Tables

    -

    Overview Table

    - +

    Overview Table

    +

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

    +
    @@ -146,8 +147,18 @@

    Overview Table

    +

    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’).

    +
    @@ -182,7 +193,7 @@

    Protein benchmark datasetsSong18

    @@ -193,7 +204,7 @@

    Protein benchmark datasetsSong18

    @@ -204,7 +215,7 @@

    Protein benchmark datasetsTang20

    @@ -215,7 +226,7 @@

    Protein benchmark datasetsSong18

    @@ -226,7 +237,7 @@

    Protein benchmark datasetsYang21

    @@ -237,7 +248,7 @@

    Protein benchmark datasetsSong18

    @@ -248,7 +259,7 @@

    Protein benchmark datasetsTeng21

    @@ -259,7 +270,7 @@

    Protein benchmark datasetsGaliez16

    @@ -270,7 +281,7 @@

    Protein benchmark datasetsCheng06

    @@ -281,7 +292,7 @@

    Protein benchmark datasetsShen19

    @@ -292,7 +303,7 @@

    Protein benchmark datasetsMagnan09

    @@ -303,7 +314,7 @@

    Protein benchmark datasetsGaliez16

    @@ -314,7 +325,7 @@

    Protein benchmark datasetsBreimann23c

    @@ -325,15 +336,16 @@

    Protein benchmark datasetsBreimann23c

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

    Amino acid

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

    Amino acid

    1 (disordered), 0 (ordered)

    Amino acid

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

    Amino acid

    1 (binding), 0 (non-binding)

    Amino acid

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

    Sequence

    1 (amyloidogenic), 0 (non-amyloidogenic)

    Sequence

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

    Sequence

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

    Sequence

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

    Sequence

    1 (soluble), 0 (insoluble)

    Sequence

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

    Domain

    1 (substrate), 0 (non-substrate)

    Domain

    1 (substrate), 2 (unknown substrate status)

    -

    Amino acid scale datasets

    - +

    Amino acid scale datasets

    +

    Different amino acid scale datasets are provided

    +
    @@ -351,32 +363,32 @@

    Amino acid scale datasets

    - + - + - + - + - + - +

    scales

    Amino acid scales (min-max normalized)

    586

    Breimann23b

    Breimann23b

    scales_raw

    Amino acid scales (raw values)

    586

    Kawashima08

    Kawashima08

    scales_classification

    Classification of scales (Aaontology)

    586

    Breimann23b

    Breimann23b

    scales_pc

    Principal component (PC) compressed scales

    20

    Breimann23a

    Breimann23a

    top60

    Top 60 scale subsets

    60

    Breimann23a

    Breimann23a

    top60_eval

    Evaluation of top 60 scale subsets

    60

    Breimann23a

    Breimann23a

    @@ -388,7 +400,7 @@

    Amino acid scale datasets - +


    diff --git a/docs/build/html/index/tables_template.html b/docs/build/html/index/tables_template.html deleted file mode 100644 index ccb0815a..00000000 --- a/docs/build/html/index/tables_template.html +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - - - - - - - - - - - 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

    Overview_benchmarks

    Protein benchmark datasets

    aa.load_dataset

    Overview_scales

    Amino acid scale datasets

    aa.load_scales

    -
    - -
    - - -
    -
    -
    - -
    - -
    -

    © 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..37737eb8 --- /dev/null +++ b/docs/build/html/index/usage_principles/aaontology.html @@ -0,0 +1,136 @@ + + + + + + + + + + + + + + + + + AAontology: Classification of amino acid scales — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
    • + +
    • + Edit on GitHub +
    • +
    +
    +
    +
    +
    + +
    +

    AAontology: Classification of amino acid scales

    +

    AAontology is a two-level classification of amino acid scale, introduced in [Breimann23b].

    +
    + + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, Stephan Breimann.

    +
    + + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/_index/usage_principles/data_loading.html b/docs/build/html/index/usage_principles/data_flow_entry_points.html similarity index 66% rename from docs/build/html/_index/usage_principles/data_loading.html rename to docs/build/html/index/usage_principles/data_flow_entry_points.html index ef75b34e..ab5caeb4 100644 --- a/docs/build/html/_index/usage_principles/data_loading.html +++ b/docs/build/html/index/usage_principles/data_flow_entry_points.html @@ -1,20 +1,20 @@ - + - + - + - - - + + + - Data Loading — AAanalysis + Data Flow and Enry Points — AAanalysis @@ -62,9 +62,9 @@ @@ -93,9 +93,9 @@

    @@ -103,19 +103,12 @@
    -
    -

    Data Loading

    -

    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’).

    -

    See also

    +
    +

    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/index/usage_principles/feature_identification.html b/docs/build/html/index/usage_principles/feature_identification.html new file mode 100644 index 00000000..7e78d856 --- /dev/null +++ b/docs/build/html/index/usage_principles/feature_identification.html @@ -0,0 +1,137 @@ + + + + + + + + + + + + + + + + + Identifying Physicochemical Signatures using CPP — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
    • + +
    • + Edit on GitHub +
    • +
    +
    +
    +
    +
    + +
    +

    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.

    +
    + + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, Stephan Breimann.

    +
    + + + +
    +
    +
    +
    +
    + + + + \ 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..895e2b21 --- /dev/null +++ b/docs/build/html/index/usage_principles/pu_learning.html @@ -0,0 +1,148 @@ + + + + + + + + + + + + + + + + + Learning from unbalanced and small data — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    Learning from unbalanced and small data

    +

    Unbalanced and small datasets are everywhere in life science … [Breimann23c].

    +

    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, [Breimann23c].

    +
    +

    What is PU learning?

    +

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

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

    © Copyright 2023, Stephan Breimann.

    +
    + + + +
    +
    +
    +
    +
    + + + + \ 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..ede290e2 --- /dev/null +++ b/docs/build/html/index/usage_principles/xai.html @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + Explainable AI at Sequence Level — AAanalysis + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    Explainable AI at Sequence Level

    +

    Unbalanced and small datasets are everywhere in life science … [Breimann23c].

    +
    +

    What is explainable AI?

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

    © Copyright 2023, Stephan Breimann.

    +
    + + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 46a2141dd605ba60ef99f6ea3e0ea8d6a91e09e9..7135c926cc9b9353aa2db0b91b48ee5146b80a88 100644 GIT binary patch delta 1396 zcmV-)1&jKE3gQcpe}7ruZ`(Eye%D_?pnY`{XTY!p!vHr+hX7qGGzo@13A9AXL?}`f zsU+@SzauGHlJ$#jl4Syz9s-)XWk+;Jx38;U= zL^;kW&lY-jQI>>ZMFn35kUV3y3{pzfGC&zok;`4ZD9Z!qBoQ=O2zcqOXeS4UCRrTw zJX2i|kh>+4Z-%@H!`bo$K4Y;bl&vWgzY{ivP*(g+03j2~ClT6j3gfhd%%bfGRyi(~ z0V_(8Qbv{mtAC0F!Xin&N0R<{Bj`Q(UJ)LXPw=``NoE(~$lnL^goXJTbetqnLeuks zr|=xXF{#M;I0_-DpKSgNgL#7-L$P{2BLQ?0M4ksmEFKOMnZ%@z71r5i$w3V&UddJ?emDPsc~(xa%?bG*i_ zPRad5(d;lM;p1n5OFmmlo#Wur9*q9Q(+|&2@cpp7nrc6&Q#!wC#G+!x*gW(WWaR}s zw5A;@CNM}3R#*~~hqU6cDdHpWZU4c!8wVLCD)5j~y;?xW5;5-4PZzu_k zl|P<~n12DS8!dlK2pETi^hS&TLgEeDgiOLJxGKw2Kutgcc?l|96r@B_CD&%l>%FM( z7M3tlBro9dT$h{LF)W25l%p-wn_Z5}Csc1B8LT7mOv-0UUZR6ldLX-~RV!+fQ!WsW zX@bm#YFMrsmU?HNz)(j(E(>bn5)tbt_-H)QCV#vOwk4rC=KSgx+{K0)CV9OmNlHpx zZCiP4Lbj)e#J(Z%j_~aAS8sQ!%vT?BDBxgh{B?>WmzaaQOU*lNUTO1R@DS8m7Qu`t zC!~aZH4$5TuUg&)$_dtOGah7^QSxK${hCt^MSG2B8xPbU55|Ol7TEP2s5=%+brL?H zhkuR(^~VFB=3$m(sM$c{_Fz)EX4yIthXxG6taQzC^mGo57=t-k^P_4>AR4v>(~>t! z(-lWFWC=V}(37VlnrPG<%xmNcgicY>m^JWGPd~yVwMFChU|wClDH|$id{7YCYa)OjVYDds!8^LX>tn0Y-+~Oqbo}2c#4f_M6UQxx!pTUth_2as_)eogG1Cdm=8R|v1 ztuA@ZNDbk-g?_lRjBq#V)DJyUF7)m2xem6COFz(#+n@*YTnAJVXCx{>Ghoa@GC<33-b8+|FEDUI!aPDG0EQ9Cv|2h&5OOuRs2pvrwz2uQi~n9p#a zotthr&?m4V3@7@QmwOZ%^Vlq0b3zjUYhGl27Q=z{PO)Qq{oko@2d5_%(2(We!_Ym= zc>P^XCAEW=`Vo$zip)JwyUow2<$rCb@!7?urN~jmx0qqrMv2Ld%rRr8R~}=3#ngip zye!-8E40y+>wSkdcL*!wjNEW~R-P>Oz12wSNubjb7 z^&dFKFiTqvJ7@TaqjQQ4gQ>aW-K#d6cD0+0ptUwr(bdX%8IbVtu!ceFC2eNjF5Igf zY~jvJN`Q%X-9S5LWA3dzgvAXc+#S~5DVKt0Ywra;x^uPn*U)v$l`(~Rc6I4Z0(Xb2 zwYPVrL`gC0l%B(2Jq`Eq$h)~qcKiXii8wwnZJJ}7WqZ`4aD8^8!5*LTLjMDN<;>F> CM4V>; delta 1171 zcmV;E1Z?}_3xW!ee}9-;Z`(E$$KUxW2z0M%;tUwJq8Q+1-C!VVg*L&kCxVuaWug?N zic}K!(~lSHX5Hvoen=wmobx*rNuB>uD5Zk(gJkmUdL8jnDx4qQMVv~BS2zIy1yvB= z(4ODhy-N9;pKyQ%Y=khu6d?wdg4$>CCA0{Hf`V|m1uiXdL?2o8N6}4AzclgU%V^zoyIYV+3tGqoxUde zH7VhGIr2<0KiT||4E709s*Ba@6%*)A0_1i0MjPWD$!Vdkm|^Fxub4sRT)zlkUlvM1 zsgsxLZ?7v3d~~Tl-pvx_x&7$~RxnrSPn2$mNODt^dVdlS_>{2$4e6yg?&o-o3(i^l ziK5xb9EVR2KnpQjN-M{~t*wpz#nX@HC-{CWucq1$bx!9uP0UMfjm<@GLjo`G(4KZu z@r-Kep^8ed>#!?DWQ+I+zU@Cack3V{%3Tfe;yL$0oO!)S679TTwetkZe4)fFQvSRu zVg`C_w151WI$#{BmzQwRgxDL}giK+u_Na)~)} z=KlqK(xksj@TVTZ4{ij}oV6+X);X=i4cZpKQhzGM-Otqe*y>=sT&efr$cnpLye96jm7BSti*bbhH?s)&beXj%nl({%L`4_T0h zFnZ?c=qMgFqj}jpBVi@7c+84?1l2F$i2&nqJDL|<@05)^<1wpAXDeT~7*?Y$cw;V6 z<95v5^7t2cl%;@`R^~n6^}5Q2wQre%aDQ6h7&rHyK0bcl{PFa-`S>@v{}le$qxM}~ zp8HI=?%CAw6P@Wpx)Rjx8=ReKSQeMA)#*jIt<^d-Bj{8w`hXKH!Jq4U)b37*f;8uP z5Br1@hfW_e+?MnirydUSWJYmVkD{;ih048d7;b;{)y0%1f{NSUy#2Fy;Hc$?pno#B zsr{pun8WeVb(+F%js|Q`^muPz%(fZ$@T!-Q3A289y+L_N>?hQ8XOWjdOVERVIEW

    EXAMPLES

    @@ -67,7 +67,7 @@
  • API
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"aaanalysis.plot_set_legend": [[11, "aaanalysis-plot-set-legend"]], "aaanalysis.plot_settings": [[12, "aaanalysis-plot-settings"]], "Welcome to the AAanalysis documentation": [[13, "welcome-to-the-aaanalysis-documentation"]], "Install": [[13, "install"]], "OVERVIEW": [[13, null]], "EXAMPLES": [[13, null]], "REFERENCES": [[13, null]], "Indices and tables": [[13, "indices-and-tables"]], "Citation": [[13, "citation"]], "Contributing": [[14, "contributing"]], "Introduction": [[14, "introduction"], [17, "introduction"]], "Vision": [[14, "vision"]], "Objectives": [[14, "objectives"]], "Non-goals": [[14, "non-goals"]], "Principles": [[14, "principles"]], "Bug Reports": [[14, "bug-reports"]], "Installation": [[14, "installation"]], "Latest Version": [[14, "latest-version"]], "Local Development Environment": [[14, "local-development-environment"]], "Fork and Clone": [[14, "fork-and-clone"]], "Install Dependencies": [[14, "install-dependencies"]], "Run Unit Tests": [[14, "run-unit-tests"]], 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[[20, "amino-acid-scale-datasets"], [26, "amino-acid-scale-datasets"]], "AAontology: Classification of amino acid scales": [[21, "aaontology-classification-of-amino-acid-scales"]], "Data Flow and Enry Points": [[22, "data-flow-and-enry-points"]], "Identifying Physicochemical Signatures using CPP": [[23, "identifying-physicochemical-signatures-using-cpp"]], "Learning from unbalanced and small data": [[24, "learning-from-unbalanced-and-small-data"]], "What is PU learning?": [[24, "what-is-pu-learning"]], "Explainable AI at Sequence Level": [[25, "explainable-ai-at-sequence-level"]], "What is explainable AI?": [[25, "what-is-explainable-ai"]], "Tutorials": [[27, "tutorials"]], "Quick start": [[27, "quick-start"]], "Data loading": [[27, "data-loading"]], "Redundancy-reduction": [[27, "redundancy-reduction"]], "Feature engineering": [[27, "feature-engineering"]], "Identification of negatives": [[27, "identification-of-negatives"]], "Protein prediction": [[27, "protein-prediction"]], "Usage 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"sequencefeature (class in aaanalysis)": [[4, "aaanalysis.SequenceFeature"]], "__init__() (aaanalysis.sequencefeature method)": [[4, "aaanalysis.SequenceFeature.__init__"]], "add_dif() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_dif"]], "add_feat_value() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_feat_value"]], "add_position() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.add_position"]], "feat_matrix() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.feat_matrix"]], "feat_names() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.feat_names"]], "get_df_parts() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.get_df_parts"]], "get_features() (aaanalysis.sequencefeature method)": [[4, "aaanalysis.SequenceFeature.get_features"]], "get_split_kws() (aaanalysis.sequencefeature static method)": [[4, "aaanalysis.SequenceFeature.get_split_kws"]], "__init__() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.__init__"]], "dpulearn (class in aaanalysis)": [[5, "aaanalysis.dPULearn"]], "eval() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.eval"]], "fit() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.fit"]], "labels_ (aaanalysis.dpulearn attribute)": [[5, "aaanalysis.dPULearn.labels_"]], "load_dataset() (in module aaanalysis)": [[6, "aaanalysis.load_dataset"]], "load_scales() (in module aaanalysis)": [[7, "aaanalysis.load_scales"]], "plot_gcfs() (in module aaanalysis)": [[8, "aaanalysis.plot_gcfs"]], "plot_get_cdict() (in module aaanalysis)": [[9, "aaanalysis.plot_get_cdict"]], "plot_get_cmap() (in module aaanalysis)": [[10, "aaanalysis.plot_get_cmap"]], "plot_set_legend() (in module aaanalysis)": [[11, "aaanalysis.plot_set_legend"]], "plot_settings() (in module aaanalysis)": [[12, "aaanalysis.plot_settings"]]}}) \ No newline at end of file diff --git a/docs/build/html/tables_template.html b/docs/build/html/tables_template.html new file mode 100644 index 00000000..e0a4b782 --- /dev/null +++ b/docs/build/html/tables_template.html @@ -0,0 +1,163 @@ + + + + + + + + + + + + + + + + + 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/tutorials.html b/docs/build/html/tutorials.html index 1ab87bca..ea466e12 100644 --- a/docs/build/html/tutorials.html +++ b/docs/build/html/tutorials.html @@ -5,12 +5,12 @@ - + - - - + + + @@ -65,12 +65,13 @@

    OVERVIEW

    EXAMPLES

    • Tutorials @@ -114,9 +115,12 @@

      Tutorials

      +
      +

      Quick start

      +

      Redundancy-reduction

      diff --git a/docs/build/html/index/usage_principles.html b/docs/build/html/usage_principles.html similarity index 52% rename from docs/build/html/index/usage_principles.html rename to docs/build/html/usage_principles.html index c2f23955..81d8de9a 100644 --- a/docs/build/html/index/usage_principles.html +++ b/docs/build/html/usage_principles.html @@ -3,44 +3,44 @@ - + - + Usage Principles — AAanalysis - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + @@ -48,14 +48,14 @@

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z2z`7~^ysh;^+ERa((+*=%bk$NgKPpIGnGsD7?Y-PBlEne81X{H*K4yar7NtQ^IM~a zLg+wnIo@nIBJ+ZdQM^pYLra&3%=3ticp>7JE^jEhJfsl1JZ#d~<;@~U-wBsD(@E`Q z{K!k2X?l+Vm>28>#S87E;W5Q^C>>K63OV9-F*G2~@8uz4;V}g?Y3QMmdBG}BywoZh zhS7jHe+vy2CcK4qeIB0M&4S20uVW(~bSwz*ut{ToH;c&oyO~aAC%5yi-Rro9c~ @@ -150,7 +150,7 @@

    API -

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

    +

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

    Load protein benchmarking datasets.

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

    diff --git a/docs/build/html/generated/aaanalysis.AAclust.html b/docs/build/html/generated/aaanalysis.AAclust.html index 4b6b8ae5..152d380f 100644 --- a/docs/build/html/generated/aaanalysis.AAclust.html +++ b/docs/build/html/generated/aaanalysis.AAclust.html @@ -63,7 +63,7 @@

    OVERVIEW

    EXAMPLES

    @@ -89,7 +89,7 @@ diff --git a/docs/build/html/generated/aaanalysis.CPP.html b/docs/build/html/generated/aaanalysis.CPP.html index 9fe66103..7e0e8ec5 100644 --- a/docs/build/html/generated/aaanalysis.CPP.html +++ b/docs/build/html/generated/aaanalysis.CPP.html @@ -63,7 +63,7 @@

    OVERVIEW

    EXAMPLES

    @@ -89,7 +89,7 @@ diff --git a/docs/build/html/generated/aaanalysis.CPPPlot.html b/docs/build/html/generated/aaanalysis.CPPPlot.html index ee586f59..8a1d7a5b 100644 --- a/docs/build/html/generated/aaanalysis.CPPPlot.html +++ b/docs/build/html/generated/aaanalysis.CPPPlot.html @@ -63,7 +63,7 @@

    OVERVIEW

    EXAMPLES

    @@ -89,7 +89,7 @@ diff --git a/docs/build/html/generated/aaanalysis.SequenceFeature.html b/docs/build/html/generated/aaanalysis.SequenceFeature.html index 5720193e..6852a658 100644 --- a/docs/build/html/generated/aaanalysis.SequenceFeature.html +++ b/docs/build/html/generated/aaanalysis.SequenceFeature.html @@ -63,7 +63,7 @@

    OVERVIEW

    EXAMPLES

    @@ -89,7 +89,7 @@ @@ -122,7 +122,7 @@

    aaanalysis.SequenceFeature

    -class aaanalysis.SequenceFeature[source]
    +class aaanalysis.SequenceFeature[source]
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HcmV?d00001 diff --git a/docs/build/html/_images/social_previews/summary_index_usage_principles_25466335.png b/docs/build/html/_images/social_previews/summary_usage_principles_25466335.png similarity index 100% rename from docs/build/html/_images/social_previews/summary_index_usage_principles_25466335.png rename to docs/build/html/_images/social_previews/summary_usage_principles_25466335.png diff --git a/docs/build/html/_sources/_index/usage_principles/data_loading.rst.txt b/docs/build/html/_sources/_index/usage_principles/data_loading.rst.txt deleted file mode 100644 index 6d77f9b8..00000000 --- a/docs/build/html/_sources/_index/usage_principles/data_loading.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -Data Loading -============ - -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'). - -See also diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 8a775008..f02cf3dd 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -5,8 +5,8 @@ Welcome to the AAanalysis documentation ======================================= -.. include:: _index/badges.rst -.. include:: _index/overview.rst +.. include:: index/badges.rst +.. include:: index/overview.rst Install ======= @@ -25,7 +25,7 @@ Install :caption: OVERVIEW index/introduction.rst - index/usage_principles.rst + usage_principles.rst index/CONTRIBUTING_COPY.rst .. toctree:: @@ -43,7 +43,7 @@ Install .. toctree:: :maxdepth: 1 - _index/tables.rst + index/tables.rst index/references.rst Indices and tables diff --git a/docs/build/html/_sources/index/CONTRIBUTING_COPY.rst.txt b/docs/build/html/_sources/index/CONTRIBUTING_COPY.rst.txt index 7d536d61..eaa92c5d 100644 --- a/docs/build/html/_sources/index/CONTRIBUTING_COPY.rst.txt +++ b/docs/build/html/_sources/index/CONTRIBUTING_COPY.rst.txt @@ -215,7 +215,21 @@ Documentation Style `napoleon `_, and `sphinx-design `_ extensions. -- **Further Details**: See `docs/source/conf.py` for more. +- **Further Details**: See our `conf.py `_ + for more. + +Documentation Layers +--------------------- +This project's documentation is organized across four distinct layers, each with a specific focus and level of detail: + +- **Docstrings**: Concise code description, with minimal usage examples and references to other layers (in 'See also'). + +- **Usage Principles**: Bird's-eye view with background and key principles, reflecting by selected code examples. + +- **Tutorial**: Close-up on public interface, as step-by-step guide on essential usage with medium detail. + +- **Tables**: Close-up on data or other tabular overviews, with detailed explanation of columns and critical values. + Building the Docs ----------------- diff --git a/docs/build/html/_sources/_index/badges.rst.txt b/docs/build/html/_sources/index/badges.rst.txt similarity index 100% rename from docs/build/html/_sources/_index/badges.rst.txt rename to docs/build/html/_sources/index/badges.rst.txt diff --git a/docs/build/html/_sources/_index/overview.rst.txt b/docs/build/html/_sources/index/overview.rst.txt similarity index 100% rename from docs/build/html/_sources/_index/overview.rst.txt rename to docs/build/html/_sources/index/overview.rst.txt diff --git a/docs/build/html/_sources/_index/tables.rst.txt b/docs/build/html/_sources/index/tables.rst.txt similarity index 62% rename from docs/build/html/_sources/_index/tables.rst.txt rename to docs/build/html/_sources/index/tables.rst.txt index a1488f89..69d3767c 100644 --- a/docs/build/html/_sources/_index/tables.rst.txt +++ b/docs/build/html/_sources/index/tables.rst.txt @@ -1,3 +1,12 @@ +.. 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 ====================== @@ -6,10 +15,13 @@ Tables :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: 10 10 10 + :widths: 8 8 8 * - Table - Description @@ -21,11 +33,24 @@ Overview Table - 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: 10 10 10 10 10 10 10 10 10 10 + :widths: 8 8 8 8 8 8 8 8 8 8 * - Level - Dataset @@ -45,7 +70,7 @@ Protein benchmark datasets - 184900 - PROSPERous - Prediction of caspase-3 cleavage site - - Song18 + - :ref:`Song18 ` - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) * - Amino acid - AA_FURIN @@ -55,7 +80,7 @@ Protein benchmark datasets - 58840 - PROSPERous - Prediction of furin cleavage site - - Song18 + - :ref:`Song18 ` - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) * - Amino acid - AA_LDR @@ -65,7 +90,7 @@ Protein benchmark datasets - 82779 - IDP-Seq2Seq - Prediction of long intrinsically disordered regions (LDR) - - Tang20 + - :ref:`Tang20 ` - 1 (disordered), 0 (ordered) * - Amino acid - AA_MMP2 @@ -75,7 +100,7 @@ Protein benchmark datasets - 310560 - PROSPERous - Prediction of Matrix metallopeptidase-2 (MMP2) cleavage site - - Song18 + - :ref:`Song18 ` - 1 (adjacent to cleavage site), 0 (not adjacent to cleavage site) * - Amino acid - AA_RNABIND @@ -85,7 +110,7 @@ Protein benchmark datasets - 48509 - GMKSVM-RU - Prediction of RNA-binding protein residues (RBP60 dataset) - - Yang21 + - :ref:`Yang21 ` - 1 (binding), 0 (non-binding) * - Amino acid - AA_SA @@ -95,7 +120,7 @@ Protein benchmark datasets - 84523 - PROSPERous - Prediction of solvent accessibility (SA) of residue (AA_CASPASE3 data set) - - Song18 + - :ref:`Song18 ` - 1 (exposed/accessible), 0 (buried/non-accessible) * - Sequence - SEQ_AMYLO @@ -105,7 +130,7 @@ Protein benchmark datasets - 903 - ReRF-Pred - Prediction of amyloidognenic regions - - Teng21 + - :ref:`Teng21 ` - 1 (amyloidogenic), 0 (non-amyloidogenic) * - Sequence - SEQ_CAPSID @@ -115,7 +140,7 @@ Protein benchmark datasets - 4071 - VIRALpro - Prediction of capdsid proteins - - Galiez16 + - :ref:`Galiez16 ` - 1 (capsid protein), 0 (non-capsid protein) * - Sequence - SEQ_DISULFIDE @@ -125,7 +150,7 @@ Protein benchmark datasets - 1650 - Dipro - Prediction of disulfide bridges in sequences - - Cheng06 + - :ref:`Cheng06 ` - 1 (sequence with SS bond), 0 (sequence without SS bond) * - Sequence - SEQ_LOCATION @@ -135,7 +160,7 @@ Protein benchmark datasets - 790 - nan - Prediction of subcellular location of protein (cytoplasm vs plasma membrane) - - Shen19 + - :ref:`Shen19 ` - 1 (protein in cytoplasm), 0 (protein in plasma membrane) * - Sequence - SEQ_SOLUBLE @@ -145,7 +170,7 @@ Protein benchmark datasets - 8704 - SOLpro - Prediction of soluble and insoluble proteins - - Magnan09 + - :ref:`Magnan09 ` - 1 (soluble), 0 (insoluble) * - Sequence - SEQ_TAIL @@ -155,7 +180,7 @@ Protein benchmark datasets - 4094 - VIRALpro - Prediction of tail proteins - - Galiez16 + - :ref:`Galiez16 ` - 1 (tail protein), 0 (non-tail protein) * - Domain - DOM_GSEC @@ -165,7 +190,7 @@ Protein benchmark datasets - 63 - nan - Prediction of gamma-secretase substrates - - Breimann23c + - :ref:`Breimann23c ` - 1 (substrate), 0 (non-substrate) * - Domain - DOM_GSEC_PU @@ -175,14 +200,18 @@ Protein benchmark datasets - 0 - nan - Prediction of gamma-secretase substrates (PU dataset) - - Breimann23c + - :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: 10 10 10 10 + :widths: 8 8 8 8 * - Dataset - Description @@ -191,24 +220,26 @@ Amino acid scale datasets * - scales - Amino acid scales (min-max normalized) - 586 - - Breimann23b + - :ref:`Breimann23b ` * - scales_raw - Amino acid scales (raw values) - 586 - - Kawashima08 + - :ref:`Kawashima08 ` * - scales_classification - Classification of scales (Aaontology) - 586 - - Breimann23b + - :ref:`Breimann23b ` * - scales_pc - Principal component (PC) compressed scales - 20 - - Breimann23a + - :ref:`Breimann23a ` * - top60 - Top 60 scale subsets - 60 - - Breimann23a + - :ref:`Breimann23a ` * - top60_eval - Evaluation of top 60 scale subsets - 60 - - Breimann23a + - :ref:`Breimann23a ` + + diff --git a/docs/build/html/_sources/index/tables_template.rst.txt b/docs/build/html/_sources/index/tables_template.rst.txt deleted file mode 100644 index 74c1cb45..00000000 --- a/docs/build/html/_sources/index/tables_template.rst.txt +++ /dev/null @@ -1,35 +0,0 @@ -.. 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. - -Tables -====================== - -.. contents:: - :local: - :depth: 1 - -Overview Table ------------------- -All tables from the AAanalysis documentation are given here in chronological order of the project history. - -.. list-table:: - :header-rows: 1 - :widths: 10 10 10 - - * - Table - - Description - - See also - * - Overview_benchmarks - - Protein benchmark datasets - - aa.load_dataset - * - Overview_scales - - Amino acid scale datasets - - aa.load_scales - -Protein benchmark datasets --------------------------- 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..d6a23856 --- /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 [Breimann23b]_. + 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..d07d3fb4 --- /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 ... [Breimann23c]_. + +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, [Breimann23c]_. + +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..00e9edbc --- /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 ... [Breimann23c]_. + +What is explainable AI? +----------------------- + diff --git a/docs/build/html/_sources/tables_template.rst.txt b/docs/build/html/_sources/tables_template.rst.txt new file mode 100644 index 00000000..40db31c9 --- /dev/null +++ b/docs/build/html/_sources/tables_template.rst.txt @@ -0,0 +1,43 @@ +.. 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/tutorials.rst.txt b/docs/build/html/_sources/tutorials.rst.txt index 291060aa..8121491d 100644 --- a/docs/build/html/_sources/tutorials.rst.txt +++ b/docs/build/html/_sources/tutorials.rst.txt @@ -4,9 +4,13 @@ Tutorials ========= +Quick start +----------- + + Data loading ------------ -`View data loading notebook <#path_to_notebook_for_load_data>`_ +`View data loading notebook <../tutorials/tutorial2_data_loader.ipynb>`_ Redundancy-reduction -------------------- diff --git a/docs/build/html/_sources/index/usage_principles.rst.txt b/docs/build/html/_sources/usage_principles.rst.txt similarity index 100% rename from docs/build/html/_sources/index/usage_principles.rst.txt rename to docs/build/html/_sources/usage_principles.rst.txt diff --git a/docs/build/html/_static/css/style.css b/docs/build/html/_static/css/style.css index b6f89b7b..fc197df9 100644 --- a/docs/build/html/_static/css/style.css +++ b/docs/build/html/_static/css/style.css @@ -1,77 +1,99 @@ +/* ------ General Styles ------ */ + /* Style for the version text in the sidebar */ .wy-side-nav-search>div.version { color: white; } -/* Responsive Table Styles */ -@media screen and (min-width: 767px) { - /* Makes the table cells wrap text */ - .wy-table-responsive table td { - white-space: normal; - } - - /* Makes the table overflow visible */ - .wy-table-responsive { - overflow: visible; - } -} - -/* Logo styling in the sidebar */ -.wy-side-nav-search .wy-dropdown>a img.logo, .wy-side-nav-search>a img.logo { - max-width: 80%; /* Sets the maximum width of the logo to 40% of its parent element */ - max-height: 60%; /* Adjust as needed */ -} - -/* Style for the top navigation bar */ -.wy-nav-top { - background: #343131; /* Sets the background color */ -} - /* Style for highlighted text */ .highlight { - background: #f7f7f7; /* Sets the background color */ + background: #f7f7f7; /* Sets the background color */ } /* Style for inline code */ .rst-content code { - background-color: #f5f5f5; /* Gray background */ - font-family: monospace; /* Monospace font */ - padding: 2px 2px; /* Padding around the text */ -} - -/* Style for the search input box in the sidebar */ -.wy-side-nav-search input[type=text] { - border-color: #666666; /* Sets the border color */ + background-color: #f5f5f5; + font-family: monospace; + padding: 2px 2px; } /* General link styling */ a { - color: #008bfb; /* Sets the link color */ + color: #008bfb; } /* Link styling on hover */ a:hover { - color: #008bfb; /* Sets the hover color */ + color: #008bfb; } /* Link styling for visited links */ a:visited { - color: #008bfb; /* Sets the visited link color */ + color: #008bfb; +} + +/* ------ Layout Styles ------ */ + +/* Override content width */ +.wy-nav-content-wrap { + max-width: 100%; + width: 100%; + padding-right: 0; } -/* Styling for specific dt elements in rst-content */ -html.writer-html4 .rst-content dl:not(.docutils)>dt, html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)>dt { - background: #008bfb11; - color: #0086f6; - border-top: 3px solid #008bfbaa; +/* Override the content area */ +.wy-nav-content { + max-width: 62%; + width: 62%; +} + +/* ------ Table Styles ------ */ + +/* Table Styling */ +.wy-table-responsive table { + table-layout: auto; /* This allows table to auto-size based on content */ + width: 100%; /* Full width */ } +/* Makes tables within .rst-content horizontally scrollable */ +.rst-content table { + display: block; + overflow-x: auto; + white-space: nowrap; +} + +/* ------ Logo Styles ------ */ + +/* Logo styling in the sidebar */ +.wy-side-nav-search .wy-dropdown>a img.logo, .wy-side-nav-search>a img.logo { + max-width: 80%; + max-height: 60%; +} + +/* ------ Top Bar Styles ------ */ + +/* Style for the top navigation bar */ +.wy-nav-top { + background: #343131; +} + +/* ------ Search Box Styles ------ */ + +/* Style for the search input box in the sidebar */ +.wy-side-nav-search input[type=text] { + border-color: #666666; +} + +/* ------ Version Menu Styles ------ */ + /* Style for the current version in the version menu */ .rst-versions .rst-current-version { - color: #d9d9d9; /* Sets the text color */ + color: #d9d9d9; } +/* ------ Vertical Menu Styles ------ */ + /* Style for vertical menu items */ .wy-menu-vertical a { - color: #d9d9d9; /* Sets the text color */ -} \ No newline at end of file + color: #d9d9d9; +} diff --git a/docs/build/html/api.html b/docs/build/html/api.html index a202b586..fa2b38bc 100644 --- a/docs/build/html/api.html +++ b/docs/build/html/api.html @@ -65,7 +65,7 @@

    OVERVIEW

    EXAMPLES

    @@ -105,7 +105,7 @@

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