Merge pull request #6 from MannLabs/development

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MannLabs · Mar 1, 2023 · 8855bf9 · 8855bf9
2 parents 3cc02f7 + 082a42f
commit 8855bf9
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diff --git a/.github/workflows/publish_and_release.yml b/.github/workflows/publish_and_release.yml
@@ -1,6 +1,6 @@
 on:
-  # push:
-  #   branches: [ main ]
+  push:
+    branches: [ main ]
   workflow_dispatch:
 
 

diff --git a/README.md b/README.md
@@ -19,6 +19,7 @@ You can process DIA and DDA data analyzed by [AlphaPept](https://github.com/Mann
   - [About](#about)
   - [Installation](#installation)
     - [One-click GUI](#one-click-gui)
+    - [Pip](#pip)
     - [Developer](#developer)
   - [Running directLFQ](#running-directlfq)
     - [GUI](#gui)
@@ -67,16 +68,22 @@ The GUI of directlfq is a completely stand-alone tool that requires no knowledge
 
 Older releases remain available on the [release page](https://github.com/MannLabs/directlfq/releases), but no backwards compatibility is guaranteed.
 
-<!---
+-
 ### Pip
 
-directlfq can be installed in an existing Python 3.8 environment with a single `bash` command. *This `bash` command can also be run directly from within a Jupyter notebook by prepending it with a `!`*:
+directLFQ can be installed in an existing Python 3.8 environment with a single `bash` command.
 
 ```bash
 pip install directlfq
 ```
 
-Installing directlfq like this avoids conflicts when integrating it in other tools, as this does not enforce strict versioning of dependencies. However, if new versions of dependencies are released, they are not guaranteed to be fully compatible with directlfq. While this should only occur in rare cases where dependencies are not backwards compatible, you can always force directlfq to use dependancy versions which are known to be compatible with:
+This installs the core directLFQ without graphical user interface (GUI). If you want to install with additional dependencies for GUI support, you can do this with:
+
+```bash
+pip install "directlfq[gui]"
+```
+
+For installation with stable dependencies, use:
 
 ```bash
 pip install "directlfq[stable]"
@@ -89,7 +96,7 @@ For those who are really adventurous, it is also possible to directly install an
 ```bash
 pip install "git+https://github.com/MannLabs/directlfq.git@development#egg=directlfq[stable,development-stable]"
 ```
--->
+
 ### Developer
 
 directlfq can also be installed in editable (i.e. developer) mode with a few `bash` commands. This allows to fully customize the software and even modify the source code to your specific needs. When an editable Python package is installed, its source code is stored in a transparent location of your choice. While optional, it is advised to first (create and) navigate to e.g. a general software folder:

diff --git a/directlfq/__init__.py b/directlfq/__init__.py
@@ -2,7 +2,7 @@
 
 
 __project__ = "directlfq"
-__version__ = "0.2.7"
+__version__ = "0.2.8"
 __license__ = "Apache"
 __description__ = "An open-source Python package of the AlphaPept ecosystem"
 __author__ = "Mann Labs"

diff --git a/directlfq/_modidx.py b/directlfq/_modidx.py
@@ -642,9 +642,6 @@
                                                                                    'directlfq/utils.py'),
                                  'directlfq.utils.parse_channel_from_peptide_column': ( 'utils.html#parse_channel_from_peptide_column',
                                                                                         'directlfq/utils.py'),
-                                 'directlfq.utils.plot_relative_to_median_fcs': ( 'utils.html#plot_relative_to_median_fcs',
-                                                                                  'directlfq/utils.py'),
-                                 'directlfq.utils.plot_withincond_fcs': ('utils.html#plot_withincond_fcs', 'directlfq/utils.py'),
                                  'directlfq.utils.prepare_loaded_tables': ('utils.html#prepare_loaded_tables', 'directlfq/utils.py'),
                                  'directlfq.utils.process_with_dask': ('utils.html#process_with_dask', 'directlfq/utils.py'),
                                  'directlfq.utils.reformat_and_save_input_file': ( 'utils.html#reformat_and_save_input_file',
@@ -726,4 +723,8 @@
                                           'directlfq.visualizations.MultiOrganismMultiMethodBoxPlot._add_expected_fold_changes': ( 'visualizations.html#multiorganismmultimethodboxplot._add_expected_fold_changes',
                                                                                                                                    'directlfq/visualizations.py'),
                                           'directlfq.visualizations.MultiOrganismMultiMethodBoxPlot.plot_boxplot': ( 'visualizations.html#multiorganismmultimethodboxplot.plot_boxplot',
-                                                                                                                     'directlfq/visualizations.py')}}}
+                                                                                                                     'directlfq/visualizations.py'),
+                                          'directlfq.visualizations.plot_relative_to_median_fcs': ( 'visualizations.html#plot_relative_to_median_fcs',
+                                                                                                    'directlfq/visualizations.py'),
+                                          'directlfq.visualizations.plot_withincond_fcs': ( 'visualizations.html#plot_withincond_fcs',
+                                                                                            'directlfq/visualizations.py')}}}
diff --git a/directlfq/utils.py b/directlfq/utils.py
@@ -27,7 +27,7 @@
            'get_original_file_from_aq_reformat', 'import_config_dict', 'load_samplemap', 'prepare_loaded_tables',
            'LongTableReformater', 'AcquisitionTableHandler', 'AcquisitionTableInfo', 'AcquisitionTableHeaders',
            'AcquisitionTableOutputPaths', 'AcquisitionTableReformater', 'AcquisitionTableHeaderFilter',
-           'merge_acquisition_df_parameter_df', 'plot_withincond_fcs', 'plot_relative_to_median_fcs']
+           'merge_acquisition_df_parameter_df']
 
 # %% ../nbdev_nbs/04_utils.ipynb 2
 import os
@@ -1190,44 +1190,3 @@ def merge_acquisition_df_parameter_df(acquisition_df, parameter_df, groupby_merg
         merged_df = merged_df.groupby('ion').max().reset_index()
     merged_df = merged_df.dropna(axis=1, how='all')
     return merged_df
-
-# %% ../nbdev_nbs/04_utils.ipynb 54
-import matplotlib.pyplot as plt
-import itertools
-
-def plot_withincond_fcs(normed_intensity_df, cut_extremes = True):
-    """takes a normalized intensity dataframe and plots the fold change distribution between all samples. Column = sample, row = ion"""
-
-    samplecombs = list(itertools.combinations(normed_intensity_df.columns, 2))
-
-    for spair in samplecombs:#compare all pairs of samples
-        s1 = spair[0]
-        s2 = spair[1]
-        diff_fcs = normed_intensity_df[s1].to_numpy() - normed_intensity_df[s2].to_numpy() #calculate fold changes by subtracting log2 intensities of both samples
-
-        if cut_extremes:
-            cutoff = max(abs(np.nanquantile(diff_fcs,0.025)), abs(np.nanquantile(diff_fcs, 0.975))) #determine 2.5% - 97.5% interval, i.e. remove extremes
-            range = (-cutoff, cutoff)
-        else:
-            range = None
-        plt.hist(diff_fcs,80,density=True, histtype='step',range=range) #set the cutoffs to focus the visualization
-        plt.xlabel("log2 peptide fcs")
-
-    plt.show()
-
-# %% ../nbdev_nbs/04_utils.ipynb 55
-import matplotlib.pyplot as plt
-import itertools
-
-def plot_relative_to_median_fcs(normed_intensity_df):
-
-    median_intensities = normed_intensity_df.median(axis=1)
-    median_intensities = median_intensities.to_numpy()
-
-    diff_fcs = []
-    for col in normed_intensity_df.columns:
-        median_fcs = normed_intensity_df[col].to_numpy() - median_intensities
-        diff_fcs.append(np.nanmedian(median_fcs))
-    plt.hist(diff_fcs,80,density=True, histtype='step')
-    plt.xlabel("log2 peptide fcs")
-    plt.show()
diff --git a/directlfq/visualizations.py b/directlfq/visualizations.py
@@ -2,7 +2,8 @@
 
 # %% auto 0
 __all__ = ['a4_dims', 'a4_width_no_margin', 'AlphaPeptColorMap', 'CmapRegistrator', 'IonTraceCompararisonPlotter',
-           'IonTraceCompararisonPlotterNoDirectLFQTrace', 'IonTraceVisualizer', 'MultiOrganismMultiMethodBoxPlot']
+           'IonTraceCompararisonPlotterNoDirectLFQTrace', 'IonTraceVisualizer', 'MultiOrganismMultiMethodBoxPlot',
+           'plot_withincond_fcs', 'plot_relative_to_median_fcs']
 
 # %% ../nbdev_nbs/05_visualizations.ipynb 1
 a4_dims = (11.7, 8.27)
@@ -180,3 +181,44 @@ def _add_expected_fold_changes(self):
                 self.ax.axhline(fc, color = color)
 
 
+
+# %% ../nbdev_nbs/05_visualizations.ipynb 6
+import matplotlib.pyplot as plt
+import itertools
+
+def plot_withincond_fcs(normed_intensity_df, cut_extremes = True):
+    """takes a normalized intensity dataframe and plots the fold change distribution between all samples. Column = sample, row = ion"""
+
+    samplecombs = list(itertools.combinations(normed_intensity_df.columns, 2))
+
+    for spair in samplecombs:#compare all pairs of samples
+        s1 = spair[0]
+        s2 = spair[1]
+        diff_fcs = normed_intensity_df[s1].to_numpy() - normed_intensity_df[s2].to_numpy() #calculate fold changes by subtracting log2 intensities of both samples
+
+        if cut_extremes:
+            cutoff = max(abs(np.nanquantile(diff_fcs,0.025)), abs(np.nanquantile(diff_fcs, 0.975))) #determine 2.5% - 97.5% interval, i.e. remove extremes
+            range = (-cutoff, cutoff)
+        else:
+            range = None
+        plt.hist(diff_fcs,80,density=True, histtype='step',range=range) #set the cutoffs to focus the visualization
+        plt.xlabel("log2 peptide fcs")
+
+    plt.show()
+
+# %% ../nbdev_nbs/05_visualizations.ipynb 7
+import matplotlib.pyplot as plt
+import itertools
+
+def plot_relative_to_median_fcs(normed_intensity_df):
+
+    median_intensities = normed_intensity_df.median(axis=1)
+    median_intensities = median_intensities.to_numpy()
+
+    diff_fcs = []
+    for col in normed_intensity_df.columns:
+        median_fcs = normed_intensity_df[col].to_numpy() - median_intensities
+        diff_fcs.append(np.nanmedian(median_fcs))
+    plt.hist(diff_fcs,80,density=True, histtype='step')
+    plt.xlabel("log2 peptide fcs")
+    plt.show()
diff --git a/misc/bumpversion.cfg b/misc/bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.7
+current_version = 0.2.8
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

diff --git a/nbdev_nbs/01_lfq_manager.ipynb b/nbdev_nbs/01_lfq_manager.ipynb
@@ -94,15 +94,6 @@
    "display_name": "alphatemplate",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.8.13"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "6323a234a298f1b06d100e5b842ce6ca7904d43140794ca9cb8f0b055bce87c6"
-   }
   }
  },
  "nbformat": 4,