skeleton for fcs signal prediction

Author: danbryce

Diff for: .gitmodules

@@ -1,3 +1,8 @@
 [submodule "app/precomputed-data-table-app/xplan-od-growth-analysis"]
 	path = app/precomputed-data-table-app/xplan-od-growth-analysis
 	url = https://gitlab.sd2e.org/rpg/xplan-od-growth-analysis.git
+
+
+[submodule "app/precomputed-data-table-app/fcs_signal_prediction"]
+	path = app/precomputed-data-table-app/fcs_signal_prediction
+	url = https://gitlab.sd2e.org/dbryce/fcs_signal_prediction

Diff for: app/precomputed-data-table-app/fcs_signal_prediction/setup.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+
+# pylint: skip-file
+
+from setuptools import setup, find_packages
+
+install_requires = [
+
+]
+
+setup(
+    name="fcs_signal_prediction",
+    version="0.1",
+    description="Python module to predict the output signal of a strain based upon its FCS data.",
+    url="https://gitlab.sd2e.org/dbryce/fcs_signal_prediction",
+    author="Daniel Bryce",
+    author_email="[email protected]",
+    license="MIT",
+    packages=find_packages('src'),
+    package_dir={'': 'src'},
+    install_requires=install_requires,
+    zip_safe=False,
+    include_package_data=True
+)

Diff for: app/precomputed-data-table-app/fcs_signal_prediction/src/fcs_signal_prediction/main.py

@@ -0,0 +1,9 @@
+
+from fcs_signal_prediction.predict import predict
+
+def main():
+    predict(None, None)
+
+
+if __name__ == '__main__':
+    main()

Diff for: app/precomputed-data-table-app/fcs_signal_prediction/src/fcs_signal_prediction/predict.py

@@ -0,0 +1,5 @@
+from pandas import DataFrame
+
+
+def predict_signal(df: DataFrame, experiment_identifier: str) -> DataFrame:
+    pass

Diff for: app/precomputed-data-table-app/src/run_fcs_signal_analysis.py

@@ -0,0 +1,106 @@
+"""
+run xplan_od_growth_analysis on plate reader data frames produced by Data Converge;
+
+example command:
+python run_od_growth_analysis.py "YeastSTATES-CRISPR-Short-Duration-Time-Series-20191208"
+
+:authors: Robert C. Moseley ([email protected])
+"""
+
+import pandas as pd
+import argparse
+import os
+from xplan_od_growth_analysis import analysis_frame_api
+
+
+def grab_pr_dataframe(exp_ref, er_dir):
+
+    pr_file_name = '__'.join([exp_ref, 'platereader.csv'])
+    platereader_df = pd.read_csv(os.path.join(er_dir, pr_file_name))
+
+    return platereader_df
+
+
+def grab_meta_dataframe(exp_ref, er_dir):
+
+    meta_file_name = '__'.join([exp_ref, 'fc_meta.csv'])
+    meta_df = pd.read_csv(os.path.join(er_dir, meta_file_name))
+
+    return meta_df
+
+
+def growth_analysis(platereader_df, exp_ref, out_dir):
+
+    od_analysis_df = analysis_frame_api.augment_dataframe(df=platereader_df, experiment_identifier=exp_ref)
+
+    return od_analysis_df
+
+
+def rows_to_replicate_groups(data_df, m_type):
+
+    if m_type == 'od':
+        # drop duplicates and induction samples
+        # *** This will be replaced when replicate group column is added
+        dup_cols = ['doubling_time', 'n0', 'inducer_type', 'well', 'experiment_id']
+        data_drop_df = data_df.drop_duplicates(subset=dup_cols, keep='first')
+        data_drop_df.dropna(subset=['inducer_type', 'inducer_concentration',
+                                              'inducer_concentration_unit'], inplace=True)
+        # drop sample-specific columns
+        drop_cols = ['_id', 'sample_id', 'replicate', 'timepoint', 'timepoint_unit', 'container_id',
+                     'aliquot_id', 'fluor_gain_0.16', 'fluor_gain_0.16/od']
+        data_drop_df.drop(drop_cols, axis=1, inplace=True)
+        # ***
+        return data_drop_df
+
+    elif m_type == 'fc':
+        # *** This will be replaced when replicate group column is added
+        # drop duplicates
+        dup_cols = ['well_id', 'experiment_id']
+        data_drop_df = data_df.drop_duplicates(subset=dup_cols, keep='first')
+        # drop sample-specific columns
+        drop_cols = ['_id', 'sample_id', 'replicate', 'timepoint', 'timepoint_unit', 'TX_plate_name']
+        data_drop_df.drop(drop_cols, axis=1, inplace=True)
+        # ***
+        return data_drop_df
+
+# happen outside of this module *****
+# def merge_growth_fc(growth_df, fc_meta_df):
+#
+#     growth_fc_df = fc_meta_df.merge(growth_df, )
+
+
+def run_od_analysis(exp_ref, exp_ref_dir, conf_dict, out_dir):
+
+    pr_df = grab_pr_dataframe(exp_ref, exp_ref_dir)
+    od_analysis_initial_df = growth_analysis(pr_df, exp_ref, out_dir)
+    # od_analysis_initial_df.to_csv(os.path.join(out_dir, 'pdt_{}__od_growth_analysis.csv'.format(exp_ref)))
+
+    # make df rows = replicate groups
+    rg_od_analysis_df = rows_to_replicate_groups(od_analysis_initial_df, 'od')
+
+    # move outside of this module *****
+    # if conf_dict['fc_raw_log10']:
+    #     fc_meta_df = grab_meta_dataframe(exp_ref, exp_ref_dir)
+    #     rg_fc_meta_df = rows_to_replicate_groups(fc_meta_df, 'fc')
+    #
+    # elif not conf_dict['fc_raw_log10']:
+    #     pass
+    return rg_od_analysis_df
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('experiment_ref', help='experimental reference from data science table')
+    parser.add_argument('exp_ref_dir', help='path to experimental reference directory')
+    parser.add_argument('data_confirm_dict', help='dictionary containing information on available data'
+                                                  ' in experimental reference')
+    parser.add_argument("output_dir", help="directory where to write the output files")
+    # parser.add_argument("output_dir", help="directory where to write the output files")
+    args = parser.parse_args()
+    arg_exp_ref = args.experiment_ref
+    arg_exp_ref_dir = args.exp_ref_dir
+    arg_conf_dict = args.data_confirm_dict
+    arg_out_dir = args.output_dir
+
+    run_od_analysis(arg_exp_ref, arg_exp_ref_dir, arg_conf_dict, arg_out_dir)