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Analyzing Data

The Analyze class contains functions which can be used to identify cohorts in datasets, perform differential expression and pathway analysis, and execute meta-analysis workflows. Args: token (str): Authentication token from polly Usage: from polly.analyze import Analyze

analysis = Analyze(token)

identify_cohorts(repo_key, dataset_id)

This function is used to get the cohorts that can be created from samples in a GEO dataset. Please note: Currently only Bulk RNASeq datasets from GEO source are supported. If results are generated for other datatypes or datasource, they may be inaccurate. If you want to use this functionality for any other data type and source, please reach out to polly.support@elucidata.io

Parameters:

Name Type Description Default
repo_key int / str

repo_id or repo_name in str or int format

 required
dataset_id str

dataset_id of the GEO dataset. eg. "GSE132270_GPL11154_raw"

 required

Returns: Dataframe showing values of samples across factors/cohorts.

run_meta_analysis(repo_key, workspace_id, analysis_name, design_formulas, samples_to_remove=[])

Use this function to execute the Polly DIY Meta-Analysis Pipeline. Only the 'geo_transcriptomics_omixatlas' omixatlas is supported currently. Args: repo_key (int/str): repo_id or repo_name in str or int format workspace_id (int): the workspace in which the datasets and results should be stored analysis_name (str): name of your analysis, eg. "MA_BRCA_run1". The reports, datasets, and results will be stored in a folder with this name. design_formulas (dict): key:value pair of atleast 2 dataset ids and a list of design formulas. eg. dataset_id:[design formula control, design formula perturbation] samples_to_remove (list, optional): List of samples to omit, if any. Defaults to [].

Examples

# Install polly python
!sudo pip3 install polly-python --quiet

# Import libraries
from polly import analyze
AUTH_TOKEN=(os.environ['POLLY_REFRESH_TOKEN'])
analysis = analyze.Analyze(AUTH_TOKEN)

Identify Cohorts

analysis.identify_cohorts("geo_transcriptomics_omixatlas","GSE117482_GPL17021_raw")
Factors based on which cohorts can be created for this dataset: ['genotype', 'gender', 'age__at_the_time_of_sacrifice_', 'treatment', 'experiment', 'curated_genetic_modification_type']

genotype gender age__at_the_time_of_sacrifice_ treatment experiment curated_genetic_modification_type
geo_accession
GSM3301731 ko sex: female 4 weeks nd exp1 [knockout]
GSM3301732 ko sex: female 4 weeks nd exp1 [knockout]
GSM3301736 control sex: male 4 weeks nd exp1 [wildtype]
GSM3301737 control sex: male 4 weeks nd exp1 [wildtype]
GSM3301739 control sex: female 4 weeks nd exp1 [wildtype]
GSM3301742 ko sex: female 21 weeks nd exp2 [knockout]
GSM3301744 ko sex: female 21 weeks nd exp2 [knockout]
GSM3301749 control sex: female 21 weeks hfd exp2 [wildtype]
GSM3301750 control sex: female 21 weeks nd exp2 [wildtype]
GSM3301753 control sex: female 21 weeks hfd exp2 [wildtype]
GSM3301754 control sex: female 21 weeks nd exp2 [wildtype]
GSM3301757 ko sex: female 21 weeks hfd exp2 [knockout]
GSM3301758 ko sex: female 21 weeks hfd exp2 [knockout]
GSM3301759 ko sex: female 21 weeks hfd exp2 [knockout]
GSM3301760 ko sex: female 21 weeks hfd exp2 [knockout]
GSM3301761 ko sex: female 21 weeks nd exp2 [knockout]
GSM3301727 control sex: male 4 weeks nd exp1 [wildtype]
GSM3301728 ko sex: male 4 weeks nd exp1 [knockout]
GSM3301729 ko sex: female 4 weeks nd exp1 [knockout]
GSM3301730 control sex: female 4 weeks nd exp1 [wildtype]
GSM3301733 ko sex: female 4 weeks nd exp1 [knockout]
GSM3301734 ko sex: female 4 weeks nd exp1 [knockout]
GSM3301735 control sex: female 4 weeks nd exp1 [wildtype]
GSM3301738 control sex: male 4 weeks nd exp1 [wildtype]
GSM3301740 control sex: female 4 weeks nd exp1 [wildtype]
GSM3301741 control sex: female 4 weeks nd exp1 [wildtype]
GSM3301743 ko sex: female 21 weeks nd exp2 [knockout]
GSM3301745 ko sex: female 21 weeks nd exp2 [knockout]
GSM3301746 control sex: female 21 weeks hfd exp2 [wildtype]
GSM3301747 control sex: female 21 weeks hfd exp2 [wildtype]
GSM3301748 control sex: female 21 weeks hfd exp2 [wildtype]
GSM3301751 control sex: female 21 weeks nd exp2 [wildtype]
GSM3301752 control sex: female 21 weeks nd exp2 [wildtype]
GSM3301755 control sex: female 21 weeks nd exp2 [wildtype]
GSM3301756 ko sex: female 21 weeks hfd exp2 [knockout] 
# If you want to plot a sunburst on specific columns of interest, please use the following code:
import plotly.express as px
metadata = identify_cohorts(repo_key, dataset_id)
fig = px.sunburst(metadata, path=['column_1','column_2','column_n'])
fig.show()

Run Meta-Analysis

repo = "bulkrnaseq_staging_oa"
datasets = ['GSE144269_GPL24676_raw','GSE114564_GPL11154_raw','GSE77314_GPL9052_raw']
designformula = {'GSE144269_GPL24676_raw' : 
                 [{'tumor_non_tumor':'non-tumor'}, {'tumor_non_tumor':'tumor'}],
                'GSE114564_GPL11154_raw' : 
                [{'curated_disease':'[Normal]'}, {'curated_disease':'[Carcinoma, Hepatocellular]'}],
                'GSE77314_GPL9052_raw' : 
                [{'curated_control':'0'}, {'curated_control':'1'}]
                }
ws_id= 14164
analysis_name = 'HCC_test1'

analysis.run_meta_analysis(repo, ws_id, analysis_name, designformula)

from polly import jobs
job = jobs.jobs()
job.job_status('14164','cb4074a13e464f3282310be2f5c60845')
Job ID Job Name Job State
0 cb4074a13e464f3282310be2f5c60845 Polly DIY pipeline: Meta Analysis SUCCEEDED 
job.job_status('14164','47ab24a74c644a6089d13de828106b94')
Job ID Job Name Job State
0 553b1df3fcff41c4a91b15ecbb582633 Polly DIY pipeline: Meta Analysis SUCCEEDED 
job.job_logs('14164','cb4074a13e464f3282310be2f5c60845')

A new version of Polly CLI is available. To update, execute the command npm update -g @elucidatainc/pollycli
Success: Logged in to Polly as shreya.raghavendra@elucidata.io
================================== POLLY LOGIN SUCCESS =======================================
“=============================== GIT CLONING POLLY-PYTHON ===================================”
Cloning into 'polly-python-code'...
“================================== GIT CLONING SUCCESS =======================================”
A new version of Polly CLI is available. To update, execute the command npm update -g @elucidatainc/pollycli
polly://HCC_test1_cohorts.csv
./cohort.csv
Success: Download complete
==================================== EXECUTING JOBS ==========================================
downloading data file:GSE77314_GPL9052_raw.gct: 100%|██████████| 29.4M/29.4M [00:00<00:00, 69.9MiB/s]
downloading data file:GSE144269_GPL24676_raw.gct: 100%|██████████| 34.3M/34.3M [00:01<00:00, 33.7MiB/s]
downloading data file:GSE114564_GPL11154_raw.gct: 100%|██████████| 58.7M/58.7M [00:01<00:00, 32.8MiB/s]
[1] "reading read_design_formula_csv "
[1] "done reading read_design_formula_csv "
[1] "process_gct_files started ....."
[1] "processing GCT files..."
parsing as GCT v1.3
datasets/GSE114564_GPL11154_raw.gct 62548 rows, 106 cols, 8 row descriptors, 40 col descriptors
parsing as GCT v1.3
datasets/GSE144269_GPL24676_raw.gct 29565 rows, 140 cols, 1 row descriptors, 42 col descriptors
parsing as GCT v1.3
datasets/GSE77314_GPL9052_raw.gct 35831 rows, 100 cols, 2 row descriptors, 42 col descriptors
================================= EXECUTING JOBS SUCCESS =====================================
==================================== SYNC RESULTS TO POLLY - STARTING ==========================================
A new version of Polly CLI is available. To update, execute the command npm update -g @elucidatainc/pollycli
Success: Sync complete
A new version of Polly CLI is available. To update, execute the command npm update -g @elucidatainc/pollycli
Success: Sync complete
================================= SYNC RESULTS TO POLLY - SUCCESS =====================================

Tutorial Notebooks

Identifying Cohorts for Multi-Factor Experiments From data findability, identifying cohorts, to running Meta-analysis