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⚠️ ARCHIVED REPOSITORY: This repository has been archived and is no longer maintained. Please visit the current repository at https://github.com/ohsu-comp-bio/regulon-enrichment for the latest updates and active development.


Enrich

regulon-enrichment is a Python module used to predict the activity of regulatory proteins from RNAseq data.

regulon-enrichment submodules:

enricher.features

Load -omic datasets

enricher.regulon

Regulon utilities

Dependencies

regulon-enrichment requires:

- Python (>= 3.6)
- scikit-learn (>= 0.21.3)
- NumPy (>= 1.17.3)
- SciPy (>= 1.3.1)
- pandas (>= 0.25.3)
- tqdm (>= 4.38.0)
- dill (>= 0.3.1.1)

User installation


If you already have a working installation of numpy and scipy,
the easiest way to install regulon-enrichment is using ``conda``   ::

    conda install -c estabroj89 regulon-enrichment

or ``pip``::

    pip install regulon-enrichment==0.0.2b0

Overview

This method leverages pathway information and gene expression data to produce regulon-based protein activity scores. Our method tests for positional shifts in experimental-evidence supported networks consisting of transcription factors and their downstream signaling pathways when projected onto a rank-sorted gene-expression signature.

This regulon enrichment method utilizes pathway and molecular interactions and mechanisms available through Pathway Commons to accurately infer aberrant transcription factor activity from gene expression data.

Running regulon-enrichment

Invoking enrich from the command line

When installing the regulon-enrichment package, the set of scripts that make up to inteface to regulon-enrichment will automatically be placed as an executables in your path, so that you can refer to these without modifying your shell environment. For example, if you install regulon-enrichment using conda, then enrich will become available on the path, and can be run as:

enrich

Enrich parameters

Required parameters

cohort : which cohort to use; this information will be retained in the serialized Enrichment class

expr : which tab delimited expression matrix to use shape : [n_features, n_samples], units : TPM, RPKM

out_dir : output directory - directory serialized Enrichment object and enrichment.tsv will be saved to

Optional parameters

regulon : optional regulon containing weight interactions between regulator and downstream members of its regulon shape : [len(Target), ['Regulator','Target','MoA','likelihood']

regulon_size : number of downstream interactions required for a given regulator in order to calculate enrichment score default=15

sec_intx : path to pre-compiled serialized secondary interaction network, default=secondary_intx_regulon.pkl

scaler_type : scaler to normalized features/samples by: standard | robust | minmax | quant, default=robust

thresh_filter : Prior to normalization remove features that have a standard deviation per feature less than {thresh_filter}, default=0.1)

Computing regulon enrichment scores

To quantify the regulon enrichment for a given dataset, the command line script enrich is used.

Use --help argument to view options

enrich --help

Enrich requires three positional arguments: cohort,expr, out_dir

enrich cohort expr out_dir [regulon] [regulon_size] [sec_intx] [scaler_type] [thresh_filter]

It is recommended to run enrich with the default parameters.

enrich test tests/resources/test_expr.tsv test_enrichment_scores

The command above will generate enrichment scores for the unittest dataset test_expr.tsv and will generate and store the output under test_enrichment_scores/. In this directory test_enrichment_scores/, both the serialized Enrichment object test_enrichment.pkl and a tsv of the enrichment scores,test_regulon_enrichment.tsv will be found.

The enrichment.tsv file be shaped : [n_samples, n_regulators], where n_samples refers to the original number of samples provided in expr, while n_regulators will be determined based on the overlapping features present in the expr dataset and the regulon_size parameter.

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