EPCY: Evaluation of Predictive CapabilitY

EPCY is a method used to rank genes (features) according to their potential as predictive (bio)markers, using quantitative data (like gene expression).

Installation

Using PyPI

pip install epcy
epcy -h

Overview

Similarly to Differential Expression analyses, EPCY take as input two tabulated files:

• a table which describes the comparative design of the experience

• a matrix which contains quantitative data for each gene (feature) and sample

Using these two input files, EPCY will evaluate the predictive capacity of each gene individually and return predictive scores, along with their confidence intervals.

To guarantee the reliability of predictive scores, EPCY uses a leave-one-out cross validation to train multiple Kernel Density Estimation (KDE) classifiers and evaluate their performances on unseen samples (see method for more details).

Background

EPCY is a product of the Leucegene project and has been developed and tested specifically to analyse RNA-seq data of acute myeloid leukemia (AML) patients. However, the method implemented in EPCY is generic and should work on different quantitative data.

Citing

While we are finalizing work on the official paper, more details can be found in a poster presented at ISMB ECCB 2019:

Audemard E, Sauvé L and Lemieux S. EPCY: Evaluation of Predictive CapabilitY for ranking biomarker gene candidates [version 1; not peer reviewed]. F1000Research 2019, 8(ISCB Comm J):1349(poster)

Documentation index

• First steps with EPCY
  • Input files
  • Download input files
  • Run your first EPCY analysis
  • Results
  • Quality control
  • Plot a KDE trained on gene expression
  • Reproducibility
  • Some details on the design table
• How EPCY works
  • Visual description of the method implemented in EPCY
• Details of predictive capability columns
  • Predictive scores
  • Statistical test
  • Normal distribution
  • Missing values
• Working on RNA quantification
  • bulk RNA
  • Kallisto quantification
  • Single-cell
• How to explore EPCY output to select best candidates
  • Volcano plot
  • Identify a threshold
  • Using empirical False Positive Rate
• Working on small dataset