get_pathway_ranking_metric: get_pathway_ranking_metric

In VCCRI/SPAGI: SPAGI: Identification of active signalling pathways by integrating gene expression and protein interaction network

Description

This function generates pathway activity score and number of downstream transcription factors of the active pathways for each cell/tissue type. It uses active pathway path and processed query data with a high expression threshold to generate the activity score. However, it uses only the active pathway path data to calculate the number of downstream transcription factors.

Usage

get_pathway_ranking_metric(active.pathway.path, processed.query.data,
  high.exp.th)

Arguments

active.pathway.path	A list of active pathway path data for each cell/tissue type as returned by the function 'identify_active_pathway_path'.
processed.query.data	A list with expressed query data where each sublist corresponds for each cell/tissue type as returned by the function 'preprocess_querydata'.
high.exp.th	A high expression threshold value for the processed query data. It is used to get active (i.e., highly expressed) molecule proportion for each pathway path.

Details

This function generates pathway activity score and number of downstream transcription factors of the active pathways for each cell/tissue type. It uses active pathway path and processed query data with a high expression threshold to generate the activity score. However, it uses only the active pathway path data to calculate the number of downstream transcription factors.

Value

This function returns a list of sublist with pathway activity score and the number of downstream transcription factors for each cell/tissue type.

Examples

## Here we will use "pathway.path" as background data from the SPAGI repository.
## Also we will use "ROR1.data" as query RNA-seq gene expression data. This data is for ocular lens epithelial cell differentiated from human pluripotent stem cells.
## These data sets are loaded automatically with the package.

## Pre-process the query data (ROR1.data), the data has already been made in CPM and log2 normalized format. Also we have already made the replicate names same for the data.
ROR1.processed.data<-preprocess_querydata(cell.tissue.data = ROR1.data, exp.cutoff.th = 1.8)
## Identify active pathway paths of the processed query data
ROR1.active.pathway<-identify_active_pathway_path(pathway.path = pathway.path, processed.query.data = ROR1.processed.data)
## Get active pathway ranking metric (i.e., activity score and number of downstream transcription factors)
ROR1.active.pathway.ranking.metric<-get_pathway_ranking_metric(active.pathway.path = ROR1.active.pathway, processed.query.data = ROR1.processed.data, high.exp.th = 7)
head(ROR1.active.pathway.ranking.metric$activity.score$ROR1_LEC)
head(ROR1.active.pathway.ranking.metric$downstream.tf.count$ROR1_LEC)

VCCRI/SPAGI documentation built on May 23, 2019, 1:08 p.m.