View source: R/mappingPathwaysClusters.R
mappingPathwaysClusters | R Documentation |
Outputs a table with pathways and their respective clusters
mappingPathwaysClusters( pcxn, dePathways, clusteringFunction = NULL, edgeFDR = 0.05, correlationCutOff = 0.316, pathwayFDR = 0.05, topPathways = 200, plotOut = TRUE, subplot = TRUE, topClusters = 2, prefix = "", outDir = ".", saveNameCSV = NULL, weighted = FALSE )
pcxn |
pathways network (edge list of pathways) |
dePathways |
differential expressed pathways, obtained from *DifferentialPathwayAnalysis* |
clusteringFunction |
clustering algorithm |
edgeFDR |
FDR threshold for pathway-pathway adjusted p-values; filter edges with adjusted p-values less than given threshold |
correlationCutOff |
cut-off threshold for pathway-pathway correlation; filter pathways with correlation less than given threshold |
pathwayFDR |
FDR threshold for DE pathways adjusted p-values; filter pathways with adjusted p-values less than given threshold |
topPathways |
use only top x paths; if NULL, use all paths |
plotOut |
if TRUE, store graph plot in Figures directory of plots |
subplot |
if TRUE, store individual clusters plots and connected plots in Figures directory of plots |
topClusters |
plot figures for top x clusters |
prefix |
add prefix to plots |
outDir |
output directory |
saveNameCSV |
if not NULL, saves output as csv using save name |
weighted |
True if you wish to include correlation weights in clustering |
a list where the first item is a table with each row containing a pathway and its respective cluster. The second item is an igraph object.
data("miniTestsPanomiR") mappingPathwaysClusters(pcxn = miniTestsPanomiR$miniPCXN, dePathways = miniTestsPanomiR$miniDEP, topPathways = 200, outDir=".", plot = FALSE, subplot = FALSE, prefix='', clusteringFunction = "cluster_louvain", correlationCutOff = 0.1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.