fig.dendrogram: Dendrogram for clustering p-values

In LXQin/PRECISION.seq: Comparison of Depth Normalization Methods

Description

Function for clustering normalization methods based on the p-values pattern calculated from the same dataset.

Usage

fig.dendrogram(DEA.pval.list, title, subset = NULL)

Arguments

DEA.pval.list	A list of p-values from differential expression analysis results with the element names to be the normalization methods
title	optional Figure title
subset	optional Vector of a subset of markers. If given, the dendrogram analysis will be limited to the given subset of markers. Leave NULL if all markers should be considered.

Value

Figure of dendrogram

Examples

test.norm <- pip.norm(raw=data.test, groups=data.group, norm.method = "all")
test.DE <- list(
TMM = DE.voom(RC=test.norm$TMM$dat.normed, groups = data.group),
TC = DE.voom(RC=test.norm$TC$dat.normed, groups = data.group),
UQ = DE.voom(RC=test.norm$UQ$dat.normed, groups = data.group),
med = DE.voom(RC=test.norm$med$dat.normed, groups = data.group),
DESeq = DE.voom(RC=test.norm$DESeq$dat.normed, groups = data.group),
PoissonSeq = DE.voom(RC=test.norm$PoissonSeq$dat.normed, groups = data.group),
QN = DE.voom(RC=test.norm$QN$dat.normed, groups = data.group),
RUVg = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVg$adjust.factor),
RUVs = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVs$adjust.factor),
RUVr = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVr$adjust.factor),
SVA = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$SVA$adjust.factor),
noNorm = DE.voom(RC=data.test, groups = data.group))
test.DE.pval <- lapply(1:12, function(x) test.DE[[x]]$p.val)
names(test.DE.pval) <- names(test.DE)

fig.dendrogram(DEA.pval.list = test.DE.pval, title = "Example of dendrogram")

LXQin/PRECISION.seq documentation built on Dec. 18, 2021, 3:41 a.m.