Make groups of genes using expression profile

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Description

Make groups of genes using expression profile

Usage

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degPatterns(ma, metadata, minc = 15, summarize = "group", time = "time",
  col = "condition", reduce = FALSE, cutoff = 0.7, scale = TRUE,
  plot = TRUE, fixy = NULL)

Arguments

ma

log2 normalized count matrix

metadata

data frame with sample information. Rownames should match ma column names row number should be the same length than p-values vector.

minc

integer minimum number of genes in a group that will be return

summarize

character column name in metadata that will be used to gorup replicates. For instance, a merge between summarize and time parameters: control_point0 ... etc

time

character column name in metadata that will be used as variable that changes, normally a time variable.

col

character column name in metadata to separate samples. Normally control/mutant

reduce

boolean reduce number of clusters using correlation values between them.

cutoff

integer threshold for correlation expression to merge clusters (0 - 1)

scale

boolean scale the ma values by row

plot

boolean plot the clusters found

fixy

vector integers used as ylim in plot

Details

It would be used diana function to detect a value to cut the expression based clustering at certain height. It can work with one or more groups with 2 or more several time points. The different patterns can be merged to get similar ones into only one pattern. The expression correlation of the patterns will be used to decide whether some need to be merged or not.

Value

list wiht two items. df is a data.frame with two columns. The first one with genes, the second with the clusters they belong. pass_to_plot is a vector of the clusters that pass the minc cutoff.

Examples

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data(humanSexDEedgeR)
ma <- humanSexDEedgeR$counts[1:100,]
des <- data.frame(row.names=colnames(ma), 
group=as.factor(humanSexDEedgeR$samples$group))
res <- degPatterns(ma, des, time="group", col=NULL)

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