Make groups of genes using expression profile
degPatterns(ma, metadata, minc = 15, summarize = "merge", time = "time", col = NULL, concensusCluster = TRUE, reduce = FALSE, cutoff = 0.7, scale = TRUE, plot = TRUE, fixy = NULL)
ma | log2 normalized count matrix |
---|---|
metadata | data frame with sample information. Rownames
should match |
minc | integer minimum number of genes in a group that will be return |
summarize | character column name in metadata that will be used to group
replicates. If the column doesn't exist it'll merge the |
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 |
concensusCluster | Indicates whether using ConsensusClusterPlus
or |
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 |
plot | boolean plot the clusters found |
fixy | vector integers used as ylim in plot |
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_plotis a vector of the clusters that pass the
minc` cutoff.
It would be used cluster::diana()
function
to detect a value to cut the expression based clustering
at certain height or ConsensusClusterPlus.
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.
data(humanGender) library(SummarizedExperiment) ma <- assays(humanGender)[[1]][1:100,] des <- colData(humanGender) res <- degPatterns(ma, des, time="group")#>#>