timecourseTable {TCseq} | R Documentation |
This fuction constructs a time course table of which rows corrsponding to genomic features and columns the timepoint. values can be mean normalized read counts or log2-fold changes compared to the first timepoint. The time course table is used for clustering analysis.
timecourseTable(object, value = "expression", lib.norm = TRUE, norm.method = "rpkm", subset = NULL, filter = FALSE, pvalue = "fdr", pvalue.threshold = 0.05, abs.fold = 2, direction = "both", ...)
object |
a |
value |
character string, either ' |
lib.norm |
logical indicating whether or not use effective
library size (see 'Details' in |
norm.method |
character string specifying the normalization
method if |
subset |
optinal character vector giving a subset of genomic features, if not NULL, time course table is generated for only this subset of genomic features. |
filter |
logical, whether to drop the genomic features
shows no significant changes (defined by |
pvalue |
character string specify the type of p-values
(' |
pvalue.threshold |
a numeric value giving threshold of selected p-value, only features with higher (ajusted) p-values than the threshold are kept. |
abs.fold |
a numeric value, the least absolute log2-fold changes |
direction |
character string specify the direction of fold
changes (' |
... |
A TCA
object
If 'expression
' in value
is chosen, for replicates ,
the normalized expression value is first calculated for each
replicate, then mean value is taken to represent the normalized
expression value.
Mengjun Wu
data(tca_ATAC) tca_ATAC <- DBanalysis(tca_ATAC) tca_ATAC <- timecourseTable(tca_ATAC, value = 'expression', lib.norm = TRUE, norm.method = 'rpkm')