multi_cor | R Documentation |
Test correlation of each row of an object to each column of pheno.tab
using
one of Pearson's, Kendall's, or Spearman's correlation methods, or limma
regression in limma_cor
. See examples in vignette.
multi_cor(
object,
pheno.tab,
method = c("pearson", "spearman", "kendall", "limma"),
reorder.rows = TRUE,
prefix = NULL,
block = NULL,
correlation = NULL,
adjust.method = "BH",
covariates = NULL,
check.names = TRUE,
limma.cols = c("AveExpr", "P.Value", "adj.P.Val", "logFC")
)
object |
Matrix-like data object containing log-ratios or log-expression values, with rows corresponding to features (e.g. genes) and columns to samples. Must have row names that are non-duplicated and non-empty. |
pheno.tab |
Matrix-like data object with columns as sample phenotypes, with |
method |
Character string indicating which association is to be used
for the test. One of |
reorder.rows |
Logical, should rows be reordered by p-value? |
prefix |
Character string to add to beginning of column names. |
block |
Vector specifying a blocking variable on the samples. Has length = |
correlation |
Numeric vector of inter-duplicate or inter-technical replicate correlations. Must be given if
|
adjust.method |
Method used to adjust the p-values for multiple testing. Options, in increasing conservatism,
include |
covariates |
If |
check.names |
Logical; should |
limma.cols |
If |
Each column of pheno.tab
is tested independently. Arguments covariates
, block
, and
correlation
only apply if method="limma"
. When each individual pheno.tab
column is tested,
if some samples have NA
s for that column, those samples are omitted for that column only.
Data frame with several statistical columns corresponding to each phenotype and one row per feature.
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