Description Usage Arguments Value Examples
View source: R/cor_tf_target_gene.R
This function evaluate the correlation of a TF and target gene expression using spearman rank correlation test. Note that genes with RNA expression equal to 0 for all samples will not be evaluated.
1 2 3 4 5 6 7 | cor_tf_target_gene(
pair.tf.target,
exp,
tf.activity.es,
cores = 1,
verbose = FALSE
)
|
pair.tf.target |
A dataframe with the following columns: TF and target (target gene) |
exp |
Gene expression matrix or SummarizedExperiment object (rows are genes, columns are samples) log2-normalized (log2(exp + 1)). Samples should be in the same order as the tf.activity.es matrix |
tf.activity.es |
A matrix with normalized enrichment
scores for each TF across all samples to be used in linear models instead
of TF gene expression. See |
cores |
Number of CPU cores to be used. Default 1. |
verbose |
Show messages ? |
A data frame with the following information: TF, target gene, correlation p-value and estimate between TF and target gene expression, FDR corrected p-values.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | exp <- t(matrix(sort(c(runif(40))), ncol = 2))
rownames(exp) <- c("ENSG00000232886","ENSG00000232889")
colnames(exp) <- paste0("Samples",1:20)
pair.tf.target <- data.frame(
"TF" = "ENSG00000232889",
"target" = "ENSG00000232886"
)
# Correlated TF and gene expression
results.cor.pos <- cor_tf_target_gene(
pair.tf.target = pair.tf.target,
exp = exp,
)
# Correlated TF and gene expression
results.cor.pos <- cor_tf_target_gene(
pair.tf.target = pair.tf.target,
exp = exp,
tf.activity.es = exp
)
|
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