Trimmed Mean of M-Values (TMM) Normalization

TMM normalization is recommended for RNA-seq data generally when the majority of genes are not differentially expressed. We use this as a quality control tool when plotting counts per gene.

tmm(object)

# S4 method for bcbioRNASeq
tmm(object)

# S4 method for DESeqDataSet
tmm(object)

# S4 method for matrix
tmm(object)

Arguments

object	Object.

Value

matrix.

Examples

# bcbioRNASeq
tmm(bcb) %>% summary()
#>     group1_1            group1_2           group2_1           group2_2       
#>  Min.   :     0.00   Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  
#>  1st Qu.:     0.00   1st Qu.:     0.0   1st Qu.:     0.0   1st Qu.:     0.0  
#>  Median :     0.00   Median :     0.0   Median :     0.0   Median :     0.0  
#>  Mean   :  1413.77   Mean   :  2200.7   Mean   :  2082.5   Mean   :  2373.1  
#>  3rd Qu.:    19.71   3rd Qu.:    17.2   3rd Qu.:    19.6   3rd Qu.:    17.1  
#>  Max.   :309985.26   Max.   :366775.7   Max.   :367736.6   Max.   :388504.5  

# DESeqDataSet
# NOT RUN {
tmm(dds) %>% summary()
# }

# matrix
# NOT RUN {
assay(bcb) %>%
    tmm() %>%
    summary()
# }