Split dataframe into multiple dataframes by grouping columns

Question

I have a dataframe of expression data where gene are rows and columns are samples. I also have a dataframe containing metadata for each sample in the expression dataframe. In reality my expr dataframe has 30,000+ rows and 100+ columns. However, below is an example with smaller data.

expr <- data.frame(sample1 = c(1,2,2,0,0), 
                   sample2 = c(5,2,4,4,0), 
                   sample3 = c(1,2,1,0,1), 
                   sample4 = c(6,5,6,6,7), 
                   sample5 = c(0,0,0,1,1))
rownames(expr) <- paste0("gene",1:5)
meta <- data.frame(sample = paste0("sample",1:5),
                   treatment = c("control","control",
                                 "treatment1", 
                                 "treatment2", "treatment2"))

I want to find the mean for each gene per treatment. From the examples I've seen with split() or group_by() people group based on a column already present in the data.frame. However, I have a separate dataframe (meta) that classifies the grouping for the columns in another dataframe (expr).

I would like my output to be a dataframe with genes as rows, treatment as columns, and values as the mean.

#        control   treatment1   treatment2
#  gene1  mean        mean         mean
#  gene2  mean        mean         mean

Answer 1

An approach in base R which works for the particular toy data example given:

colnames(expr) = paste0(colnames(expr), "_", 
                        meta$treatment[match(colnames(expr), meta$sample)])
vapply(unique(meta$treatment), 
       \(i) rowMeans(expr[grepl(i, colnames(expr))]), numeric(nrow(expr)))
#>       control treatment1 treatment2
#> gene1       3          1        3.0
#> gene2       2          2        2.5
#> gene3       3          1        3.0
#> gene4       2          0        3.5
#> gene5       0          1        4.0

Data

expr <- data.frame(sample1 = c(1,2,2,0,0), 
                   sample2 = c(5,2,4,4,0), 
                   sample3 = c(1,2,1,0,1), 
                   sample4 = c(6,5,6,6,7), 
                   sample5 = c(0,0,0,1,1))
rownames(expr) <- paste0("gene",1:5)

meta <- data.frame(sample = paste0("sample",1:5),
                   treatment = c("control","control",
                                 "treatment1", 
                                 "treatment2", "treatment2"))

Answer 2

A base R approach:

expr|>
    split.default(with(meta, treatment[match(names(expr), sample)]))|>
    lapply(rowMeans)|>
    structure(dim=3)|>
    array2DF()

        Var1 gene1 gene2 gene3 gene4 gene5
1    control     3   2.0     3   2.0     0
2 treatment1     1   2.0     1   0.0     1
3 treatment2     3   2.5     3   3.5     4