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Convert Read Counts to Transcripts Per Million (TPM)

Source: R/count2tpm.R
count2tpm.Rd

Transforms gene expression count data into Transcripts Per Million (TPM) values, normalizing for gene length and library size. Supports multiple gene ID types and can retrieve gene length information from BioMart or use local datasets.

Usage

count2tpm(
  countMat,
  idType = "Ensembl",
  org = c("hsa", "mmus"),
  source = c("local", "biomart"),
  effLength = NULL,
  id = "id",
  gene_symbol = "symbol",
  length = "eff_length",
  check_data = FALSE
)

Arguments

countMat

Numeric matrix of raw read counts with genes in rows and samples in columns.

idType

Character string specifying the gene identifier type. Options are `"Ensembl"`, `"Entrez"`, or `"Symbol"`. Default is `"Ensembl"`.

org

Character string specifying the organism. Options include `"hsa"` (human) or `"mmus"` (mouse). Default is `"hsa"`.

source

Character string specifying the source for gene length information. Options are `"biomart"` (retrieve from Ensembl BioMart) or `"local"` (use local dataset). Default is `"local"`.

effLength

Data frame containing effective gene length information. If `NULL`, lengths are retrieved based on `source`. Default is `NULL`.

id

Character string specifying the column name in `effLength` containing gene identifiers. Default is `"id"`.

gene_symbol

Character string specifying the column name in `effLength` containing gene symbols. Default is `"symbol"`.

length

Character string specifying the column name in `effLength` containing gene lengths. Default is `"eff_length"`.

check_data

Logical indicating whether to check for missing values in the count matrix. Default is `FALSE`.

Value

Data frame of TPM-normalized expression values with genes in rows and samples in columns. Gene identifiers are converted to gene symbols in the output, regardless of the input `idType`.

Author

Wubing Zhang, Dongqiang Zeng, Yiran Fang

Examples

# Simulated count data
set.seed(123)
count_matrix <- matrix(
  rpois(100, lambda = 10),
  ncol = 5,
  dimnames = list(
    paste0("ENSG00000", 101:120),
    paste0("Sample", 1:5)
  )
)
# Simulated effective length data
eff_len <- data.frame(
  id = paste0("ENSG00000", 101:120),
  symbol = paste0("Gene", 1:20),
  eff_length = runif(20, 500, 5000)
)
# Transform to TPM using local gene annotation
eset <- count2tpm(
  countMat = count_matrix, effLength = eff_len,
  id = "id", length = "eff_length", gene_symbol = "symbol"
)
#> ℹ No duplicate gene symbols found.
head(eset)
#>         Sample1   Sample2   Sample3   Sample4   Sample5
#> Gene1  17872.27  15587.34  18209.61  24387.20  27170.38
#> Gene2  25679.75  26544.19  23257.31  33978.89  34701.99
#> Gene3  42051.04  13971.40  36724.09  26826.92  30910.36
#> Gene4 146098.96 101936.44 133970.97 130487.57 136681.51
#> Gene5  47308.28  33008.03  48201.25  32863.57  35407.06
#> Gene6  61007.04  37836.40  37295.14  28253.11  53269.60