rnaseq_norm | R Documentation |
Perform various RNAseq normalization methods on a count table.
rnaseq_norm(
data,
gene_lengths = NULL,
method = c("DESeq2_norm_counts", "edgeR_TMM", "TPM"),
gene_names = rownames(data),
gene_names_col_id = "ensembl_id",
sample_names = colnames(data)
)
data |
[Required] Integer based count table. Rows represent genes (features), columns represent samples (observations). |
gene_lengths |
[Default: NULL] The nucleotide count (length) of each gene. Required for TPM method. |
method |
[Default: c("DESeq2_norm_counts", "edgeR_TMM", "TPM")] Method of RNAseq normalization. |
gene_names |
[Default: rownames(data)] Character vector of gene names. |
gene_names_col_id |
[Default: "ensembl_id"] String column name that should be used for output table. |
sample_names |
[Default: colnames(data)] Character vector of sample names. |
A tibble::tibble data frame object of normalized counts. The first column will be a character type of gene names. Remaining columns will represent sample names.
Good info about normalization methods: [https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html](https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html) [https://www.biostars.org/p/317701](https://www.biostars.org/p/317701) [https://www.biostars.org/p/317417](https://www.biostars.org/p/317417) [https://www.biostars.org/p/210447](https://www.biostars.org/p/210447)
set.seed(1)
nrow = 10000
ncol = 6
min = 0
max = 1000
gene_lengths = sample(400:10000, nrow, TRUE)
my_counts <- matrix(sample(min:max, nrow*ncol, TRUE),
nrow = nrow, ncol = ncol,
dimnames = list(paste0("gene", 1:nrow), paste0("sample", 1:ncol)))
my_counts_norm <- rnaseq_norm(my_counts, method = "DESeq2_norm_counts")
my_counts_norm <- rnaseq_norm(my_counts, method = "edgeR_TMM")
my_counts_norm <- rnaseq_norm(my_counts, gene_lengths = gene_lengths, method = "TPM")
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