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Generate Reference Gene Matrix from RNA-seq DEGs

Source: R/generateRef_rnaseq.R
generateRef_rnaseq.Rd

Uses DESeq2 to identify differentially expressed genes and create a reference matrix from median expression levels across cell types.

Usage

generateRef_rnaseq(dds, pheno, mode = "oneVSothers", FDR = 0.05, dat)

Arguments

dds

Matrix. Raw count data from RNA-seq.

pheno

Character vector. Cell type classes.

mode

Character. DEG identification mode: `"oneVSothers"` or `"pairs"`. Default is `"oneVSothers"`.

FDR

Numeric. Threshold for adjusted p-values. Default is 0.05.

dat

Matrix. Normalized expression data (e.g., FPKM, TPM).

Value

List containing: - `reference_matrix`: Data frame of median expression for significant genes across cell types. - `G`: Optimal number of probes minimizing condition number. - `condition_number`: Minimum condition number. - `whole_matrix`: Full median expression matrix.

Author

Rongfang Shen

Examples

dds <- matrix(rpois(200 * 10, lambda = 10), ncol = 10)
rownames(dds) <- paste("Gene", 1:200, sep = "_")
colnames(dds) <- paste("Sample", 1:10, sep = "_")
pheno <- sample(c("Type1", "Type2", "Type3"), 10, replace = TRUE)
dat <- matrix(rnorm(200 * 10), ncol = 10)
rownames(dat) <- rownames(dds)
colnames(dat) <- colnames(dds)
# \donttest{
results <- generateRef_rnaseq(dds = dds, pheno = pheno, FDR = 0.05, dat = dat)
#> converting counts to integer mode
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> converting counts to integer mode
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testing
#> converting counts to integer mode
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testing
# }