Preform Differential Gene Analysis

Usage

run_DESeq2(se_ln, group_var = "cell_type", ref_level = "Tconv")

Arguments

se_ln: A SummarizedExperiment object
group_var: determines the column to categorization
ref_level: Reference cell type (default: "Tconv")

Value

Differential gene expression results between cell types

Examples

data(example_se)

# Step 1: Filter low expression genes
se_filtered <- filter_low_exp_genes(example_se,
                                    min_count_per_group = 10)
#> Genes after filtering: 500 
#> colData names: cell_id cell_type batch

# Step 2: Run the DESeq2 pipeline
se_dge <- run_DESeq2(se_ln = se_filtered,
                     group_var = "cell_type",
                     ref_level = "Tconv")
#> converting counts to integer mode
#> 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