Export differential expression results, summary counts, and filtering diagnostics to TSV files

Usage

export_outputs(
  res_df,
  summary_df,
  filtering_diag,
  volcano,
  output_dir = file.path(tempdir(), "de_output")
)

Arguments

res_df: Dataframe of log2fold-changed shrinkage applied to ds
summary_df: A data frame summarizing the number of non-significant(ns)/up/down genes
filtering_diag: A data frame summarizing the number of significant genes per threshold
volcano: Your choice of volcano plot
output_dir: Directories where files will be written

Value

TSV files of differential expression results, summary counts, and filtering diagnostics

Examples

 data(example_se)

# Step 1: Evaluate how model preforms using different threshold values
example_se_filtering_assessment<- determine_filter_threshold(example_se)
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.
#> converting counts to integer mode
#> -- 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.

# Step 2; 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 3: Run the DESeq2 pipeline to get differential gene expression results
 se_dge<- run_DESeq2(se_filtered)
#> 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

# Step 4: Run log2_shrinkage on DESeq2 results for reliable estimates
se_dge_shrink <- log2_shrinkage(se_dge)
#> using 'apeglm' for LFC shrinkage. If used in published research, please cite:
#>     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
#>     sequence count data: removing the noise and preserving large differences.
#>     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895

# Step 5: Create a volcano plot using log2_shrinkage results
example_se_volcano<- generate_volcano(se_dge_shrink)

# Step 6: Generate the regulation summary of the genes
DESeq2_gene_reg_summary<- gene_regulation_summary(se_dge_shrink)

# Step 7: Export final results
example_se_exports <- export_outputs(
  res_df = se_dge_shrink,
  summary_df = DESeq2_gene_reg_summary,
  filtering_diag = example_se_filtering_assessment,
  volcano = example_se_volcano,
  output_dir = file.path(tempdir(), "de_output")
)
#> Export complete. Files saved:
#> /tmp/RtmpSPBh2d/de_output/de_results.tsv
#> /tmp/RtmpSPBh2d/de_output/de_summary.tsv
#> /tmp/RtmpSPBh2d/de_output/filtering_diagnostics.tsv
#> /tmp/RtmpSPBh2d/de_output/volcano_plot.pdf
#> /tmp/RtmpSPBh2d/de_output/volcano_plot.png