Use your own differential-expression table

Once Riaz ON runs, replace its table with your own. Supply the complete DE result, not only significant genes.

Prepare the input

For a DESeq2 table, use these columns:

Column	Meaning
`symbol`	Gene symbol matching the gene-set resource.
`log2FoldChange`	Signed effect for your comparison.
`stat`	Signed ranking statistic, normally the Wald statistic.
`pvalue`	Gene-level raw P value.
`padj`	Gene-level adjusted P value.

Check which group is in the numerator before writing the direction sentence. A DESeq2 likelihood-ratio statistic is unsigned; the extended R guide explains that case and edgeR/limma inputs.

Run one analysis

This example assumes a human DESeq2 Wald-test result and the resource prepared during Riaz ON. Change the two paths and the direction sentence.

library(lisaR)

result <- run_lisa(
  de = "de_treated_vs_control.tsv",
  output_dir = "lisa-treated-vs-control",
  species = "Homo sapiens",
  method = "DESeq2",
  positive_direction = "Higher in treated than in control"
)

stopifnot(identical(result$gate, "PASS"))
browseURL(result$report_index)

Choose a new output directory. STANDARD is the default; it includes PNG and SVG figures, source tables and figure recipes. Add pdf = TRUE if needed.

lisaR uses the supplied DE results. It does not re-estimate your effects or fit your experimental design. For another species, prepare the corresponding membership resource; do not substitute the human resource.

Next: Read your report.