View source: R/olink_Pathway_Visualization.R
olink_pathway_visualization | R Documentation |
Pathways are ordered by increasing p-value (unadjusted)
olink_pathway_visualization(
enrich_results,
method = "GSEA",
keyword = NULL,
number_of_terms = 20
)
enrich_results |
data frame of enrichment results from olink_pathway_enrichment() |
method |
method used in olink_pathway_enrichment ("GSEA" (default) or "ORA") |
keyword |
(optional) keyword to filter enrichment results on, if not specified, displays top terms |
number_of_terms |
number of terms to display, default is 20 |
A bargraph as a ggplot object
olink_pathway_enrichment
for generating enrichment results
olink_pathway_heatmap
for generating a heat map of results
library(dplyr)
# Run olink_ttest or other stats test (see documentaiton )
npx_df <- npx_data1 %>% filter(!grepl('control',SampleID, ignore.case = TRUE))
ttest_results <- olink_ttest(df=npx_df,
variable = 'Treatment',
alternative = 'two.sided')
try({ # This expression might fail if dependencies are not installed
# Run olink_pathway_enrichment (see documentation)
gsea_results <- olink_pathway_enrichment(data = npx_data1, test_results = ttest_results)
ora_results <- olink_pathway_enrichment(data = npx_data1,
test_results = ttest_results, method = "ORA")
olink_pathway_visualization(enrich_results = gsea_results)
olink_pathway_visualization(enrich_results = gsea_results, keyword = "immune")
olink_pathway_visualization(enrich_results = ora_results, method = "ORA", number_of_terms = 15)
})
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