Prerank Algorithm

Purpose and Usage

• Scenario 1: Perform prerank analysis on all differentially expressed genes obtained from SDAS DEG analysis

  SDAS geneSetEnrichment prerank \
  -i de_t-test.anno_rctd.SmoothMuscle-vs-Endo.all.csv -o ./ \
  --species human

• Scenario 2: Analyze only with databases of interest

  SDAS geneSetEnrichment prerank \
  -i de_t-test.anno_rctd.SmoothMuscle-vs-Endo.all.csv -o ./ \
  --gmt sdas_deg_enrichment/lib/GSEADB/h.all.v2024.1.Hs.symbols.gmt,sdas_deg_enrichment/lib/GSEADB/KEGG_2021_Human.gmt

• Scenario 3: Plot only pathways of interest. Write the full names of the pathways of interest into a txt file, one per line, and pass this txt file to the analysis process via the --pathways parameter. Note that the specified pathways must be included in the database used.

  SDAS geneSetEnrichment prerank \
  -i de_t-test.anno_rctd.SmoothMuscle-vs-Endo.all.csv -o ./ \
  --gmt sdas_deg_enrichment/lib/GSEADB/h.all.v2024.1.Hs.symbols.gmt,sdas_deg_enrichment/lib/GSEADB/KEGG_2021_Human.gmt \
  --pathways ./term.txt

Input Parameter Description

Prerank Parameter	Required	Default Value	Description
-i / --input	Yes		deg result file in csv format.
-o / --output	Yes		The GSEApy output directory. Default: the current working directory
--species	No	human	Use biuld-in gmt database: human or mouse. Default: human. More database see here: https://amp.pharm.mssm.edu/modEnrichr.
--gmt	No		Customized gene set database in GMT format. One or more databases split by ",". Default use --species build-in database.
--graph	No	5	Numbers of top graphs produced. Default: 5
--pathways	No		Specify graphs name in a txt file to draw GSEA picture, default uses --graph .
--min_size	No		Min size of input genes presented in Gene Sets. Default: 15
--max_size	No		Max size of input genes presented in Gene Sets. Default: 20000
--label	No		The phenotype label argument need two parameters to define. Default: ('Pos','Neg')
-v / --verbose	No		Increase output verbosity, print out progress of your job. Default False
--permu_num	No	1000	Number of random permutations. For calculating esnulls. Default: 1000
--weight	No	1	Weighted_score of rank_metrics. For weighting input genes. Choose from {0, 1, 1.5, 2}. Default: 1
--ascending	No		Rank metric sorting order. If the --ascending flag was chosen, then ascending equals to True. Default: False.
--seed	No	123	Number of random seed. Default: 123
--threads	No	1	Number of threads you are going to use. Default: 1

Output Results Display

Prerank Result File	Description
prerank_{database}.csv	Result file in csv format
prerank_{database}:top10.pdf/png	Top 10 pathway plots in pdf and png formats

• csv file format: prerank_{database}.csv is similar to the gsea result, containing Name, Term, ES, NES, NOM p-val, FDR q-val, FWER p-val, Tag %, Gene %, Lead_genes. Term is the pathway name; ES is the Enrichment Score, reflecting the degree of enrichment of gene set members in the ranked gene list (e.g., ranked by log2FC). Positive ES: gene set is enriched at the top of the list (positively correlated with phenotype); negative ES: enriched at the bottom (negatively correlated). NES is the Normalized Enrichment Score; NOM p-val is the nominal p-value; FDR q-val is the adjusted p-value; FWER p-val is the family-wise error rate adjusted p-value; Tag % is the percentage of genes in the core enrichment region; Gene % is the percentage of genes used in the analysis out of the total in the gene set; Lead_genes are the core genes contributing most to the ES.

Name	Term	ES	NES	NOM p-val	FDR q-val	FWER p-val	Tag %	Gene %	Lead_genes
prerank	HALLMARK_MYC_TARGETS_V1	0.7472938191195556	2.39333105644001	0.0	0.0	0.0	160/195	18.89%	RPL14;HNRNPA2B1;...
prerank	HALLMARK_OXIDATIVE_PHOSPHORYLATION	0.7431758291176868	2.376055485647371	0.0	0.0	0.0	168/200	20.44%	MDH2;COX8A;...
prerank	HALLMARK_ALLOGRAFT_REJECTION	0.744882727767552	2.3688992213810462	0.0	0.0	0.0	118/194	14.03%	ITGB2;HLA-DRA;...
prerank	...	...	...	...	...	...	...	...	...

• Top Terms Enrichment Curve Plot: prerank_{database}:top10.pdf/png (see example below). In the plot, the sign of the Enrichment Score (ES) directly reflects the distribution pattern of the gene set in the gene list ranked by log2FC: positive ES means the gene set is concentrated at the top of the list and positively correlated with the phenotype; negative ES means the gene set is concentrated at the bottom and negatively correlated.