How to use the gseapy.gsea.Prerank function in gseapy

rep.run()


    elif subcommand == "gsea":
        # compute using GSEAPY
        from .gsea import GSEA

        gs = GSEA(args.data, args.gmt, args.cls, args.outdir,
                  args.mins, args.maxs, args.n, args.weight,
                  args.type, args.method, args.ascending, args.threads,
                  args.figsize, args.format, args.graph, args.noplot, args.seed, args.verbose)
        gs.run()
    elif subcommand == "prerank":
        from .gsea import Prerank

        pre = Prerank(args.rnk, args.gmt, args.outdir, args.label[0], args.label[1],
                      args.mins, args.maxs, args.n, args.weight, args.ascending, args.threads,
                      args.figsize, args.format, args.graph, args.noplot, args.seed, args.verbose)
        pre.run()

    elif subcommand == "ssgsea":
        from .gsea import SingleSampleGSEA
        ss = SingleSampleGSEA(data=args.data, gene_sets=args.gmt, outdir=args.outdir,
                              sample_norm_method=args.norm,
                              min_size=args.mins, max_size=args.maxs, permutation_num=args.n,
                              weighted_score_type=args.weight, scale=args.scale,
                              ascending=args.ascending, processes=args.threads,
                              figsize=args.figsize, format=args.format, graph_num=args.graph,
                              no_plot=args.noplot, seed=args.seed, verbose=args.verbose)
        ss.run()

    elif subcommand == "enrichr":

:return: Return a Prerank obj. All results store to  a dictionary, obj.results,
             where contains::

                 | {es: enrichment score,
                 |  nes: normalized enrichment score,
                 |  p: P-value,
                 |  fdr: FDR,
                 |  size: gene set size,
                 |  matched_size: genes matched to the data,
                 |  genes: gene names from the data set
                 |  ledge_genes: leading edge genes}


    """
    pre = Prerank(rnk, gene_sets, outdir, pheno_pos, pheno_neg,
                  min_size, max_size, permutation_num, weighted_score_type,
                  ascending, processes, figsize, format, graph_num, no_plot, seed, verbose)
    pre.run()
    return pre