Package index
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chunked_crossprod() - Chunked computation of cross product
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colranks() - Compute columnwise ranks of matrix
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cor_sparse_matrix() - Calculate sparse correlation matrix handling missing values
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dualGSEA() - Reimplementation of dualGSEA (Bull et al., 2024) but defaults with replaid backend. For the preranked test we still use fgsea. Should be much faster than original using fgsea + GSVA::ssGSEA.
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dual_test() - Statistical testing of differentially enrichment
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fc_ttest() - Statistical testing of differentially enrichment
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gmt2mat() - Convert GMT to Binary Matrix
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gset.rankcor() - Calculate gene set rank correlation
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gset_averageCLR() - Compute geneset expression as the average log-ration of genes in the geneset. Requires log-expression matrix X and (sparse) geneset matrix matG.
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gset_ttest() - Perform t-test on gene set scores
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mat.rowsds() - Calculate row standard deviations for matrix
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mat2gmt() - Convert Binary Matrix to GMT
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matrix_metap() - Matrix version for combining p-values using fisher or stouffer method. Much faster than doing metap::sumlog() and metap::sumz()
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matrix_onesample_ttest() - Perform one-sample t-test on matrix with gene sets
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normalize_medians() - Normalize column medians of matrix
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plaid() - Compute PLAID single-sample enrichment score
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read.gmt() - Read GMT File
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replaid.aucell() - Fast calculation of AUCell
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replaid.gsva() - Fast approximation of GSVA
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replaid.scse() - Fast calculation of scSE score
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replaid.sing() - Fast calculation of singscore
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replaid.ssgsea() - Fast calculation of ssGSEA
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replaid.ucell() - Fast calculation of UCell
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sparse_colranks() - Compute columm ranks for sparse matrix. Internally used by colranks()
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write.gmt() - Write GMT File