{KOBAS}

R# Documentation

KOBAS

require(GCModeller); #' KOBAS toolkit imports "KOBAS" from "gseakit";

KOBAS toolkit

The KOBAS (KEGG Orthology Based Annotation System) enrichment analysis toolkit. This package provides the VB.NET port of the KOBAS 3.0 enrichment calculation algorithms:

kobas.gsea: GSEA analysis of a expression matrix against a gene set collection,
kobas.identify: four statistical enrichment tests (hypergeometric / Fisher / binomial / chi-square) for a foreground gene list against a background gene list,
kobas.plage / kobas.gage: SNBEA style pathway level analysis,
kobas.term_similarity / kobas.term_cluster: term similarity and clustering of the enrichment result terms,
kobas.fdr: multiple testing correction (BH / BY / q-value),
kobas.fisher_test / kobas.hyper_test / kobas.binom_test / kobas.chisq_test: the exact statistical tests used by the enrichment analysis.

The translated algorithms follow the standard GSEA / Fisher definitions rather than the original KOBAS implementation where the original code deviates from the standard definition (for example the miss step of the running enrichment score or the construction of the 2x2 contingency table). See the remarks of the individual functions for the detailed notes.

.NET clr function exports
as.data.frame.gsearesult	GseaResult: 一个基因集的 GSEA 分析结果。
as.data.frame.enrichmentrow	EnrichmentRow: 一行富集计算结果。
as.data.frame.snbearesult	SnbeaResult: SNBEA（子网络/通路水平富集）分析的一行结果。
read.KEGGpathway	Read KOBAS enrichment output result table file
kobas.background	Load a gene set collection from a GMT file as a GSEA background model.
kobas.gsea	Run the KOBAS style GSEA analysis Gene Set Enrichment Analysis with the KOBAS ranking and permutation scheme: the genes are ranked by the signal-to-noise ratio or the t statistics between the two phenotype groups, then a weighted Kolmogorov-Smirnov like running sum is evaluated for each gene set. The significance of the enrichment score is estimated by permuting the phenotype class labels.
kobas.identify	Run the KOBAS style over-representation (enrichment) analysis Perform the over-representation analysis with the KOBAS statistical tests: the foreground gene list is mapped onto the given gene set collection, then the members of each term are compared with the background gene list by the hypergeometric test, the Fisher exact test, the binomial test or the chi-square test.
kobas.read_annotate	Read a KOBAS `annotate` output file as a `dataframe`.
kobas.fdr	Multiple testing correction of the p-values.
kobas.plage	PLAGE pathway level analysis (KOBAS SNBEA) Score each gene set of the expression matrix with the PLAGE method (the first right singular vector of the row standardized gene set submatrix), then compare the activity scores between the two phenotype groups.
kobas.gage	GAGE style gene set enrichment analysis (KOBAS SNBEA) Aggregate the gene level t statistics of each gene set into a gene set level z-score (`sum(t) / sqrt(n)`) and estimate the significance from the upper tail of the standard normal distribution.
kobas.term_similarity	Compute the pairwise similarity matrix of the given term list.
kobas.term_cluster	Cluster the given terms by the pairwise similarity.
kobas.fisher_test	The Fisher exact test (greater / enrichment tail) of a 2x2 contingency table.
kobas.hyper_test	The hypergeometric test (upper tail) of a term.
kobas.binom_test	The exact binomial test (upper tail) of a term.
kobas.chisq_test	The chi-square test (Yates continuity correction) of a 2x2 contingency table.

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