mGSZ2: mGSZ2: Improved Modified Gene Set Z-score method for gene set...

In jcrodriguez1989/mGSZ2:

Description

mGSZ2 Gene set analysis based on Gene Set Z scoring function and asymptotic p-value

Usage

mGSZ2(x, y, l, rankFn = "MA", min.sz = 5, pv = 0, w1 = 0.2,
  w2 = 0.5, vc = 10, p = 200, rankInParallel = !F)

Arguments

x	Gene expression data matrix (rows as genes and columns as samples). Raw counts for RNA-Seq data.
y	Gene set data (dataframe/table/matrix/list)
l	Vector of response values (example:1,2)
rankFn	One of 'MA', 'RNA' if data comes from microarrays-compatible or RNA-Seq technologies respectively. Or a function with inputs (x, l), and returns a vector with the same length as the nrow(x). Or a matrix of rankings genes as rows (must have genes as rownames), first column for real ranking, rest of columns for permuted rankings, in this case mGSZ2 will not use x and l inputs (can be NA).
min.sz	Minimum size of gene sets (number of genes in a gene set) to be included in the analysis
pv	Estimate of the variance associated with each observation
w1	Weight 1, parameter used to calculate the prior variance obtained with class size var.constant. This penalizes especially small classes and small subsets. Default is 0.2. Values around 0.1 - 0.5 are expected to be reasonable
w2	Weight 2, parameter used to calculate the prior variance obtained with the same class size as that of the analyzed class. This penalizes small subsets from the gene list. Default is 0.5. Values around 0.3 and 0.5 are expected to be reasonable
vc	Size of the reference class used with wgt1. Default is 10
p	Number of permutations for p-value calculation
rankInParallel	If FALSE, permutation gene rankings will be calculated sequentially. Useful if rankFn is provided and is already parallelized.

Details

A function for Gene set analysis based on Gene Set Z-scoring function and asymptotic p- value. It differs from GSZ (Toronen et al 2009) in that it implements asymptotic p-values instead of empirical p-values. Asymptotic p-values are based on fitting suitable distribution model to the permutation data. Unlike empirical p-values, the resolution of asymptotic p-values are independent of the number of permutations and hence requires consideralbly fewer permutations. In addition to GSZ, this function allows the users to carry out analysis with seven other scoring functions and compare the results.

Value

Dataframe with gene sets (in decreasing order based on the significance) reported by mGSZ method, scores, p-values, and list of genes that contributed to the enrichment.

Author(s)

Juan Cruz Rodriguez [e-mail]

Examples

## Not run: 
data(dummyData);
mGSZres <- mGSZ2(dummyData$x, dummyData$y, dummyData$l);

## End(Not run)

jcrodriguez1989/mGSZ2 documentation built on May 24, 2019, 9:51 a.m.