SISPA: SISPA
In BhaktiDwivedi/SISPA: SISPA: Method for Sample Integrated Set Profile Analysis

Description Usage Arguments Details Value Examples

View source: R/SISPA.R

SISPA: Method for Sample Integrated Gene Set Analysis

1	SISPA(feature=1,f1.df,f1.profile,f2.df,f2.profile,cpt_data="var",cpt_method="BinSeg",cpt_max=60)

`feature`	Number of input feature or data types
`f1.df`	A data matrix of first feature (e.g., gene or probe expression values) where rows corrospond to genes and columns corrospond to samples
`f1.profile`	A flag to specify gene profile. If gene.profile="up" then samples with increased zscores are identified. If gene.profile="down" then samples with decreased zscores are identified. Default is "up".
`f2.df`	A data matrix of second feature (e.g., gene variant change) where rows corrospond to genes and columns corrospond to samples
`f2.profile`	A flag to specify gene profile. If gene.profile="up" then samples with increased zscores are identified. If gene.profile="down" then samples with decreased zscores are identified. Default is "up".
`cpt_data`	Identify changepoints for data using variance (cpt.var), mean (cpt.mean), or both (cpt.meanvar). Default is cpt.var.
`cpt_method`	Choice of single or multiple changepoint model. Default is "BinSeg". See changepoint R package for details
`cpt_max`	The maximum number of changepoints to search for using "BinSeg" method. Default is 60.

Sample Integrated Gene Set Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. The user specifies a gene list of interest and sample by gene molecular data (expression, methylation, variant, or copy change data) to obtain gene set enrichment scores by each sample. The score statistics is rank ordered by the desired profile (e.g., upregulated or downregulated) for samples. A change point model is then applied to the sample scores to identify groups of samples that show similar gene set profile patterns. Samples are ranked by desired profile activity score and grouped by samples with and without profile activity. Figure 1 shows the schematic representation of the SISPA method overview.

The input molecular data frame with added sample identifiers and estimated changepoints. A plot showing the changepoint locations estimated on the data. Bar plots pdf illustrating distinct distribution of samples with and without profile activity

g <- 10 ## number of genes
s <- 60 ## number of samples
## sample data matrix with values ranging from 1 to 10
rnames <- paste("g", 1:g, sep="")
cnames <- paste("s", 1:s, sep="")
expr <- matrix(sample.int(10, size = g*s, replace = TRUE), nrow=g, ncol=s, dimnames=list(rnames, cnames))
SISPA(feature=1,f1.df=expr,f1.profile="up")