featureAssociation: Calculate the associations between modules and sample...

In bingzhang16/NetSAM: Network Seriation And Modularization

Calculate the associations between modules and sample features

Description

The featureAssociation function will calculate the associations between the sample features in the input annotation data and the modules identified by NetSAM or MatSAM function.

Usage

	featureAssociation(inputMat, sampleAnn, NetSAMOutput, outputHtmlFile, CONMethod="spearman", CATMethod="kruskal", BINMethod="ranktest", fdrmethod="BH",pth=0.05,collapse_mode="maxSD")

Arguments

inputMat	inputMat should contain a file name with extension "cct" or "cbt" or a matrix or data.frame object in R. The first column and first row of the "cct" or "cbt" file should be the row and column names, respectively and other parts are the numeric values. The detail information of "cct" or "cbt" format can be found in the manual of NetGestalt (www.netgestalt.org). A matrix or data.frame object should have row and column names and only contain numeric or integer values.
sampleAnn	sampleAnn should be a directory containing a file name with "tsi" extension or a data.frame object in R. The detail information of "tsi" format can be found in the manual of NetGestalt (www.netgestalt.org). The first row of the sample annotation data is the feature names. The second row is the feature types. The function supports four types: BIN (binary feature, such as male and female), CAT (category feature, such as stage i, stage ii and stage iii), CON (continuous feature, such as age) and SUR (survival data, such as overall survival). The third row is the feature categories. If there is no category information for the features, the sample information will start from the third row . The first column is the sample names.
NetSAMOutput	The list object outputted from NetSAM or MatSAM.
outputHtmlFile	The output directory of the HTML file.
CONMethod	The method to calculate the associations between modules and continuous features. The function provides two methods: "spearman" and "pearson" and the default is "spearman".
CATMethod	The method to calculate the associations between modules and category features. The function provides two methods: "anova" and "kruskal" and the default is "kruskal".
BINMethod	The method to calculate the associations between modules and binary features. The function provides two methods: "test" and "rankest" and the default is "rankest".
fdrmethod	The FDR method for identifying the significantly associated GO terms. The default is "BH".
pth	The threshold of the p values to identify the significant associations.
collapse_mode	The method to collapse duplicate ids. "mean", "median", "maxSD", "maxIQR", "max" and "min" represent the mean, median, max standard deviation, max interquartile range, maximum and minimum of values for ids in each sample. The default is "maxSD".

Value

The function will output a data.frame object and a HTML file to show the significant associations.

Author(s)

Jing Wang

See Also

MatSAM NetSAM

Examples

	inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM")
	sampleAnnDir <- system.file("extdata","sampleAnnotation.tsi",package="NetSAM")
	data(NetSAMOutput_Example)
	outputHtmlFile <- paste(getwd(),"/featureAsso_HTML",sep="")
	featureAsso <- featureAssociation(inputMat= inputMatDir, sampleAnn=sampleAnnDir, NetSAMOutput=netsam_output, outputHtmlFile=outputHtmlFile, CONMethod="spearman", CATMethod="kruskal", BINMethod="ranktest", fdrmethod="BH",pth=0.05,collapse_mode="maxSD")

bingzhang16/NetSAM documentation built on April 3, 2024, 3:35 a.m.