KSEAapp (version 0.99.0)

KSEA.Heatmap: The KSEA App Analysis (KSEA Heatmap Only)

Description

Takes a list of the KSEA kinase score outputs from KSEA.Scores() and creates a merged heatmap (only applicable for multi-treatment studies)

Usage

KSEA.Heatmap(score.list, sample.labels, stats, m.cutoff, p.cutoff,
  sample.cluster)

Arguments

score.list

the data frame outputs from the KSEA.Scores() function, compiled in a list format

sample.labels

a character vector of all the sample names for heatmap annotation; the names must be in the same order as the data in score.list; please avoid long names, as they may get cropped in the final image

stats

character string of either "p.value" or "FDR" indicating the data column to use for marking statistically significant scores

m.cutoff

a numeric value between 0 and infinity indicating the min. # of substrates a kinase must have to be included in the heatmap

p.cutoff

a numeric value between 0 and 1 indicating the p-value/FDR cutoff for indicating significant kinases in the heatmap

sample.cluster

a binary input of TRUE or FALSE, indicating whether or not to perform hierarchical clustering of the sample columns

Value

exports a .png heatmap image highlighting the merged datasets; heatmap was generated using the heatmap.2() function (gplots package); asterisks mark scores that met the statistical cutoff, as defined by p.cutoff; blue color indicates negative kinase score, and red indicates positive kinase score

References

Casado et al. (2013) Sci Signal. 6(268):rs6

Hornbeck et al. (2015) Nucleic Acids Res. 43:D512-20

Horn et al. (2014) Nature Methods 11(6):603-4

Examples

#The score.list input must be a list of the data frame outputs from KSEA.Scores() function
#KSEA.Scores.1, KSEA.Scores.2, and KSEA.Scores.3 are all 
#sample datasets provided within this package

KSEA.Heatmap(score.list=list(KSEA.Scores.1, KSEA.Scores.2, KSEA.Scores.3), 
             sample.labels=c("Tumor.A", "Tumor.B", "Tumor.C"), 
             stats="p.value", m.cutoff=3, p.cutoff=0.05, sample.cluster=TRUE)