FunciSNPplot(dat, rsq = 0, split = FALSE, splitbysnp = FALSE, tagSummary = FALSE, heatmap = FALSE, heatmap.key = FALSE,genomicSum = FALSE, save = FALSE, pathplot=getwd(), text.size=10, save.width=7, save.height=7)dat is a data.frame object from FunciSNPAnnotateSummary. Need to run FunciSNPAnnotateSummary first.
rsq is the Rsquared cutoff used to subset.
split will generate distribution plot of all Correlated SNPs by Rsquare values.
splitbysnp is similar to split but instead split the distribution by tagSNP.
tagSummary Will output two plots per biofeature. The first one is a scatter plot showing the relationship between Rsquare and Distance to tagSNP for each YAFSNP. The second plot is a histogram distribution of number of correlated SNPs at each Rsquare value. Each set of plot is further divided by tagSNP. Best if used with rsq value.
heatmap correlation heatmap to visualize the number of correlated SNPs at each tagSNP overlapping each biological feature. Most informative if used with a rsq value.
heatmap.key Places the count of each cell on the heatmap.
genomicSum Stacked bar chart summarizing all correlated SNPs for each of the identified genomie features (exon, intron, 5'UTR, 3'UTR, promoter, lincRNA or in gene desert (intergentic)). Most informative if used with a rsq value.
save to save outputs to folder. Set at getwd(), in folder 'FunciSNP.VERSION/plots
pathplot is the path to the folder where to save the plots. Default to getwd() or current working directory.
getFSNPs, FunciSNPplot, FunciSNPAnnotateSummary, FunciSNPtable, FunciSNPbed
data(glioma)
gl <- FunciSNPAnnotateSummary(glioma)
FunciSNPplot(gl)
FunciSNPplot(gl, rsq=0, genomicSum=TRUE, save=FALSE)
FunciSNPplot(gl, rsq=0.5, genomicSum=TRUE, save=FALSE)
# DO NOT RUN
#FunciSNPplot(gl, tagSummary=TRUE, rsq=0.5)
#
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