CScompare: Compare CS Results.

In CSFA: Connectivity Scores with Factor Analysis

Description

After applying different CSanalysis on the same data, you can compare 2 different results of connectivity loadings, connectivity ranking scores and gene scores Unless the result came from a Zhang and Gant analysis, you choose from which component (factor, PC, bicluster) the scores should be derived. Further, for Zhang and Gant analysis, the "CRanking Scores" and "CLoadings" will be the same as the ZG Score as well as the p-values.

Usage

CScompare(CSresult1, CSresult2, component1.plot, component2.plot,
  threshold.pvalues = 0.05, which = c(1, 2, 3), color.columns = NULL,
  gene.thresP = NULL, gene.thresN = NULL, thresP.col = c("blue",
  "light blue"), thresN.col = c("red", "pink"), legend.names = NULL,
  legend.cols = NULL, legend.pos = "topright", plot.type = "device",
  basefilename = "CScompare")

Arguments

CSresult1	First result.
CSresult2	Second result.
component1.plot	If you are using a non-Zhang&Gant result, specify the bicluster, factor or principal component which should be used to derive connectivity scores from for the first result.
component2.plot	If you are using a non-Zhang&Gant result, specify the bicluster, factor or principal component which should be used to derive connectivity scores from for the second result.
threshold.pvalues	If both CSresult1 and CSresult contain pvalues (and adjusted pvalues), this threshold will be used to compare the number of overlapping significant results.
which	Choose one or both plots which should be created. CS Comparison Plot GS Comparison Plot CSRankScores (Normal CS for CSzhang) Comparison Plot CS p-values comparison plot (Raw & Adjusted). CRankScores p-values comparison plot (Raw & Adjusted).
color.columns	Vector of colors for the reference and query columns (compounds). If NULL, blue will be used for reference and black for query. Use this option to highlight reference columns and query columns of interest.
gene.thresP	Vector of length 2 containing the positive gene thresholds for CSresult1 and CSresult2. Genes above the threshold will be colored. (e.g. c(1,2))
gene.thresN	Vector of length 2 containing the negative gene thresholds for CSresult1 and CSresult2. Genes below the threshold will be colored. (e.g. c(-1,-2))
thresP.col	Vector of length 2 containing the colors for the high gene scores for CSresult1 and CSresult2 (e.g. c("blue","light blue")).
thresN.col	Vector of length 2 containing the colors for the low gene scores for CSresult1 and CSresult2 (e.g. c("red","pink")).
legend.names	Option to draw a legend (about the highlights in color.columns) in the CS plot. If NULL, only references are in the legend.
legend.cols	Colors to be used for the legend.names.
legend.pos	The location of the legend: "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center".
plot.type	How should the plots be outputted? "pdf" to save them in pdf files, device to draw them in a graphics device (default), sweave to use them in a sweave or knitr file.
basefilename	Basename of the graphs if saved in pdf files

Value

A list object with 2 slotes. In the first slot, Pearson and Spearman correlation between the results (CLoadings, Gene Scores, CRanking Scores, (adjusted) p-values) can be found. The second slot, if permutation was applied, contaisn a small comparison between the significant results based on threshold.pvalues.

Examples

## Not run: 
data("dataSIM",package="CSFA")
Mat1 <- dataSIM[,c(1:6)]
Mat2 <- dataSIM[,-c(1:6)]

MFA_analysis <- CSanalysis(Mat1,Mat2,"CSmfa")
ZHANG_analysis <- CSanalysis(Mat1,Mat2,"CSzhang")

CScompare(MFA_analysis,ZHANG_analysis,1)

## End(Not run)

CSFA documentation built on May 2, 2019, 5:56 p.m.