Description Usage Arguments Value Examples
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.
1 2 3 4 5 6 | 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")
|
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.
|
color.columns |
Vector of colors for the reference and query columns (compounds). If |
gene.thresP |
Vector of length 2 containing the positive gene thresholds for |
gene.thresN |
Vector of length 2 containing the negative gene thresholds for |
thresP.col |
Vector of length 2 containing the colors for the high gene scores for |
thresN.col |
Vector of length 2 containing the colors for the low gene scores for |
legend.names |
Option to draw a legend (about the highlights in |
legend.cols |
Colors to be used for the |
legend.pos |
The location of the legend: |
plot.type |
How should the plots be outputted? |
basefilename |
Basename of the graphs if saved in pdf files |
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
.
1 2 3 4 5 6 7 8 9 10 11 | ## 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.