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===Global analysis of all data sets=== *First I need to combine the gene expression levels (FPKM) for all data sets in one matrix. For this I write a script [[Media:combine_geneTracking.txt|combine_geneTracking.pl]], and ran it in the directory:/home/kunzhang/SingleCell/Data/HL101 *For hierarchical clustering analysis, missing values are allowed in the matrix. I loaded the matrix into R, and create a dendrogram using the following commands: x=read.table("Mm_Indx3-15_FPKM_matrix.txt",header=TRUE,row.names=1); x.cor = cor(x,use="pairwise.complete.obs",method="pearson") x.d = as.dist(1-x.cor) x.h = hclust(x.d,"average") plot(x.h) [[Image:PGC-RNAseq-Hierarchical_analysis_092011.png|400px]] *Replicates tend to cluster together, suggesting that the quality of the data is good. *For PCA analysis, no missing value is allowed. So I set $no_missing_values = 1, and re-created the matrix. The PCA plot was generated with the following commands: methylMatrix=read.table("Mm_Indx3-15_FPKM_matrix_no_NA.txt",header=TRUE,row.names=1) pca1 <- prcomp(t(methylMatrix), scale=TRUE) mycolors=c("cyan", "green", "blue", "magenta","cyan", "green", "blue", "blue","magenta","magenta","dark red","red","red"); plot(pca1$x, pch=20, col=mycolors); plot(pca1$x, type="n") text(pca1$x, rownames(pca1$x), cex=0.8, col=mycolors) [[Image:PGC-RNAseq-PCA_plot_092011.png|500px]] *PC1 seems to explain the difference in developmental stage, whereas PC2 accounts for biological or technical variability among replicates. *Tet1 knock-out seem to slow down the development into the E13.5 stage.
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