![]() If you get 0 and 1, respectively, then you have to take the normalization for each library, separately. zis.na(z) <- 0 Replaces NA values with zeros. As a first step, weâll need to construct some data that we can use in the exemplifying syntax later on. You can check this very easily: calculate mean and standard deviation of one sample from cosmic. calculate average and std for all genes in a library, and then transform to Z-scores all the values of the genes in that library. Very likely therefore you have to take the normalization for each library, separately. Therefore, from one database release to the other, the gene expression level of a gene in the same library would change, which, I think is nonsense for a database. Packages like EdgeR gives only logfc and pvalue. The change in gene expression means that the number of the end product (mostly proteins) that one gene is assigned to produce has changed. Facet the barplot, add a title and change the colour scale for the z-score GOBar(circ, display multiple, title Z-score coloured barplot, zsc. ![]() Now, if the normalization was done in the dimension of the samples, as you suggest to do, when a sample is added, the normalization would have to be performed again, because the average and standard deviation, after adding one sample, would be changed. Hi, This may not be relevant to iDEA but its a dependency in the input. To add a title use title and to change the colour scale of the z-score use the argument zsc.col (output not shown). In other words, A has gene expression four times lower than B, which means at the same time that B has gene expression 4 times higher than A. If log2 (FC) 2, the real increase of gene expression from A to B is 4 (22) ( FC 4 ). There is one point that makes me think you are going in the wrong direction by performing the Z-score transformation over the samples: I don't know the cosmic database very well, but let's assume that the data is constantly increasing by submission of new RNA-seq libraries. 1 Answer Sorted by: 2 Let's say that for gene expression the logFC of B relative to A is 2.
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