Likewise, Let v ( x) 1/ (1+exp (-crossprod (coefD, x))) be the expected value of the discrete component. For each gene, let u ( x) be the expected value of the continuous component, given a covariate x and the estimated coefficients coefC, ie, u ( x) crossprod (x, coefC). Naturally it would be wrong just to compare the processed expression data of the cells after the stimulation between the groups. Details The log-fold change is defined as follows. It seems that the authors knew what they were doing while processing the data so I am trying to make use of the processed results. Each of these groups have preprocessed the signal from the machine in a slightly different way and applied different normalization procedure therefore two types of data are available: raw and processed. Each of these groups analyzed gene expression with a different microarray platform. Usage logfc2fc (logFC) Arguments logFC Numeric vector of log2 fold-changes. Hello Bioc Users, I am having a hard time understanding why heatmap.2 uses row (or column) Z-score instead of directly plotting logFC values Based on the formula for calculating Z-score, I understand that it is used for scaling or smoothing. All of them have uploaded the data into bioinformatical databases. Transform a log2 fold-change to a (possibly negative) fold-change. Let's say that I want to compare the effect of monocytes' stimulation with factor X on the gene expression (microarrays) performed by five distinct groups (this is just an example). So operate this pC, if you have 14 variables (7 control + 7 treatment) you will have in principle 14 components but, due to the mutual correlation between the abundance of different protein species you will end up into very few (2 or 3) principal acomponents explaining the by far major part of total variance. On this matrix operate a Principal Component Analysis (that allow a dual representation of the same data set in terms of loadings (correlation coefficients of the variables with components) and scores (values of different component for each sample). the matrox having as rows the protein species and as columns (variables) the samples. Simply operate on the transpose of tour original data matrix, i,e. Thus the main lane IS TO EXCHANGE THE ROLE OF VARIABLES AND STATISTICAL UNITS. The basic problem of high-througput data (common to metabolomics, transcriptomics and protein arrays) is the huge number of variables (protein species in your case) with respect to statistical units (the 14 samples) that open the way to a plethora of chance correlations. To convert a logFC value, simply use it as the exponent of two: 2 logFC.
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