| z_score {geneExpression} | R Documentation |
<br /> To avoid the influence of expression level to the <br /> clustering analysis, z-score transformation can <br /> be applied to covert the expression values to <br /> z-scores by performing the following formula:<br /> <br /> <br /> z = (x - u) / sd<br /> <br /> <br /> x is value to be converted (e.g., a expression value <br /> of a genomic feature in one condition), µ is the <br /> population mean (e.g., average expression value Of <br /> a genomic feature In different conditions), σ Is the <br /> standard deviation (e.g., standard deviation of <br /> expression of a genomic feature in different conditions).
z_score(x);In statistics, the standard score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. Observed values above the mean have positive standard scores, while values below the mean have negative standard scores. The standard score is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more).
the HTS matrix object has been normalized in each gene expression row, z-score is calculated for each gene row across multiple sample expression data.