Designs, which estimate expression changes relative to a standard, facilitate across-study comparisons.
Because of the well-established precision of the Affymetrix platform, and that our goal was to broadly cataloging patterns of gene expression, not to precisely estimate expression changes for specific genes, we did not conduct RT-PCR validation studies.
As shown in table S2, estimated gene expression changes may differ among the different probesets that are available in the array for each gene.
Cuffdiff2 estimates the expression changes at transcript level and controls for variability across replicate libraries, modeling variability in the number of fragments generated by each transcript across replicates.
The fold change (FC) simply estimates the expression change in logarithmic scale, log log F C g, m ≡ Δ e g, m. 2. The weighted average difference (WAD -score, (1) WAD -score = w g, m · Δ e g, m w i t h w g, m = Δ e g, m − min Δ e g, m max Δ e g, m − min Δ e g, m is a fold-change based measure well performing in differential expression analysis [ 14, 15].
It can correct biased estimates of expression changes in the case of systematic differences in tissue composition between sample groups.
It can even lead to wrong estimates of expression changes in the case of systematic changes in tissue composition between test conditions.
Using ancestral (1 2 million years old) and artificially generated Gossypium hybrids, Flagel et al.[ 45] estimated that ¼ of expression changes are related to allopolyploidy.
41, 42 Previously, Shen-Orr et al have developed a deconvolution method (csSAM), which takes advantage of complete blood cell count and differential (CBC/diff) data to statistically estimate differential gene expression changes within each cell type.
Gene expression changes were estimated discarding probes with SFPs from each probe set.
