Sentence examples for deviation in expression from inspiring English sources

To identify genes that cluster together under the tested conditions, we excluded probe sets with a standard deviation in expression values of < log2 (0.45) from the vector mean.

To objectively quantify tissue-specificity, we used the concept of entropy (HT) to measure the deviation in expression uniformity for a given transcript across multiple tissues [17].

Based on the number of samples and the observed standard deviation in expression of each assay, we had 80% power and 95% confidence to detect a >1.2-fold difference in expression between groups with risk and non-risk alleles for each SNP and between the T2D and control groups.

A signature of chromosome instability was inferred from transcriptomic data as functional aneuploidy related to a clear deviation in expression of contiguous genes from the same loci [ 17].

On the basis of the number of samples and standard deviation in expression of assays in each tissue tested, we estimated the detectable fold difference between the groups of samples carrying the risk and non-risk alleles (Table  1).

Frequently these genes are absent in one sample but highly expressed in the other sample (shown as coloured lines in figure 6), which may explain the deviation in expression ratios following TwoRA.

R11C05-LexA expresses in sexpressesginnseverale Dregionsla brain, including the ellipsofd body, the sub-esophageal zone, anDrosophilac lobrainbut the lincludingmber of cells expressing thes drivellipsoids that deviations in expression can bodyasily spothed.

Some of those deviations in expression from the model can be attributed to low or uneven depth of coverage, while other deviations might be attributable to errors in the gene model.

In addition to these main effects (genes as a group or tissues as a group), significant variation could be attributed to the gene-tissue interaction effect (F1680,5669 = 38.55, p<0.0001), reflecting gene specific deviations in expression across various tissues.

Power analysis was performed with StatMate 2.0 (GraphPad) and was based on standard deviations in expression of each of assays, number of samples with 0 or 1 and 2 risk alleles at each SNP and 80% power to detect the difference with 5% of type I error.

Considering the FDMs and counting the number of deviations in expression from the model, 228 (23%) of the 974 significantly modified models had at least one error flag not at the ends of the sequence and 62 (6%) had more than one error flag.