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In general, our comparison illustrated that a large proportion of the detections involved relatively low count genes, requiring caution when interpreting the results.
For gene expression profiles, low count genes were filtered using the NOISeq function filtered.data with filter 1 and a minimum count per million of 30 [ 36].
Interestingly, filtering out low count genes, even up to a total count of 10,000 (average count 435), had only a minor impact on the distributions (data not shown).
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Prior to statistical analysis, the bottom 20%% of low-count genes was filtered from the data set [ 37].
Both Classic and Signature MPSS data show a constant decrease in the percentage of low-count genes in the low tag-position range.
One method is to calculate the percentage of low-count genes (genes with count < 4 tpm) for different tag-position ranges.
Classic MPSS data shows a greater percentage of low-count genes in the high tag-position range while Signature MPSS data shows a flatter relationship.
In this paper, we define low-count genes as genes with measured abundance smaller than 4 tpm and significantly measured genes as genes with measured abundance greater than and equal to 4 tpm.
Presented in Dataset S1 (with an accompanying description in Text S1) are the genes, their fold change, a measure of average read count and a flag indicating especially lowly expressed (low read count) genes.
It should be noted that the relationship between expression and copy number is complex, and that the absence of significant correlations does not exclude the presence of the CNA, especially in cases where the low count of genes or samples makes this correlation statistically difficult to prove.
Given the association between high 25(OH D levels with low GEL count, genes regulated by 25(OH D were examined with respect to their impact on regulating GELs.
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