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Fig. 7 | Biology Direct

Fig. 7

From: Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

Fig. 7

Volcano plots of dilution data set before and after power-law correction. Akin to Fig. 6, the volcano plots of the dilution dataset before (left-column) and after (right-column) the power-law correction is shown in Fig. 7. In particular, Figs a, b, c and d shows the MA-plot analysis for 4 mapping (Bowtie1, Bowtie2(global), Novoalign and BWA) algorithms while the permutation of the 6 normalization algorithms (DESeq, Relative Log Expression (RLE), Trimmed Mean of M-values (TMM), UpperQuartile (UQ), Count Per Million (CPM) and Quantile normalization) are arranged in a row-wise manner. Overall, the apparent asymmetrical spread of the noise comparisons (in blue) of the uncorrected data set demonstrates the non-zero fold-change bias despite the application of various normalization methods. Most importantly, the slower rate of change in p-values of the uncorrected cases (see left-column) when compared to the power-law corrected cases (see right-column), implies that a higher fold-change threshold is needed to acquire the same p-value (or Type I error rate) during statistical testing. In turn, a higher fold-change threshold also implies a larger type II error (i.e., failing to detect an effect that is present) for the uncorrected cases and eventually, a compromised sensitivity on the statistical testing

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