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Iterative Differential Expression Analysis Using baySeq Algorithm

This study employs an iterative approach with TbT and TMM steps for differential gene expression analysis using the baySeq algorithm. The analysis includes multiple iterations to enhance accuracy and reliability.

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Iterative Differential Expression Analysis Using baySeq Algorithm

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  1. (a) Default TbT procedure step1: TMM step2: baySeq step3: TMM ↓ TbT Second iteration step1: TbT1 step2: baySeq step3: TMM ↓ TbT2 First iteration step1: TbT step2: baySeq step3: TMM ↓ TbT1 Third iteration step1: TbT2 step2: baySeq step3: TMM ↓ TbT3 (b) PDEG=20% and PA = 50% p = 0.04 p = 0.74 p = 0.77 p = 0.61 p = 0.71 p = 0.86 p = 1.00 p = 0.71 p = 0.55 p = 0.89 p = 0.99 p = 0.96 (c)PDEG=20% and PA = 70% p < 0.01 p < 0.01 p < 0.01 p = 0.88 p = 0.49 p = 0.60 (d) PDEG=20% and PA = 90%

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