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Table 4 Median number of significant transcripts calls in the comparative dilution analysis (AGS versus NUGC3) before and after power-law correction

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

 

Original data

Power-law corrected data

Mapping method

AGS 12p vs NUGC3 12p

AGS 12p vs NUGC3 3p

AGS 3p vs NUGC3 12p

AGS 3p vs NUGC3 3p

AGS 12p vs NUGC3 12p

AGS 12p vs NUGC3 3p

AGS 3p vs NUGC3 12p

AGS 3p vs NUGC3 3p

Bowtie1

42

41

39

36

57

52

52

50

Bowtie2 (global)

44

43

43

41

61

59

61

58

Novoalign

43

40

39

36

58

57

57

54

BWA

41

41

39

36

58

55

56

53

  1. The breakdown of significant transcript calls for each combination of the mapping algorithms (Bowtie1, Bowtie2(global), Novoalign and BWA) and normalization methods (DESeq, RLE, TMM, Upperquartile, CPM and Quantile) for all 4 positive comparisons (AGS-12p versus NUGC-12p, AGS-12p versus NUGC-3p, AGS-3p versus NUGC-12p and AGS-3p versus NUGC-3p) are given in the following table. The median number of significant calls for 6 normalization methods are highlighted in red for each mapping algorithm