| 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 |
- 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