We computed mRIN on the 537 SCZ case and control samples for 18,338 (17,527 uniquely identified) RefSeq transcripts using the mRIN package by Feng et al. Sample by transcript combinations were required to have an abundance > 2. After this step, 6,072 transcripts with a missing rate > 50% were removed from the analysis. Finally, for transcripts with more than one entry in the dataset the entry with the lowest missing rate was retained. After these edits a total of 12,246 transcripts remained. The mRIN statistics and associated P values were subsequently computed using the formulas from the Feng et al. paper. Samples with extreme negative values for the mRIN statistic should indicate low quality samples. The distribution is centered near zero and has no extreme negative values. There were 17 and 3 samples with P value < 0.05 and < 0.01, respectively. One would expect a total of 29 and 5 samples to have P values of this magnitude by chance alone. We therefore conclude the RNA quality of the samples is adequate.
