Choice of lowly-expressed gene cutoff
Our analysis showed that stronger filtering improved the inter-site agreement—in particular, the correlation between fold-change estimates—by removing lowly-expressed genes. This prompted us to investigate the relationship between known fold changes using endogenous reference RNA (ERCC1 and ERCC2 controls) which were spiked into clinical samples in a way to evaluate fold-change accuracy (ERCC1 was spiked into 10 samples, and ERCC2 was spiked into the remaining 40 samples). Based on the observed non-linear trend, we devised a method to empirically determine a low gene expression LCPM cutoff that represents good Spearman correlation of observed vs expected ERCC control fold changes. Using this approach with each site’s data separately, we established low gene expression thresholds of 3 LCPM and 2 LCPM for Sites 1 and 2, respectively. These thresholds balance the trade-off between fold-change accuracy and filtering out too many genes for each site. These results were encouraging as they demonstrated that both sites retained a high level of technical precision to accurately quantify ERCC1/2 spike-in expression changes at relatively low CPM. However, differences remained with Site 1 requiring stronger filtering than Site 2 due to the slightly lower sequencing depth and increased noise (one globally outlying sample was identified) for Site 1. This highlights the usefulness of internal ERRC1/2 spike-ins for estimating empirical gene expression filtering cutoffs accounting for systematic experimental differences across sites. This strategy for determining the LCPM cutoff was implemented in the 