Revisit linear regression-based deconvolution methods for tumor gene expression data

We have recently published a statistical deconvolution method to study infiltrating immune cells using tumor RNA-seq data One of the goals in that work was to understand how proportions of different cell types covary across different cancer tissues. To this end, we estimated the abundance of six cell types over 9000 tumor samples across 23 cancer types, and then assessed the correlations of these estimated proportions across the different samples within a cancer type. In particular we compared our method (TIMER) with CIBERSORT [2], a previously published deconvolution approach, for their ability to assess such correlations. To our surprise, we found many non-biological negative correlations between CIBERSORT estimates, and we believed that this artifact was, to a large extent, due to the incorporation of highly similar features in the linear model, or statistical collinearity. Newman et al., the authors of CIBER-SORT, have raised concerns that these correlations were due to data normalization, instead of collinearity While we agree with Newman and coauthors that the forced normalization indeed introduces unwanted negative correlations, we will show in this response that the inclusion of highly similar features contributes as significantly as normalization, if not more, to the observed artificial negative correlations among the estimates obtained by CIBERSORT.