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Direct cDNA preamplification protocols developed for single-cell RNA-seq haveenabled transcriptome profiling of precious clinical samples and rare cellswithout sample pooling or RNA extraction. Currently, there is no algorithmoptimized to reveal and remove noisy transcripts in limiting-cell RNA-seq(lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflowthat identifies reliably quantifiable transcripts in lcRNA-seq data fordifferentially expressed gene (DEG) analysis. Libraries at three input amountsof FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patientwere used to develop CLEAR. When using CLEAR transcripts vs. using alltranscripts, downstream analyses revealed more shared transcripts acrossdifferent input RNA amounts, improved Principal Component Analysis (PCA)separation, and yielded more DEGs between cell types. As proof-of-principle,CLEAR was applied to an in-house lcRNA-seq dataset and two public datasets.When imputation is used, CLEAR is also adaptable to large clinical studies andfor single cell analyses.

CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq