  Direct cDNA preamplification protocols developed for single-cell RNA-seq have
enabled transcriptome profiling of precious clinical samples and rare cells
without sample pooling or RNA extraction. Currently, there is no algorithm
optimized to reveal and remove noisy transcripts in limiting-cell RNA-seq
(lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflow
that identifies reliably quantifiable transcripts in lcRNA-seq data for
differentially expressed gene (DEG) analysis. Libraries at three input amounts
of FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patient
were used to develop CLEAR. When using CLEAR transcripts vs. using all
transcripts, downstream analyses revealed more shared transcripts across
different 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 and
for single cell analyses.
