RT Journal Article SR Electronic T1 Cell-ID: gene signature extraction and cell identity recognition at individual cell level JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.07.23.215525 DO 10.1101/2020.07.23.215525 A1 Akira, Cortal A1 Loredana, Martignetti A1 Emmanuelle, Six A1 Antonio, Rausell YR 2020 UL http://biorxiv.org/content/early/2020/07/23/2020.07.23.215525.abstract AB The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique cell identity cards for every cell in the body. However, the stochasticity associated with high-throughput single-cell RNA sequencing has made it necessary to use clustering-based computational approaches in which the transcriptional characterization of cell-type heterogeneity is performed at cell-subpopulation level rather than at full single-cell resolution. We present here Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. Cell-ID allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. Cell-ID is distributed as an open-source R software package: https://github.com/RausellLab/CelliD.Competing Interest StatementThe authors have declared no competing interest.