Open AccessReproducibility across single-cell RNA-seq protocols for spatial ordering analysis
Author(s) -
Morten Seirup,
LiFang Chu,
Srikumar Sengupta,
Ning Leng,
Hadley Browder,
Kevin Kapadia,
Christina M. Shafer,
Bret Duffin,
Angela L. Elwell,
Jennifer M. Bolin,
Scott Swanson,
Ron Stewart,
Christina Kendziorski,
James A. Thomson,
Rhonda Bacher
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0239711
Subject(s) - protocol (science) , computer science , rna seq , single cell analysis , context (archaeology) , computational biology , reproducibility , data mining , biology , gene , cell , transcriptome , gene expression , genetics , mathematics , statistics , medicine , paleontology , alternative medicine , pathology
As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq’s higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.