title: PRODUCTION OF A PRELIMINARY QUALITY CONTROL PIPELINE FOR SINGLE NUCLEI RNA-SEQ AND ITS APPLICATION IN THE ANALYSIS OF CELL TYPE DIVERSITY OF POST-MORTEM HUMAN BRAIN NEOCORTEX.
abstract: Next generation sequencing of the RNA content of single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. However, the fact that the procedure begins with a relatively small amount of starting material, thereby pushing the limits of the laboratory procedures required, dictates that careful approaches for sample quality control (QC) are essential to reduce the impact of technical noise and sample bias in downstream analysis applications. Here we present a preliminary framework for sample level quality control that is based on the collection of a series of quantitative laboratory and data metrics that are used as features for the construction of QC classification models using random forest machine learning approaches. We've applied this initial framework to a dataset comprised of 2272 single nuclei RNA-seq results and determined that ~79% of samples were of high quality. Removal of the poor quality samples from downstream analysis was found to improve the cell type clustering results. In addition, this approach identified quantitative features related to the proportion of unique or duplicate reads and the proportion of reads remaining after quality trimming as useful features for pass/fail classification. The construction and use of classification models for the identification of poor quality samples provides for an objective and scalable approach to sc/nRNA-seq quality control.
authors.affiliation: J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA 92037, USA#Contributed equally to this work.
authors.forename: Brian
authors.initials: B
authors.lastname: Aevermann
authors.forename: Jamison
authors.initials: J
authors.lastname: McCorrison
authors.forename: Pratap
authors.initials: P
authors.lastname: Venepally
authors.forename: Rebecca
authors.initials: R
authors.lastname: Hodge
authors.forename: Trygve
authors.initials: T
authors.lastname: Bakken
authors.forename: Jeremy
authors.initials: J
authors.lastname: Miller
authors.forename: Mark
authors.initials: M
authors.lastname: Novotny
authors.forename: Danny N
authors.initials: DN
authors.lastname: Tran
authors.forename: Francisco
authors.initials: F
authors.lastname: Diezfuertes
authors.forename: Lena
authors.initials: L
authors.lastname: Christiansen
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.forename: Frank
authors.initials: F
authors.lastname: Steemers
authors.forename: Roger S
authors.initials: RS
authors.lastname: Lasken
authors.forename: E D
authors.initials: ED
authors.lastname: Lein
authors.forename: Nicholas
authors.initials: N
authors.lastname: Schork
authors.forename: Richard H
authors.initials: RH
authors.lastname: Scheuermann
mesh_terms: D001344:Autopsy; D015982:Bias; D002467:Cell Nucleus / Q000235:genetics; D019295:Computational Biology; D030561:Databases, Nucleic Acid; D003663:Decision Trees; D059014:High-Throughput Nucleotide Sequencing / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D019579:Neocortex / Q000166:cytology* / Q000378:metabolism*; D011786:Quality Control; D015138:RNA, Nuclear / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000592:standards / Q000706:statistics & numerical data*; D059010:Single-Cell Analysis; D012984:Software
chemical_list: D015138:RNA, Nuclear