For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
10
views
16
references
 Top references
cited by
16
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      458
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

      research-article
      Author(s): 1 , , 2 , 3
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central
      Keywords: Gene expression, Single cell, RNA-seq, Normalization, Quasi-UMI

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

          Related collections

          Most cited references16

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

          Alex Shalek, Rahul Satija, Xian Adiconis … (2013)
          Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
          Article 0 comments Cited 516 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Quantitative single-cell RNA-seq with unique molecular identifiers.

            Saiful Islam, Amit Zeisel, Simon Joost … (2014)
            Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
            Article 0 comments Cited 510 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A cellular census of human lungs identifies novel cell states in health and in asthma

              Felipe A. Vieira Braga, Gozde Kar, Marijn Berg … (2019)
              Human lungs enable efficient gas exchange and form an interface with the environment, which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single-cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in healthy lungs, and lower airways in asthmatic lungs. We report location-dependent airway epithelial cell states and a novel subset of tissue-resident memory T cells. In the lower airways of patients with asthma, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report the presence of pathogenic effector type 2 helper T cells (TH2) in asthmatic lungs and find evidence for type 2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell-cell interactions identifies a shift from airway structural cell communication in healthy lungs to a TH2-dominated interactome in asthmatic lungs.
              Article 0 comments Cited 369 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                F. William Townes: ftownes@princeton.edu
                Rafael A. Irizarry: rafa@ds.dfci.harvard.edu
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 3 July 2020
                Publication date PMC-release: 3 July 2020
                Publication date Collection: 2020
                Volume: 21
                Electronic Location Identifier: 160
                Affiliations
                [1 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Department of Computer Science, Princeton University, ; Princeton, NJ USA
                [2 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Department of Data Sciences, Dana-Farber Cancer Institute, ; Boston, MA USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, Harvard University, ; Cambridge, MA USA
                Article
                Publisher ID: 2078
                DOI: 10.1186/s13059-020-02078-0
                PMC ID: 7333325
                PubMed ID: 32620142
                SO-VID: 7c053284-dbe2-4ea0-a21c-404e959fd644
                Copyright © © The Author(s) 2020
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 18 December 2019
                Date accepted : 19 June 2020
                Categories
                Subject: Method
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Genetics
                Keywords: gene expression,single cell,rna-seq,normalization,quasi-umi
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: gene expression, single cell, rna-seq, normalization, quasi-umi

                Comments

                Comment on this article

                scite_

                Similar content458

                See all similar

                Cited by16

                See all cited by

                Most referenced authors1,149

                See all reference authors