Literature DB >> 29608555

scmap: projection of single-cell RNA-seq data across data sets.

Vladimir Yu Kiselev1, Andrew Yiu1, Martin Hemberg1.   

Abstract

Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.

Mesh:

Year:  2018        PMID: 29608555     DOI: 10.1038/nmeth.4644

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  25 in total

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2.  Multilineage communication regulates human liver bud development from pluripotency.

Authors:  J Gray Camp; Keisuke Sekine; Tobias Gerber; Henry Loeffler-Wirth; Hans Binder; Malgorzata Gac; Sabina Kanton; Jorge Kageyama; Georg Damm; Daniel Seehofer; Lenka Belicova; Marc Bickle; Rico Barsacchi; Ryo Okuda; Emi Yoshizawa; Masaki Kimura; Hiroaki Ayabe; Hideki Taniguchi; Takanori Takebe; Barbara Treutlein
Journal:  Nature       Date:  2017-06-14       Impact factor: 49.962

3.  Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells.

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4.  RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes.

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Journal:  Cell Metab       Date:  2016-09-22       Impact factor: 27.287

5.  Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.

Authors:  Karthik Shekhar; Sylvain W Lapan; Irene E Whitney; Nicholas M Tran; Evan Z Macosko; Monika Kowalczyk; Xian Adiconis; Joshua Z Levin; James Nemesh; Melissa Goldman; Steven A McCarroll; Constance L Cepko; Aviv Regev; Joshua R Sanes
Journal:  Cell       Date:  2016-08-25       Impact factor: 41.582

6.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

Authors:  Cole Trapnell; Davide Cacchiarelli; Jonna Grimsby; Prapti Pokharel; Shuqiang Li; Michael Morse; Niall J Lennon; Kenneth J Livak; Tarjei S Mikkelsen; John L Rinn
Journal:  Nat Biotechnol       Date:  2014-03-23       Impact factor: 54.908

7.  Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor.

Authors:  Megan Crow; Anirban Paul; Sara Ballouz; Z Josh Huang; Jesse Gillis
Journal:  Nat Commun       Date:  2018-02-28       Impact factor: 14.919

8.  Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells.

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Journal:  Cell       Date:  2016-10-06       Impact factor: 41.582

9.  Batch effects and the effective design of single-cell gene expression studies.

Authors:  Po-Yuan Tung; John D Blischak; Chiaowen Joyce Hsiao; David A Knowles; Jonathan E Burnett; Jonathan K Pritchard; Yoav Gilad
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10.  Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.

Authors:  Åsa Segerstolpe; Athanasia Palasantza; Pernilla Eliasson; Eva-Marie Andersson; Anne-Christine Andréasson; Xiaoyan Sun; Simone Picelli; Alan Sabirsh; Maryam Clausen; Magnus K Bjursell; David M Smith; Maria Kasper; Carina Ämmälä; Rickard Sandberg
Journal:  Cell Metab       Date:  2016-09-22       Impact factor: 27.287

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  164 in total

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2.  Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization.

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Journal:  Nucleic Acids Res       Date:  2019-07-26       Impact factor: 16.971

3.  Comprehensive Integration of Single-Cell Data.

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4.  Complementary networks of cortical somatostatin interneurons enforce layer specific control.

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Journal:  Elife       Date:  2019-03-18       Impact factor: 8.140

5.  LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection.

Authors:  Travis S Johnson; Tongxin Wang; Zhi Huang; Christina Y Yu; Yi Wu; Yatong Han; Yan Zhang; Kun Huang; Jie Zhang
Journal:  Bioinformatics       Date:  2019-11-01       Impact factor: 6.937

6.  Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID.

Authors:  Akira Cortal; Loredana Martignetti; Emmanuelle Six; Antonio Rausell
Journal:  Nat Biotechnol       Date:  2021-04-29       Impact factor: 54.908

Review 7.  Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods.

Authors:  Zoe A Clarke; Tallulah S Andrews; Jawairia Atif; Delaram Pouyabahar; Brendan T Innes; Sonya A MacParland; Gary D Bader
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8.  SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.

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Journal:  Cell Syst       Date:  2019-07-31       Impact factor: 10.304

9.  Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT.

Authors:  Bushra Raj; James A Gagnon; Alexander F Schier
Journal:  Nat Protoc       Date:  2018-11       Impact factor: 13.491

Review 10.  Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

Authors:  Megan Crow; Jesse Gillis
Journal:  Trends Genet       Date:  2018-08-23       Impact factor: 11.639

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