Batch effects and the effective design of single-cell gene expression studies. - Wikidata - wikimedia - TriplyDB

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

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

http://www.wikidata.org/entity/Q37550421

about

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.Gene length and detection bias in single cell RNA sequencing protocols.Comprehensive single-cell transcriptional profiling of a multicellular organism.Delineating biological and technical variance in single cell expression data Single-Cell Genomics: Approaches and Utility in Immunology.Single-Cell mRNA Sequencing in Cancer Research: Integrating the Genomic Fingerprint.Splatter: simulation of single-cell RNA sequencing data.Single-Cell Landscape of Transcriptional Heterogeneity and Cell Fate Decisions during Mouse Early Gastrulation.Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis.Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data.Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017.Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress.SCPortalen: human and mouse single-cell centric database.A general and flexible method for signal extraction from single-cell RNA-seq data.The Human Cell Atlas: Technical approaches and challenges.Experimental design for single-cell RNA sequencing.Single-cell RNA sequencing reveals developmental heterogeneity of blastomeres during major genome activation in bovine embryos.Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.BCseq: accurate single cell RNA-seq quantification with bias correction.UMI-count modeling and differential expression analysis for single-cell RNA sequencing.A systematic performance evaluation of clustering methods for single-cell RNA-seq data Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data Experimental Considerations for Single-Cell RNA Sequencing Approaches

P2860

Q31146507-4C6F158B-F096-44F1-A29B-773F22BC5CCB Q36320205-A9BD3431-221A-474C-987C-540AD500352D Q36377347-877AD2F4-91C7-49D7-906A-68D60A308EE6 Q38619221-C2537D9A-DED4-444C-A566-705244976CF7 Q38675040-A062F952-ABFD-4CBB-9BCE-73F96D50A3ED Q38769681-0ED4EFB5-910F-4311-9A86-BDEB8CBB7C82 Q39377159-76BAB985-674B-48FC-862F-67530C12DFDB Q41152743-9055C0FA-C773-436D-978C-A2C0E5FC720F Q41373461-4BDB000B-DC9B-4B00-A308-5786CFB9E655 Q42777634-AA00D662-029B-417B-98F0-2F7FFA52B900 Q42778181-ACC7FAA4-B0B3-45F6-8C94-8AB151D05B1D Q43492548-3EC2592C-87DE-4246-A028-908E2348861A Q46094386-D96EF983-6678-48CE-8610-58DA388E40F2 Q46177879-5AB1B529-C1F3-4E46-904B-9AB60DD3BE25 Q46514101-151309E6-9C37-4CFF-B68E-48DA296FEB3B Q47143140-F6B66642-85EF-4CB5-B140-DE8B798A19B9 Q47559173-13D53EBA-D9DB-4665-9DE5-FB355DA63527 Q48563763-12323976-9863-4FE1-BB12-D25C39A139A2 Q50068010-93A77269-86AD-432A-B6A1-E2B848DFC2A9 Q50420616-1647E2BC-D8E1-434E-959F-D4B8B3E00DFF Q52571727-9F21BA61-CD0D-471C-9B2B-F85225BAAED6 Q52714789-38292023-8CFC-48DB-BA25-19E64EDFED88 Q55317542-7D260EB8-9589-4A07-B594-93832B236B57 Q56879988-0A785A5C-CB40-4CBE-93F5-E41CA23D0DCF Q57235290-16364EAD-272A-4249-878E-44FD13731D37 Q58694528-A94F4A60-6E5E-4F3F-A467-E01D822B5B56 Q58764868-922DEBBC-87A4-41D4-9948-4421A223C79B

P2860

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

http://www.wikidata.org/entity/Q37550421

description

article científic

@ca

article scientifique

@fr

articolo scientifico

@it

artigo científico

@pt

bilimsel makale

@tr

scientific article published on 03 January 2017

@en

vedecký článok

@sk

vetenskaplig artikel

@sv

videnskabelig artikel

@da

vědecký článek

@cs

name

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

@en

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

@nl

type

label

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

@en

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

@nl

prefLabel

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

@en

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

@nl

P1433

Q37550421-01C3E33F-38D3-42C4-BDC5-AAFE2FBDC1D6

P1476

Q37550421-4C42F7E9-9418-46E9-B5E1-5F82584E1F32

P2093

Q37550421-0FB8105F-2E08-410A-8995-235164FAB301

Q37550421-25ECCD59-734B-4CBD-8F73-4E6A956E74DF

Q37550421-8B254287-EE18-4077-ACAA-5BEAB82EEBDC

Q37550421-A48769BB-F1EC-44B9-AEC1-59B3A4CD248A

Q37550421-DDF0AD74-198D-4876-B833-78A3201650D4

Q37550421-FBB0B426-C713-4211-A43F-122A9DF2A156

P2860

Q37550421-11CFE3BF-7E43-4357-94E0-477EAAD0E291

Q37550421-2A84AAAA-A5F9-4EF3-B582-9733AEF8AE0A

Q37550421-2C9525D7-707E-4829-9C1E-704B35D77214

Q37550421-340681EE-FF27-4702-9F3F-D241E763431E

Q37550421-3C990DCF-14F5-45C3-90C0-1810F3055915

Q37550421-3D96FA36-2995-4BCA-A199-3300529BAB5D

Q37550421-3F2A2C1A-BE08-41B4-B9EC-2A9AAFC1A82D

Q37550421-3F4CC9B6-4B6B-4FCC-AFF7-0ADA5FD269AF

Q37550421-4C299EEF-7ED6-4CD6-83EA-2F62B9E7AD4B

Q37550421-5823D57B-E12E-4085-9077-9CDE04828034

P2888

Q37550421-EE25D6C0-0ACE-4205-8227-3E0236A6A881

P304

Q37550421-9E6ACF52-F3B1-4684-8DCA-78089509519D

P31

Q37550421-17E471BC-B5DF-4B13-81E8-2CA555661B15

P356

Q37550421-1B0C6584-A671-48AF-8EEA-B7F4D56B2328

P407

Q37550421-96F4E3A3-132A-403C-856E-43AB00C60237

P478

Q37550421-C8519B8C-7DB3-40CA-B3B7-D7F8917FB0E0

P50

Q37550421-39DC4B1F-AA2B-4014-A883-B6AA1165FA4C

P577

Q37550421-7934DCC9-FB87-452B-B8D7-4723D4D2AC71

P698

Q37550421-F1EA1915-4F80-4268-8E41-0C0BC739CD81

P932

Q37550421-DE3C8CAA-0353-4D14-AEC2-8D98F16FBCEE

P356

P1433

Scientific Reports

P1476

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

@en

P2093

Chiaowen Joyce Hsiao

http://www.w3.org/2001/XMLSchema#string

David A Knowles

http://www.w3.org/2001/XMLSchema#string

Jonathan E Burnett

http://www.w3.org/2001/XMLSchema#string

Jonathan K Pritchard

http://www.w3.org/2001/XMLSchema#string

Po-Yuan Tung

http://www.w3.org/2001/XMLSchema#string

Yoav Gilad

http://www.w3.org/2001/XMLSchema#string

P2860

Single cell genomics: advances and future perspectives

An integrated map of genetic variation from 1,092 human genomes

Noise in gene expression: origins, consequences, and control.

Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq

ConsensusPathDB: toward a more complete picture of cell biology

A haplotype map of the human genome

Single-cell RNA-seq: advances and future challenges

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

The Sequence Alignment/Map format and SAMtools

Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise

P2888

P304

39921

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1038/SREP39921

http://www.w3.org/2001/XMLSchema#string

P407

P478

7

http://www.w3.org/2001/XMLSchema#string

P50

John D Blischak

P577

2017-01-03T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

28045081

http://www.w3.org/2001/XMLSchema#string

P932

5206706

http://www.w3.org/2001/XMLSchema#string