CQN protocols

CQN specifications

Information


Unique identifier OMICS_01949
Name CQN
Alternative name Conditional Quantile Normalization
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


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Documentation


Maintainers


  • person_outline Kasper Daniel Hansen <>
  • person_outline Zhijin Wu <>

Publication for Conditional Quantile Normalization

CQN IN pipelines

 (13)
2018
PMCID: 5768803
PMID: 29335477
DOI: 10.1038/s41598-017-17735-x

[…] tophat (1.3.3) and bowtie (0.12.7). quality control and normalization of the mrna-sequencing gene counts data are as described by ovsyannikova et al.51 briefly, gene counts were normalized using conditional quantile normalization52, and 14,197 genes with at least 32 counts at one of our three timepoints (day 0, 3, or 28) were used in subsequent analyses., weighted gene coexpression network […]

2017
PMCID: 5428070
PMID: 28282965
DOI: 10.1038/s41598-017-00267-9

[…] mm9) with the splice junction-aware short-read alignment tool tophat (version 2.1.0)36. we restricted tophat to only align to known transcript splice junctions. we used the bioconductor package conditional quantile normalization (cqn, version 1.6.0)37 to remove systematic biases due to gc-content and gene length coverage and used deseq2 (version 1.0.18)38 to perform differential expression […]

2017
PMCID: 5428070
PMID: 28282965
DOI: 10.1038/s41598-017-00267-9

[…] short-read alignment tool tophat (version 2.1.0)36. we restricted tophat to only align to known transcript splice junctions. we used the bioconductor package conditional quantile normalization (cqn, version 1.6.0)37 to remove systematic biases due to gc-content and gene length coverage and used deseq2 (version 1.0.18)38 to perform differential expression analyses. we considered a gene […]

2017
PMCID: 5689655
PMID: 29156765
DOI: 10.18632/oncotarget.20717

[…] field and declared to be expressed based on a median gene read count ≥ 10., to remove potential biases such as gc content and differences in sequencing depth, gene read counts were normalized using conditional quantile normalization [79]. to account for latent sources of non-genetic variation in gene expression, we applied principal components analysis (pca) to the complete normalized gene […]

2016
PMCID: 4762891
PMID: 26892004
DOI: 10.1038/ncomms10717

[…] for downstream analyses. transcript levels were quantified using gencode v18 gene models at the union gene model level using ht-seq counts67. we normalized these data for gc content biases using the cqn package in r (ref. 63), which resulted in log2(normalized fpkm) values, and ensured that there were no sample outliers with a summed sample correlation z-score>2 (ref. 68). genes not expressed […]

CQN institution(s)
Department of Biostatistics, Brown University, Providence, RI, USA
CQN funding source(s)
Supported by the National Institutes of Health (R01HG004059) and National Science Foundation (DBI-1054905).

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