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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
Version 1.8.0
Stability Stable
Requirements
R(>=2.10.0), stats, splines, edgeR, scales, preprocessCore, nor1mix, quantreg, mclust
Maintained Yes

Versioning


No version available

Documentation


Maintainers


  • person_outline Kasper Daniel Hansen
  • person_outline Zhijin Wu

Publication for Conditional Quantile Normalization

CQN citations

 (32)
call_split

Pheromone expression reveals putative mechanism of unisexuality in a saprobic ascomycete fungus

2018
PLoS One
PMCID: 5837088
PMID: 29505565
DOI: 10.1371/journal.pone.0192517
call_split See protocol

[…] odel per Million mapped reads) corrects for differences in expression levels between genes of different lengths and allows for accurate intra- and inter-sample comparisons []. Quantile normalization (CQN) was subsequently performed on the RPKM values in order to account for the expected technical variation specifically found in RNA-Seq data []. Baggerley’s Z test [] was used to test for expression […]

library_books

Transcriptomic signatures of cellular and humoral immune responses in older adults after seasonal influenza vaccination identified by data driven clustering

2018
Sci Rep
PMCID: 5768803
PMID: 29335477
DOI: 10.1038/s41598-017-17735-x

[…] using 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. Briefly, gene counts were normalized using Conditional Quantile Normalization, 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. […]

library_books

A comprehensive analysis of breast cancer microbiota and host gene expression

2017
PLoS One
PMCID: 5708741
PMID: 29190829
DOI: 10.1371/journal.pone.0188873

[…] We aligned RNA-Seq fastq files using TopHat (v1.3.3), mapped reads were used to obtain gene expression counts using HTSeq (v0.5.3p3) [, ]. Host gene expression counts were normalized with conditional quantile normalization to account for potential GC, and/or gene length biases []. Subtype specific host expression cohorts were inspected for outliers using calibration stress measures [] […]

library_books

CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre clinical models

2017
Sci Rep
PMCID: 5709354
PMID: 29192179
DOI: 10.1038/s41598-017-16747-x

[…] Klijn et al. colorectal, liver and stomach cancer cell line mRNA and non-coding RNA-sequencing counts were downloaded from ref., non-Entrez features were discarded and pre-processing was performed by conditional quantile normalization and variance stabilization using cqn and DESeq2. Gao et al. PDX RNA-sequencing FPKM values were retrieved from Supplementary Table  in ref.. GSE35144, GSE64392, GSE7 […]

call_split

Integration of RNA Seq data with heterogeneous microarray data for breast cancer profiling

2017
BMC Bioinformatics
PMCID: 5697344
PMID: 29157215
DOI: 10.1186/s12859-017-1925-0
call_split See protocol

[…] it [], tophat2 [], bowtie2 [], samtools [] and htseq [] have been used to obtain the read count for each gene. Once the read count files were obtained, the expression values were calculated using the cqn and the NOISeq R packages []. Fig. 2 […]

call_split

Network directed cis mediator analysis of normal prostate tissue expression profiles reveals downstream regulatory associations of prostate cancer susceptibility loci

2017
Oncotarget
PMCID: 5689655
PMID: 29156765
DOI: 10.18632/oncotarget.20717
call_split See protocol

[…] ion 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 []. To account for latent sources of non-genetic variation in gene expression, we applied principal components analysis (PCA) to the complete normalized gene express […]

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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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