Evidence map›Paper›PMID 22523482›Full record

ArticleJournal of statistical software2011

Using the R Package crlmm for Genotyping and Copy Number Estimation.

Robert B Scharpf, Rafael A Irizarry, Matthew E Ritchie, Benilton Carvalho, Ingo Ruczinski

Abstract read
In one paragraph

Article in Journal of statistical software, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Robert B ScharpfDepartment of Oncology, Johns Hopkins University School of Medicine, 550 N. Broadway, Suite 1103, Baltimore, MD 21218, United States of America.
Rafael A Irizarry
Matthew E Ritchie
Benilton Carvalho
Ingo Ruczinski

Funding

Preprocessing and Analysis Tools for High-Throughput TechnologiesR01GM083084 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2007 to 2018
$4.4M
Software for the Statistical Analysis of Microarray Probe Level DataR01RR021967 · NCRR · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2007 to 2011
$1.2M
Statistical methods for assessing copy number variation in SNP arraysR00HG005015 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SCHARPF, ROBERT B. · 2010 to 2012
$741k
Software for the statistical analysis of microarray probe level dataR01GM103552 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2012 to 2013
$735k
NCRR NIH HHS R01 RR021967NHGRI NIH HHS R00 HG005015NIGMS NIH HHS R01 GM083084NIGMS NIH HHS R01 GM103552
6 · The paper itself

Abstract

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number and integration of the marker-level estimates with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R.

Identifiers

PMID22523482
PMCPMC3329223

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.