Evidence map›Paper›PMID 19609370›Full record

ArticleThe annals of applied statistics2008

Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays.

Robert B Scharpf, Giovanni Parmigiani, Jonathan Pevsner, Ingo Ruczinski

Abstract read
In one paragraph

Article in The annals of applied statistics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 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

4 authors.

Robert B ScharpfDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.
Giovanni Parmigiani
Jonathan Pevsner
Ingo Ruczinski

Funding

CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
NICHD MENTAL RETARDATION RESEARCH CENTER CORE GRANTP30HD024061 · NICHD · KENNEDY KRIEGER CHILDRENS HOSPITAL · PI VALLE, DAVID · 1988 to 2013
$15.8M
Preprocessing and Analysis Tools for High-Throughput TechnologiesR01GM083084 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2007 to 2018
$4.4M
Adaptive Function Estimation for Genomic DataR01CA074841 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KOOPERBERG, CHARLES L · 2003 to 2006
$1.1M
Predoctoral Biostatistics Training in Genetics/GenomicsT32GM074906 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2006 to 2010
$905k
Regulation of Gene Expression in Down SyndromeR01HD046598 · NICHD · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI PEVSNER, JONATHAN · 2004 to 2006
$863k
HAZARD REGRESSION AND OTHER POLYNOMIAL SPLINE METHODSR29CA074841 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KOOPERBERG, CHARLES L · 1998 to 2002
$351k
NCI NIH HHS R01 CA074841NCI NIH HHS R29 CA074841NHLBI NIH HHS T32 HL007024NICHD NIH HHS P30 HD024061NICHD NIH HHS R01 HD046598NIGMS NIH HHS R01 GM083084NIGMS NIH HHS T32 GM074906
6 · The paper itself

Abstract

Chromosomal DNA is characterized by variation between individuals at the level of entire chromosomes (e.g. aneuploidy in which the chromosome copy number is altered), segmental changes (including insertions, deletions, inversions, and translocations), and changes to small genomic regions (including single nucleotide polymorphisms). A variety of alterations that occur in chromosomal DNA, many of which can be detected using high density single nucleotide polymorphism (SNP) microarrays, are linked to normal variation as well as disease and therefore of particular interest. These include changes in copy number (deletions and duplications) and genotype (e.g. the occurrence of regions of homozygosity). Hidden Markov models (HMM) are particularly useful for detecting such alterations, modeling the spatial dependence between neighboring SNPs. Here, we improve previous approaches that utilize HMM frameworks for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding measures of uncertainty when available. Using simulated and experimental data, we in particular demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package vanillaICE.

Identifiers

PMID19609370
PMCPMC2710854

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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.