English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Nested effects models for high-dimensional phenotyping screens.

Markowetz, F., Kostka, D., Troyanskaya, O. G., & Soang, R. (2007). Nested effects models for high-dimensional phenotyping screens. Bioinformatics, 23(13), i305-i312. doi:10.1093/bioinformatics/btm178.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-82C4-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-82C5-1
Genre: Journal Article
Alternative Title : Bioinformatics

Files

show Files
hide Files
:
i305.pdf (Any fulltext), 190KB
View   Save
File Permalink:
https://hdl.handle.net/11858/00-001M-0000-0010-82C3-5
Name:
i305.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
eDoc_access: PUBLIC
License:
-

Locators

show

Creators

show
hide
 Creators:
Markowetz, Florian1, Author
Kostka, Dennis1, Author
Troyanskaya, Olga G., Author
Soang, Rainer1, Author
Affiliations:
1Max Planck Society, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: Motivation: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and high-dimensional phenotyping screens. Results: We introduce and compare probabilistic methods to efficiently infer a genetic hierarchy from the nested structure of observed perturbation effects. These hierarchies elucidate the structures of signaling pathways and regulatory networks. Our methods achieve two goals: (1) they reveal clusters of genes with highly similar phenotypic profiles, and (2) they order (clusters of) genes according to subset relationships between phenotypes. We evaluate our algorithms in the controlled setting of simulation studies and show their practical use in two experimental scenarios: (1) a data set investigating the response to microbial challenge in Drosophila melanogaster, and (2) a compendium of expression profiles of Saccharomyces cerevisiae knockout strains. We show that our methods identify biologically justified genetic hierarchies of perturbation effects. Availability: The software used in our analysis is freely available in the R package ‘nem’ from www.bioconductor.org

Details

show
hide
Language(s): eng - English
 Dates: 2007
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 336055
DOI: 10.1093/bioinformatics/btm178
URI: http://bioinformatics.oxfordjournals.org/cgi/reprint/23/13/i305?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=1&title=Nested+effects+models+for+high-dimensional+phenotyping+screens&andorexacttitle=and&andorexacttitleabs=and&andorexactfulltext=and&searchid=1&FIRSTINDEX=0&sortspec=relevance&fdate=//&resourcetype=HWCIT
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Bioinformatics
  Alternative Title : Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 23 (13) Sequence Number: - Start / End Page: i305 - i312 Identifier: ISSN: 1367-4803