Normalizing for Individual Cell Population Context in the
Analysis of High-Content Cellular Screens

J 2011

Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens

KNAPP, Bettina, Ilka REBHAN, Anil KUMAR, Petr MATULA, Narsis A KIANI et. al.

Basic information

Original name

Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens

Authors

KNAPP, Bettina (276 Germany), Ilka REBHAN (276 Germany), Anil KUMAR (356 India), Petr MATULA (203 Czech Republic, guarantor, belonging to the institution), Narsis A KIANI (364 Islamic Republic of Iran), Marco BINDER (276 Germany), Hoger ERFLE (276 Germany), Karl ROHR (276 Germany), Roland EILS (276 Germany), Ralf BARTENSCHLAGER (276 Germany) and Lars KADERALI (276 Germany)

Edition

BMC Bioinformatics, BioMed Central, 2011, 1471-2105

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10103 Statistics and probability

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.751

RIV identification code

RIV/00216224:14330/11:00054378

Organization unit

Faculty of Informatics

UT WoS

000299110500001

Keywords in English

high-content screening; normalization; cell-based analysis

Abstract

V originále

We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell’s individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a nonvirus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.

Links

MSM0021622419, plan (intention)

Name: Vysoce paralelní a distribuované výpočetní systémy

Investor: Ministry of Education, Youth and Sports of the CR, Highly Parallel and Distributed Computing Systems

2B06052, research and development project

Name: Vytipování markerů, screening a časná diagnostika nádorových onemocnění pomocí vysoce automatizovaného zpracování multidimenzionálních biomedicínských obrazů (Acronym: Biomarker)

Investor: Ministry of Education, Youth and Sports of the CR, Determination of markers, screening and early diagnostics of cancer diseases using highly automated processing of multidimensional biomedical images

Citovat

KNAPP, Bettina, Ilka REBHAN, Anil KUMAR, Petr MATULA, Narsis A KIANI, Marco BINDER, Hoger ERFLE, Karl ROHR, Roland EILS, Ralf BARTENSCHLAGER and Lars KADERALI. Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens. BMC Bioinformatics. BioMed Central, 2011, vol. 12, No 485, p. 1-14. ISSN 1471-2105.

@article{962192,
   author = {Knapp, Bettina and Rebhan, Ilka and Kumar, Anil and Matula, Petr and Kiani, Narsis A and Binder, Marco and Erfle, Hoger and Rohr, Karl and Eils, Roland and Bartenschlager, Ralf and Kaderali, Lars},
   article_number = {485},
   keywords = {high-content screening; normalization; cell-based analysis},
   language = {eng},
   issn = {1471-2105},
   journal = {BMC Bioinformatics},
   title = {Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens},
   url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf},
   volume = {12},
   year = {2011}
}

TY  - JOUR
ID  - 962192
AU  - Knapp, Bettina - Rebhan, Ilka - Kumar, Anil - Matula, Petr - Kiani, Narsis A - Binder, Marco - Erfle, Hoger - Rohr, Karl - Eils, Roland - Bartenschlager, Ralf - Kaderali, Lars
PY  - 2011
TI  - Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens
JF  - BMC Bioinformatics
VL  - 12
IS  - 485
SP  - 1-14
EP  - 1-14
PB  - BioMed Central
SN  - 14712105
KW  - high-content screening
KW  - normalization
KW  - cell-based analysis
UR  - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf
L2  - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3259109/pdf/1471-2105-12-485.pdf
N2  - We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell’s individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a nonvirus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.
ER  -

KNAPP, Bettina, Ilka REBHAN, Anil KUMAR, Petr MATULA, Narsis A KIANI, Marco BINDER, Hoger ERFLE, Karl ROHR, Roland EILS, Ralf BARTENSCHLAGER and Lars KADERALI. Normalizing for Individual Cell Population Context in the Analysis of High-Content Cellular Screens. \textit{BMC Bioinformatics}. BioMed Central, 2011, vol.~12, No~485, p.~1-14. ISSN~1471-2105.

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