TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F15%3A00083345" target="_blank" >RIV/00216224:14110/15:00083345 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1515/sagmb-2014-0093" target="_blank" >http://dx.doi.org/10.1515/sagmb-2014-0093</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/sagmb-2014-0093" target="_blank" >10.1515/sagmb-2014-0093</a>

Jazyk výsledku

angličtina

Název v původním jazyce

TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Popis výsledku v původním jazyce

High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of thesevaluable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format - rankings - for the same set of items.

Název v anglickém jazyce

TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Popis výsledku anglicky

High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of thesevaluable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format - rankings - for the same set of items.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

CE - Biochemie

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY

ISSN

2194-6302

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

6

Strana od-do

311-316

Kód UT WoS článku

000355417300008

EID výsledku v databázi Scopus

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