HDTD: analyzing multi-tissue gene expression data
Motivation: By collecting multiple samples per subject, researchers can characterize intra-subject variation using physiologically relevant measurements such as gene expression profiling. This can yield important insights into fundamental biological questions ranging from cell type identity to tumou...
| Main Authors: | , , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
Oxford University Press
2016
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937203/ |
| id |
pubmed-4937203 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-49372032016-07-11 HDTD: analyzing multi-tissue gene expression data Touloumis, Anestis Marioni, John C. Tavaré, Simon Applications Notes Motivation: By collecting multiple samples per subject, researchers can characterize intra-subject variation using physiologically relevant measurements such as gene expression profiling. This can yield important insights into fundamental biological questions ranging from cell type identity to tumour development. For each subject, the data measurements can be written as a matrix with the different subsamples (e.g. multiple tissues) indexing the columns and the genes indexing the rows. In this context, neither the genes nor the tissues are expected to be independent and straightforward application of traditional statistical methods that ignore this two-way dependence might lead to erroneous conclusions. Herein, we present a suite of tools embedded within the R/Bioconductor package HDTD for robustly estimating and performing hypothesis tests about the mean relationship and the covariance structure within the rows and columns. We illustrate the utility of HDTD by applying it to analyze data generated by the Genotype-Tissue Expression consortium. Oxford University Press 2016-07-15 2016-06-07 /pmc/articles/PMC4937203/ /pubmed/27266441 http://dx.doi.org/10.1093/bioinformatics/btw224 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Touloumis, Anestis Marioni, John C. Tavaré, Simon |
| spellingShingle |
Touloumis, Anestis Marioni, John C. Tavaré, Simon HDTD: analyzing multi-tissue gene expression data |
| author_facet |
Touloumis, Anestis Marioni, John C. Tavaré, Simon |
| author_sort |
Touloumis, Anestis |
| title |
HDTD: analyzing multi-tissue gene expression data |
| title_short |
HDTD: analyzing multi-tissue gene expression data |
| title_full |
HDTD: analyzing multi-tissue gene expression data |
| title_fullStr |
HDTD: analyzing multi-tissue gene expression data |
| title_full_unstemmed |
HDTD: analyzing multi-tissue gene expression data |
| title_sort |
hdtd: analyzing multi-tissue gene expression data |
| description |
Motivation: By collecting multiple samples per subject, researchers can characterize intra-subject variation using physiologically relevant measurements such as gene expression profiling. This can yield important insights into fundamental biological questions ranging from cell type identity to tumour development. For each subject, the data measurements can be written as a matrix with the different subsamples (e.g. multiple tissues) indexing the columns and the genes indexing the rows. In this context, neither the genes nor the tissues are expected to be independent and straightforward application of traditional statistical methods that ignore this two-way dependence might lead to erroneous conclusions. Herein, we present a suite of tools embedded within the R/Bioconductor package HDTD for robustly estimating and performing hypothesis tests about the mean relationship and the covariance structure within the rows and columns. We illustrate the utility of HDTD by applying it to analyze data generated by the Genotype-Tissue Expression consortium. |
| publisher |
Oxford University Press |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937203/ |
| _version_ |
1613605863514177536 |