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Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings

It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researc...

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Autores principales: Cai, Hao, Li, Xiangyu, Li, Jing, Liang, Qirui, Zheng, Weicheng, Guan, Qingzhou, Guo, Zheng, Wang, Xianlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036750/
https://www.ncbi.nlm.nih.gov/pubmed/29989020
http://dx.doi.org/10.7150/ijbs.24548
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author Cai, Hao
Li, Xiangyu
Li, Jing
Liang, Qirui
Zheng, Weicheng
Guan, Qingzhou
Guo, Zheng
Wang, Xianlong
author_facet Cai, Hao
Li, Xiangyu
Li, Jing
Liang, Qirui
Zheng, Weicheng
Guan, Qingzhou
Guo, Zheng
Wang, Xianlong
author_sort Cai, Hao
collection PubMed
description It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments.
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spelling pubmed-60367502018-07-09 Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings Cai, Hao Li, Xiangyu Li, Jing Liang, Qirui Zheng, Weicheng Guan, Qingzhou Guo, Zheng Wang, Xianlong Int J Biol Sci Research Paper It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments. Ivyspring International Publisher 2018-05-22 /pmc/articles/PMC6036750/ /pubmed/29989020 http://dx.doi.org/10.7150/ijbs.24548 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Cai, Hao
Li, Xiangyu
Li, Jing
Liang, Qirui
Zheng, Weicheng
Guan, Qingzhou
Guo, Zheng
Wang, Xianlong
Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title_full Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title_fullStr Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title_full_unstemmed Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title_short Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
title_sort identifying differentially expressed genes from cross-site integrated data based on relative expression orderings
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036750/
https://www.ncbi.nlm.nih.gov/pubmed/29989020
http://dx.doi.org/10.7150/ijbs.24548
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