Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1783338214418808832 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6036750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT caihao identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT lixiangyu identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT lijing identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT liangqirui identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT zhengweicheng identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT guanqingzhou identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT guozheng identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings AT wangxianlong identifyingdifferentiallyexpressedgenesfromcrosssiteintegrateddatabasedonrelativeexpressionorderings |