A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations.
BACKGROUND: The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, bu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3520972?pdf=render |
| _version_ | 1811259498715676672 |
|---|---|
| author | David Venet Vincent Detours Hugues Bersini |
| author_facet | David Venet Vincent Detours Hugues Bersini |
| author_sort | David Venet |
| collection | DOAJ |
| description | BACKGROUND: The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies. RESULTS: As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the "Signal-to-Noise Applied to Gene Expression Experiments", or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results. CONCLUSIONS: We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor. |
| first_indexed | 2024-04-12T18:31:26Z |
| format | Article |
| id | doaj.art-b2907a9a619e45a0b2d9a54014997998 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T18:31:26Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b2907a9a619e45a0b2d9a540149979982022-12-22T03:21:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01712e5101310.1371/journal.pone.0051013A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations.David VenetVincent DetoursHugues BersiniBACKGROUND: The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies. RESULTS: As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the "Signal-to-Noise Applied to Gene Expression Experiments", or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results. CONCLUSIONS: We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor.http://europepmc.org/articles/PMC3520972?pdf=render |
| spellingShingle | David Venet Vincent Detours Hugues Bersini A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. PLoS ONE |
| title | A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. |
| title_full | A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. |
| title_fullStr | A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. |
| title_full_unstemmed | A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. |
| title_short | A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations. |
| title_sort | measure of the signal to noise ratio of microarray samples and studies using gene correlations |
| url | http://europepmc.org/articles/PMC3520972?pdf=render |
| work_keys_str_mv | AT davidvenet ameasureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations AT vincentdetours ameasureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations AT huguesbersini ameasureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations AT davidvenet measureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations AT vincentdetours measureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations AT huguesbersini measureofthesignaltonoiseratioofmicroarraysamplesandstudiesusinggenecorrelations |