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Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns
BACKGROUND: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expre...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078975/ https://www.ncbi.nlm.nih.gov/pubmed/24947138 http://dx.doi.org/10.1186/1471-2105-15-209 |
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author | Li, Rongjian Zhang, Wenlu Ji, Shuiwang |
author_facet | Li, Rongjian Zhang, Wenlu Ji, Shuiwang |
author_sort | Li, Rongjian |
collection | PubMed |
description | BACKGROUND: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. RESULTS: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. CONCLUSIONS: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain. |
format | Online Article Text |
id | pubmed-4078975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40789752014-07-07 Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns Li, Rongjian Zhang, Wenlu Ji, Shuiwang BMC Bioinformatics Methodology Article BACKGROUND: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. RESULTS: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. CONCLUSIONS: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain. BioMed Central 2014-06-20 /pmc/articles/PMC4078975/ /pubmed/24947138 http://dx.doi.org/10.1186/1471-2105-15-209 Text en Copyright © 2014 Li et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Li, Rongjian Zhang, Wenlu Ji, Shuiwang Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title | Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title_full | Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title_fullStr | Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title_full_unstemmed | Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title_short | Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
title_sort | automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078975/ https://www.ncbi.nlm.nih.gov/pubmed/24947138 http://dx.doi.org/10.1186/1471-2105-15-209 |
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