Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns

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 certaingenes are expressed only in specific cell-types.

Analysis of cell-type-specific gene expressionpatterns can provide insights into the relationship between genes, connectivity, brain regions, andcell-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 themouse brain by automated image computing of in situ hybridization (ISH) expression patterns.

Weapplied invariant image feature descriptors to capture local gene expression information fromcellular-resolution ISH images. We then built image-level representations by applying vectorquantization on the image descriptors.

We employed regularized learning methods for classifyinggenes specifically expressed in different brain cell-types. These methods can also rank imagefeatures based on their discriminative power.

We used a data set of 2,872 genes from the Allen BrainAtlas in the experiments. Results showed that our methods are predictive of cell-type-specificity ofgenes.

Our classifiers achieved AUC values of approximately 87% when the enrichment level is setto 20. In addition, we showed that the highly-ranked image features captured the relationshipbetween cell-types.

Conclusions:
Overall, our results showed that automated image computing methods could potentially be used toidentify cell-type-specific genes in the mouse brain.

Author: Rongjian LiWenlu ZhangShuiwang Ji
Credits/Source: BMC Bioinformatics 2014, 15:209

Published on: 2014-06-20

News Provider: 7thSpace Interactive

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