The study of transcriptomics, also referred to as expression profiling, examines the expression level of mRNAs in a given cell population, often using high-throughput techniques based on DNA microarray technology. The use of next-generation sequencing technology to study the transcriptome at the nucleotide level is known as RNA-Seq.

Although gene co-expression networks typically do not provide information about causality, emerging methods for differential coexpression analysis are enabling the identification of regulatory genes underlying various phenotypes.

The application of deep learning methods to transcriptomic data has the potential to enhance the accuracy and efficiency of tissue classification and cell state identification. Herein, we developed a multitask deep learning model for tissue classification combining publicly available whole transcriptomic (RNA-seq) datasets of non-neoplastic, neoplastic, and peri-neoplastic tissue to classify disease states, tissue origin, and neoplastic subclass. RNA-seq data from a total of 10,116 patient samples processed through a common pipeline were used for model training and validation. The model achieved 99% accuracy for disease state classification (ROC-AUC of 0.98) and 97% accuracy for tissue origin (ROC-AUC of 0.99). Moreover, the model achieved an accuracy of 92% (ROC-AUC 0.95) for neoplastic subclassification. This is the first multitask deep learning algorithm developed for tissue classification employing a uniform pipeline analysis of transcriptomic data with multiple tissue classifiers. This model serves as a framework for incorporating large transcriptomic datasets across conditions to facilitate clinical diagnosis and cell-based treatment strategies 1)

van Dam et al. introduced and guide researchers through a (differential) co-expression analysis. They provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and they explain how these can be used to identify genes with a regulatory role in disease. Furthermore, they discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis 2).

Hong J, Hachem LD, Fehlings MG. A deep learning model to classify neoplastic state and tissue origin from transcriptomic data. Sci Rep. 2022 Jun 11;12(1):9669. doi: 10.1038/s41598-022-13665-5. PMID: 35690622.
van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2018 Jul 20;19(4):575-592. doi: 10.1093/bib/bbw139. PubMed PMID: 28077403; PubMed Central PMCID: PMC6054162.
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