Machine learning reveals correlations of gene expression in RNA-Seq data


Shirley Pepke – The complexity of cancer has famously eluded conquering by modern medicine. Every tumor has many aberrations that drive its growth. As a result, treatments that target single vulnerabilities are typically of short-lived efficacy. After being diagnosed with advanced stage ovarian cancer in 2013, I wagered that what was needed was an algorithm capable of digesting and analyzing the complexity to provide a detailed view into the multitude of factors at work in a given tumor. To pursue this goal, I began a collaboration with Greg Ver Steeg, who specializes in analyzing big data, to bring state-of-the-art machine learning to bear on the recently released large-scale data from the Cancer Genome Atlas (TCGA).

mTim: Rapid and accurate transcript reconstruction from RNA-Seq data Machine Learning

Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have revolutionized transcriptome studies. A major motivation for RNA-Seq is to map the structure of expressed transcripts at nucleotide resolution. With accurate computational tools for transcript reconstruction, this technology may also become useful for genome (re-)annotation, which has mostly relied on de novo gene finding where gene structures are primarily inferred from the genome sequence. We developed a machine-learning method, called mTim (margin-based transcript inference method) for transcript reconstruction from RNA-Seq read alignments that is based on discriminatively trained hidden Markov support vector machines. In addition to features derived from read alignments, it utilizes characteristic genomic sequences, e.g. around splice sites, to improve transcript predictions. mTim inferred transcripts that were highly accurate and relatively robust to alignment errors in comparison to those from Cufflinks, a widely used transcript assembly method.

RNA-seq assistant - machine learning based methods to identify more transcriptional regulated genes


Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. ML-based differential network analysis has been applied to predict stress-responsive genes through learning the patterns of 32 expression characteristics of known stress-related genes.

Understanding Cancer using Machine Learning


As demonstrated by many researchers [1, 2], the use of Machine Learning (ML) in Medicine is nowadays becoming more and more important. Researchers are now using ML in applications such as EEG analysis and Cancer Detection/Analysis. For example, by examining biological data such as DNA methylation and RNA sequencing can then be possible to infer which genes can cause cancer and which genes can instead be able to suppress its expression. In this post, I will walk you through how I examined 9 different datasets about TCGA Liver, Cervical and Colon Cancer. All the datasets have been provided by the UCSC Xena (University of California, Santa Cruz website).

Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis Artificial Intelligence

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification.