Multi-Omics factor analysis - a framework for unsupervised integration of multi-omic data sets

“Multi-Omics factor analysis - a framework for unsupervised integration of multi-omic data sets” by Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C. Marioni, Wolfgang Huber, Florian Buettner, and Oliver Stegle. https://doi.org/10.1101/217554 I selected this article for review for two reasons: Methods integrating multiple types of ‘Omics data are in great demand, both in bulk tissues and at the single-cell level. I have an interest in methods that use factor analysis to capture biological and technical sources of variation. »

DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia

“DeepProfile: Deep learning of patient molecular profiles for precision medicine in acute myeloid leukemia” by Ayse Berceste Dincer, Safiye Celik, Naozumi Hiranuma, and Su-In Lee. https://doi.org/10.1101/278739 I selected this article for review for a few reasons: I was intrigued by the compilation of a large dataset from many small datasets before applying unsupervised learning, which we have had success with in other settings. I have an interest in methods that use variational autoencoders to learn low-dimensional representations. »

Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma

“Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma” by Reuben Moncada, Marta Chiodin, Joseph C. Devlin, Maayan Baron, Cristina H. Hajdu, Diane Simeone, and Itai Yanai. https://doi.org/10.1101/254375 I selected this article for review for a few reasons: I thought the spatial aspects of the work were intriguing. I have an interest in methods that use single cell data to deconvolve bulk samples. I was curious about heterogeneity in this disease based on some of our previous work with FNA-derived PDX models. »