Rna Sequencing

“Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data” by Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter. https://doi.org/10.1101/534834 I selected this article for review for four reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit. I was curious to learn more about methods related to infer gene regulatory networks using single-cell data. I was curious to learn how the authors were benchmarking their results from the network reconstruction algorithm. »

Update: This paper has now been published at Nature Biotech “Panoramic stitching of heterogeneous single-cell transcriptomic data” by Brian L Hie, Bryan Bryson, Bonnie Berger. https://doi.org/10.1101/371179 I selected this article for review for three reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit. Methods that can integrate two or more scRNA-seq data sets (across experiments, conditions, treatments, etc) are in high demand and are actively being worked on by many groups. »

Update: This paper has now been published at Nature Communications. “Single cell RNA-seq denoising using a deep count autoencoder” by Gokcen Eraslan, Lukas M. Simon (both first-authors contributed equally), Maria Mircea, Nikola S. Mueller, Fabian J. Theis. https://doi.org/10.1101/300681 I selected this article for review for two reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit and I have a general interest in methods that remove technical sources of variation from it. »

Update: This paper has now been published at Nature Biotechnology. “Human 5’ UTR design and variant effect prediction from a massively parallel translation assay” by Paul J. Sample, Ban Wang, David W. Reid, Vlad Presnyak, Iain McFadyen, David R. Morris, and Georg Seelig. https://doi.org/10.1101/310375 I selected this article for review for two reasons: I am excited to learn about applications of massively parallel reporter assays (MPRAs). I have a general interest in methods that use deep learning (in this case convolutional neural networks) with genomic data. »

Update: This paper has now been published at Molecular Systems Biology. “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. »