Panoramic stitching of heterogeneous single-cell transcriptomic data

“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. I was intrigued about the idea of drawing “inspiration from computer vision algorithms for panorama stitching that identify images with … overlapping content, which are then merged into a larger panorama”. »

Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0

“Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0” Dylan M Nielson, Francisco Pereira, Charles Y Zheng, Nino Migineishvili, John A Lee, Adam G Thomas, Peter A Bandettini bioRxiv 309260; doi: https://doi.org/10.1101/309260 I have selected the manuscript this article to be featured in biOverlay because it discusses an increasingly important topic of statistical issues involved in combining data from multiple sources. The authors used a large dataset produced by the ABCD consortium that spans multiple sites and scanners. »

Analysis and Correction of Inappropriate Image Duplication: The Molecular and Cellular Biology Experience

“Analysis and Correction of Inappropriate Image Duplication: The Molecular and Cellular Biology Experience” by Arturo Casadevall, Elisabeth M Bik, Ferric C Fang, Amy Kullas, Roger J Davis. https://doi.org/10.1101/354621 [Ed note: we’re using the bioRxiv listing information because that’s what we sent for review, but the author order has changed on the published paper and on that Dr. Bik is first author.] I selected this article for review for a few reasons: »

Mapping DNA sequence to transcription factor binding energy in vivo

“Mapping DNA sequence to transcription factor binding energy in vivo” by Stephanie L Barnes, Nathan M Belliveau, William T Ireland, Justin B Kinney and Robert Phillips https://doi.org/10.1101/331124 In this article, Barnes and colleagues constructed a libraries of strains in which GFP expression is controlled by a transcription factor, and the transcription factor (TF) binding sites were randomly mutated. Libraries were sorted by GFP fluorescence and sequenced to determine the likelihood that a given binding site corresponds to a range of fluroescence. »

Single cell RNA-seq denoising using a deep count autoencoder

“Single cell RNA-seq denoising using a deep count autoencode” 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. I have a general interest in methods that use machine learning approaches (in this case an autoencoder network) with genomic data. »