Data-Driven Identification Of Total RNA Expression Genes (Tregs) скачать в хорошем качестве

Data-Driven Identification Of Total RNA Expression Genes (Tregs)

Louise A. Huuki-Myers, Staff Scientist at the Lieber Institute for Brain Development, presented a short talk at the Bioconductor Conference 2022. Louise gave her presentation on Data-Driven Identification Of Total RNA Expression Genes (Tregs) For Estimation Of RNA Abundance In Heterogeneous Cell Types. Huuki-Myers started the presentation by sharing the motivation behind the team's project to study different cell types in the human brain using smFISH and RNAscape. How do we measure total RNA content of a cell if we can only observe a few genes at a time? She continued by sharing TREG, Total RNA Expression Gene, a gene the team is trying to find whose expression is proportional to the overall RNA expression access to different cell types. Huuki-Myers then shared the data-driven TREG discovery and evaluations of rank invariance. Afterward, Huuki-Myers introduced the TREG package, which was used to compute findings from TREG. She then covered the experimental design, candidate selection, validation, and quantification of TREGs. The presentation wrapped up with Huuki-Myers sharing patterns observed puncta over cell types. There was a short Q&A session at the end of the presentation. Main Sections 0:00 Introductions 0:38 Motivation 1:21 What is TREG? 1:52 Data driver TREG discovery 2:42 Evaluating for rank invariance 4:07 TREG R package functions 4:38 Experimental design 5:28 Selecting candidate TREGs 6:16 Validation in smFISH + RNAscope 6:49 TREG quantification across DLPFC tissue 7:55 Patterns of observed Puncta over cell types 9:04 Conclusion with Q&A More Resources Bioconductor Conference Site: https://bioc2022.bioconductor.org/ BioC2022 Github: https://github.com/Bioconductor/BioC2022 Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Twitter:   / rconsortium   LinkedIn:   / r-consortium