Researchers at Northwestern University's Feinberg School of Medicine have developed a new technique for accurately identifying individual cells in single-cell RNA sequencing. This advancement improves data precision and advances gene expression studies. Additionally, the SCALPEL method has been introduced for quantifying transcript isoforms at the single-cell level. The recent Integration Day event highlighted current and potential future work in single-cell sequencing, emphasizing the importance of these advancements in the field. PredGCN, a pruning-enabled gene-cell net for automatic cell annotation of single-cell transcriptome data, has also been introduced.
PredGCN: A Pruning-enabled Gene-Cell Net for Automatic Cell Annotation of Single Cell Transcriptome Data https://t.co/OWfmwHOCxy https://t.co/o7gGbb2hri
Researchers at @NUFeinbergMed have developed a new technique for accurately identifying individual cells in #singlecell #RNAsequencing, improving data precision and advancing #geneexpression studies. https://t.co/JJkFeJbKq6
Thank you to everyone who attended and presented at yesterday's Integration Day — a chance to dive deeper into current and potential future work in single cell sequencing 🧪🧬 https://t.co/HUt3n8xyBq
Quantification of transcript isoforms at the single-cell level using SCALPEL https://t.co/l9VUimTL4V #biorxiv_bioinfo
CELLULAR (CELLUlar contrastive Learning for Annotation and Representation) leverages single-cell RNA sequencing data to train a deep neural network to produce an efficient, lower-dimensional, generalizable embedding space.