id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-295217-z2erqkr9	Seow, Justine Jia Wen	Single‐Cell RNA Sequencing for Precision Oncology: Current State-of-Art	2020-06-02		.txt	text/plain	4353	249	48	Majority of scRNA-seq approaches provide the steady state kinetics of mRNA (messenger RNA) expression without deeper insights into transcriptional dynamics of cells. However, a recent method called scSLAMseq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) profiles the transcriptional activity at the singlecell resolution which can help in differentiating old and new RNA for thousands of genes 14 A very recent method SMART-Seq3 provides the allele and isoform resolution in scRNA-seq approach 17 . This results in a data set that is roughly symmetric and often roughly normal Mutual Nearest Neighbours: a pair of cells from each batch is contained in each other's set of nearest neighbours Batch correction: scRNA-seq datasets generated across different conditions or from technologies that contain batch specific systematic bias leading to batch-effect. From single-cell transcriptomic data, Cell-PhoneDB calculates significant receptor-ligand pairs from cluster information and differentially expressed genes. A benchmark of batch-effect correction methods for single-cell RNA sequencing data	./cache/cord-295217-z2erqkr9.txt	./txt/cord-295217-z2erqkr9.txt