MULTI-seq | R implementation of MULTI-seq sample classification workflow | Genomics library

MULTI-seq is a R library typically used in Artificial Intelligence, Genomics, Pytorch applications. MULTI-seq has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

deMULTIplex is an R package containing the companion software for our method for single-cell RNA sequencing sample Multiplexing Using Lipid-Tagged Indices: MULTI-seq (for more information, check out our Nature Methods manuscript: MULTI-seq is methodologically analogous to the Cell Hashing (Stoeckius et al., 2018, Genome Biology) and Click-Tags (Gehring et al., 2019, NBT) except we utilize lipid- and cholesterol-modified oligonucleotides to rapidly and non-perturbatively label live-cell and nuclear membranes. If you want try out MULTI-seq reagents (for free!), fill out this form and/or send me an email (chris.mcginnis@ucsf[dot]edu). MULTI-seq interfaces with any droplet microlfuidics-based scRNA-seq methodology (e.g., 3' and 5' 10X Genomics, Drop-Seq, In-Drop, Seq-Well, etc.), and is also compatible with single-nucleus RNA-sequencing. MULTI-seq can also be used simultaneously with CITE-seq/REAP-seq/Total-seq for single-cell proteomics. We currently supply LMOs for 96 samples, and MULTI-seq reagents will soon be available commercially from Millipore-Sigma (contact james.hoberg@milliporesigma[dot]com for more info). Download example dataset for trying deMULTIplex here: NOTE: This 8-donor PBMC experiment is different from the HMEC data used in the tutorial. I opted to upload these data because (i) the MULTI-seq barcoding work very well and there were no missing barcodes, (ii) negative-cell reclassification was unncessary because >95% of the cells were classified, and (iii) the files are much smaller.