#scrna — Public Fediverse posts

Any #tiledb #scRNA users out there?

I am ingesting data from an `h5ad` file into a soma with `tiledbsoma.io.from_h5ad` (#python API).

Does this function load the entire h5ad into memory before conversion? Python keeps crashing which I am guessing are OOO issues. If yes, is there another option to do this conversion without loading the data in memory?

It would be strange if the only option is to load the whole thing into memory considering how massive scRNA datasets are these days.

Pipeline release! nf-core/scnanoseq v1.0.0 - nf-core/scnanoseq v1.0.0 - Titanium Toad!

Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.0.0

#10xgenomics #long-read-sequencing #nanopore #scrna-seq #single-cell #nfcore #openscience #nextflow #bioinformatics

@lwpembleton @grrrck @adamhsparks @defuneste @danwwilson @milesmcbain @njtierney @jimjamslam the workstation at work (ubuntu 22, 24 cores, 125GB RAM memory, 2TB storage). Working very well for omics analysis #single_cell #scRNA, #methylation and #transcriptomics #ShareYourSetup

@lwpembleton @grrrck @adamhsparks @defuneste @danwwilson @milesmcbain @njtierney @jimjamslam the workstation at work (ubuntu 22, 24 cores, 125GB RAM memory, 2TB storage). Working very well for omics analysis #single_cell #scRNA, #methylation and #transcriptomics #ShareYourSetup

@lwpembleton @grrrck @adamhsparks @defuneste @danwwilson @milesmcbain @njtierney @jimjamslam the workstation at work (ubuntu 22, 24 cores, 125GB RAM memory, 2TB storage). Working very well for omics analysis #single_cell #scRNA, #methylation and #transcriptomics #ShareYourSetup

@lwpembleton @grrrck @adamhsparks @defuneste @danwwilson @milesmcbain @njtierney @jimjamslam the workstation at work (ubuntu 22, 24 cores, 125GB RAM memory, 2TB storage). Working very well for omics analysis #single_cell #scRNA, #methylation and #transcriptomics #ShareYourSetup

@lwpembleton @grrrck @adamhsparks @defuneste @danwwilson @milesmcbain @njtierney @jimjamslam the workstation at work (ubuntu 22, 24 cores, 125GB RAM memory, 2TB storage). Working very well for omics analysis #single_cell #scRNA, #methylation and #transcriptomics #ShareYourSetup

Check out the new & improved single cell analysis training page! #scrna
https://training.galaxyproject.org/training-material/topics/single-cell/
It takes you from concepts to easy walkthroughs, to case studies with tricky decision-making, to data reformatting and even the beginning of our multiomics section!
@gtn

This looks potentially useful at reducing noise in scRNA-seq datasets

#immunology #scrnaseq #scRNA #bioinformatics #Science #ScienceMastodon

https://www.nature.com/articles/s41592-023-01943-7

So excited to share MMoCHi: a tool I've built for multi-modal cell type classification for CITE-seq data
The pre-print can be found here:
https://www.biorxiv.org/content/10.1101/2023.07.06.547944v1

And we've put it out on #GitHub so that you can try it out on your own data:
https://mmochi.readthedocs.io
#scRNAseq #scRNA #CITEseq #sequencing #analysis #ColumbiaUniversity #immunology

@InnesBT This review paper by Gasperini, Tome, and Shendure is pretty good for the biology and protocols you can use for answering questions like this:

https://www.nature.com/articles/s41576-019-0209-0

It covers a lot o protocols and methods (#MPRA, #CRISPR screens, #eQTLs, #scRNA-seq, etc), but it doesn't focus as much on the computational construction of those things.

Just read the incredible preprint "A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain" by Yao et al https://www.biorxiv.org/content/10.1101/2023.03.06.531121v1. An atlas of the whole mouse brain, with single-cell RNA-seq and spatial (MERFISH) data from > 4 million high-quality cells. The authors describe 5,000 distinguishable "clusters", the most corresponding to neurons with unique patterns of expression of ~ 500 transcription factors.A landmark paper. #scRNA #allen #spatial #neuroscience

@haojiawu @biorxivpreprint this is very interesting. I have been telling people that one of the main reasons for using #RStats instead of #python for gene expression analysis, is the lack of methods such as #DESeq2 or #limma as python libraries. This might tip the balance for a lot of people.

Of course there are many reasons to prefer R, the excellent #Bioconductor ecosystem one of them, and in fairness, for #scRNA analysis python has very strong ecosystem and community.

I'm excited about this new #genomics #scRNA #rnaseq preprint from @ariel_hippen! It's a molecular biology deep dive into what happens when you dissociate and single-cell sequence a tissue with the goal of performing deconvolution of bulk tissue.

Some key bits: there are dissociation effects, RNA capture/depletion effects, and potentially some microfluidics effects with the #10X platform. Also, deconvolution algorithms are variably robust to these things.

See more:
https://www.biorxiv.org/content/10.1101/2022.12.04.519045v1

By way of #introduction for the great migration: I am a builder of models: computational (#ml, #machinelearning), mathematical, and scale models (#hoscale #scalemodel). Working as a computational biologist / bioinformaticist for ExosomeDx. (#multiomics #scRNA-seq #dimensionality #bioinformatics #computationalbiology #compbio) Follower of and/or interested in #osint, science history, nuclear power, #trains, pretty birds, canyon hikes, and scientific ethics.