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#singlecell

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Happy to see out our latest work on studying animal body size variations using single cell transcriptomics to measure gene expression of individual cells. Learned my fair bit of statistics here, and most importantly I learned how to build wrapper functions for "complex" tasks in R (such as differntial gene expression) in a modular way. Thanks to all the authors involved! science.org/doi/10.1126/sciadv #singlecell #scrna #allometry #splitseq #planarians #generegulation

🔬✨Zooming In—One Cell at a Time!

The 5th Danish #Single-Cell #Symposium is coming to #Copenhagen, and it's THE place to be if you’re into all things single-cell!

Topics will include:
Advances in single-cell #genomics and #transcriptomics
#Computational approaches to #dataanalysis
Applications in health and disease

🗓️May 15–16
📍Hosted at Panum, UCPH
💡Scientists, students, and techies are all welcome!

🔗 Check it out: events.au.dk/5thdanishsingle-c

#Genomics #SingleCell

These FASTQ files are driving me batty.

I have FASTQ files for a Chromium prepared library. I run cutadapt to try and limit bad reads. I run RNASTAR solo with correct parameters to extract the read UMI and GEM barcode into UM CB tags of BAM file.

I run umitools to dedup based on barcode.

AssertionError: not all umis are the same length(!): 1 - 10

Why. Is umitools. Dying??? All the barcodes and UMIs should be the same length. They're fixed length pulls from the read.

Any #rstats #Genomics #SingleCell people used scSplit for genetic demultiplexing of non-hashed mixed samples?

On the GitHub page it says:

"If necessary, remove duplicated reads based on UMI using tools like rmdup in UMI-tools."

github.com/jon-xu/scSplit

That makes sense. But shouldn't it really be by GEM *and* UMI? Just UMI would mix GEMs, and that would exclude identically mapping reads from different droplets. Unless umitools checks *both*. I've only used it on fastq files before, not BAM.

GitHubGitHub - jon-xu/scSplit: Genotype-free demultiplexing of pooled single-cell RNA-Seq, using a hidden state model for identifying genetically distinct samples within a mixed population.Genotype-free demultiplexing of pooled single-cell RNA-Seq, using a hidden state model for identifying genetically distinct samples within a mixed population. - jon-xu/scSplit

Pleased to share a review I contributed to, now published in Nature Neuroscience: Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system.

The article highlights how lineage tracing sheds light on cell fate decisions and molecular mechanisms in development and disease. Read here: doi.org/10.1038/s41593-024-018