#scrnaseq — Public Fediverse posts

Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.3.0

#10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

RE: https://genomic.social/@bgeedb/116760320692467761

Super happy to see this out. 😎 #FAIR #FAIRdata #SingleCell #scRNAseq

Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

📅 When: 12 and 13 May 2026
🕑 Time: 2:00 PM – 4:00 PM (AEST)
📍 Where: Online
💰 Cost: FREE for academic sector (places are limited!)

🔗Registration form link: https://lnkd.in/gQcHggGF

#Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology https://plos.io/4ch0XiX

Interesting #10XGenomics #Flex #scRNAseq #bias

Original post: https://bsky.app/profile/did:plc:4hfcecqwahpnojy4sr7vay6j/post/3mj4ovdavhs2d

Pipeline release! nf-core/scnanoseq v1.2.2 - nf-core/scnanoseq v1.2.2 - Thallium Tiger!

Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.2

#10xgenomics #longreadsequencing #nanopore #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

Our new pre‑print is out!

scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

https://wellcomeopenresearch.org/articles/11-43

@haessar.bsky.social @fionan-a.bsky.social @yiyicheng

#scRNAseq #bioinformatics

Pipeline release! nf-core/scnanoseq v1.2.1 - nf-core/scnanoseq v1.2.1 - Zinc Turtle!

Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.1

#10xgenomics #longreadsequencing #nanopore #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

#NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology https://plos.io/3HbrsJG

Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
https://doi.org/10.1093/bioinformatics/btaf195

Single cell RNA-sequencing (#scRNAseq) is an essential method to learn about cells in health and disease. Here we have studied "multiplets", an important source of error of scRNAseq. We find that multiplets are astonishingly frequent and hard to eliminate.
https://doi.org/10.1101/2025.06.09.658708

Pipeline release! nf-core/scnanoseq v1.2.0 - nf-core/scnanoseq v1.2.0 - Copper Rhinoceros!

Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.2.0

#10xgenomics #longreadsequencing #nanopore #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

How do #brain cells change over #evolution? @bentonlab compare #scRNAseq from ecologically distinct #drosophilid species to identify changes in composition & gene expression of different cell types, revealing higher divergence in #glia than #neurons @PLOSBiology https://plos.io/4js7Rms

How does transcriptional patterning regulate #SalivaryGland #morphogenesis? Annabel May & @katjaroeper use #scRNAseq of early morphogenesis of the #Drosophila salivary gland placode to reveal regulation by induction & exclusion of regulatory factors @PLOSBiology https://plos.io/4cUw827

Our new preprint is now out!

Dynamic transcriptional heterogeneity in pituitary corticotrophs

https://www.biorxiv.org/content/10.1101/2025.04.04.645979v1

We analysed publicly available single-cell RNA sequencing data of pituitary gland tissue and looked at corticotrophs, cells that are central to mediate stress responses.

We identified several transcriptional states in these cells that are related to how they respond to stress. Cells are able to transition between these states and this might be helpful for them to respond to stress coming at unpredictable times.

We also highlight issues related to using scRNAseq to look at functional subpopulations of cells.

#scrnaseq #stress #physiology #cellbiology #bioinformatics #corticotrophs #pituitary

Pipeline release! nf-core/scnanoseq v1.1.0 - nf-core/scnanoseq v1.1.0 - Iron Alligator!

Please see the changelog: https://github.com/nf-core/scnanoseq/releases/tag/1.1.0

#10xgenomics #longreadsequencing #nanopore #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

Uneven #hormone distribution in #plants regulates #cell_division and #growth, biologists discover.

#Brassinosteroids #signalling #cell_cycle #agriculture #scRNAseq

https://phys.org/news/2025-03-uneven-hormone-cell-division-growth.html

mascarade package implements a procedure to automatically generate 2D masks for clusters on single-cell dimensional reduction plots like t-SNE or UMAP https://github.com/alserglab/mascarade #rstats #scRNAseq

chatomics! How to Fine-Tune the Best Clustering Resolution for #scRNAseq Data 🎯 🧵

Finally, our paper studying synovial tissue #DC #DendtriticCells in joint health and #rheumatoidArthritis is out in Immunity!
#scRNAseq #spatialTranscriptomic
From: @ImmunityCP
https://mstdn.science/@ImmunityCP/113557582489769767

Computational toolbox from Human Cell Atlas.
#scrnaseq #humancellatlas
https://www.nature.com/articles/d41586-024-03762-y

2D embeddings for high-dimensional data, the battle rages on

#umap #tsne #scrnaseq

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012403

Insbesondere bei der Interpretation von #RNA-Sequenzanalysen einzelner Zellen (#scRNAseq) sind Techniken zur Dimensionen-Reduktion wie #UMAP der neuste Schrei. Doch bilden UMAP-Plots tatsächlich die Realität ab? Es gibt Zweifel … Zur Methoden-Kontroverse: https://www.laborjournal.de/rubric/hintergrund/hg/hg_24_11_01.php

Coming to #biocuration2025? Join our workshop scFAIR: FAIRification of single-cell data https://www.stowers.org/events/biocuration2025 @SIB @biocurator @marcrr @fbastian
#scRNAseq #FAIR #biocuration #bgee #FAIRdata #FAIRification #scFAIR #SingleCellAnalysis https://sc-fair.org/

Do you know gget by the Pachter lab? You should! It now includes efficient querying of Bgee in Python. Get high quality curated gene expression data directly in Python or command line. https://pachterlab.github.io/gget/en/bgee.html #RNAseq #biocuration #Python #scRNAseq @SIB @marcrr @fbastian @dee_unil

#bgee #Bioinformatics #RNASequencing #SingleCellRNASeq #Genomics #Transcriptomics #GeneExpression #ComputationalBiology #SingleCellAnalysis #GeneExpressionAnalysis

paraCell: A novel software tool for the interactive analysis and visualization of host-parasite single cell RNA-Seq data (without knowing programming) https://biorxiv.org/content/10.1101/2024.08.29.610375v1 . Interested in the datasets? Here: http://cellatlas.mvls.gla.ac.uk

#scRNAseq #parasites

"By using time-resolved analyses of scRNA-seq data, we determined the potential transitional trajectories of tumor cells and identified the metastasis-initiating subpopulations"

https://link.springer.com/article/10.1007/s11684-024-1081-7

Reading right now. The identification of cells that initiate #metastasis are of interest, although n=2 paired primary and #BoneMarrow samples may be a bit limited.

#scRNAseq #tumour #Neuroblastoma #pseudotime

Just read this great paper on "Identifying cell states in single-cell RNA-seq data at statistically maximal resolution" by Pascal Grobecker, Thomas Sakoparnig and Erik van Nimwegen from
@NimwegenLab
They address the issue of ad-hoc clustering of single-cell data by asking "[how can we] maximally reduce the complexity of the dataset without removing any of its meaningful structure"? And then answer it in a rigorous way implemented in {Cellstates}.

#scRNAseq
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012224