scBaseCamp was built by directly mining all publicly accessible 10X Genomics scRNAseq data from the Sequence Read Archive (SRA) With over 230M cells drawn from 21 species, 72 tissues, scBaseCamp is significantly larger and more diverse than existing single-cell data repositories

DeepCellTypes pre-print: https://t.co/8vD471yKbR Code and vignettes:

Thank you @xuefei_w (@davidvanvalen lab) for a great presentation of DeepCellTypes at our NYU Single-Cell Club. DeepCellTypes is a spatial proteomics phenotyping method with the ability to generalize across diverse datasets with varying marker panels.

The wait is over!! We are thrilled to announce that we have chosen Spatial Proteomics as 2024’s Method of the Year! 🥳 For more on Spatial Proteomics and a road map to this special issue, please see this month’s Editorial or read on in this thread. https://t.co/phXooJzJda

This is great! @NIAIDNews have launched a collection of public domain illustrations (currently 550 individual icons) https://t.co/qYzIsbr0TH

TCAT pre-print: https://t.co/nnPQ9zIGDL Code and vignettes:

Thank you @DKotliar (@soumya_boston lab) for a great presentation of CellAnnoTator (*CAT) and T-CellAnnoTator (TCAT) at our NYU Single-Cell Club. TCAT annotates scRNA-Seq T-cell subsets with predefined gene expression programs compiled from a range of reference datasets.

🗨️ WANNA TALK TO YOUR CELLS? Try out CellWhisperer – our new multimodal AI that turns single-cell RNA-seq analysis into a conversation. No coding needed, just chat in plain English. Short walkthrough below. Web app & bioRxiv preprint linked in the thread. Let's dive in! (1/9)

Here we go…repurposing spatial imaging techs to achieve ultra-low to ultra-high, multiplexing, nuclei, cells, single (RNA or Protein), multimodal (RNA and Protein), super cheap! NO SEQUENCING! @10xGenomics @nanostringtech @AkoyaBio @bruker

There's a lot of excitement about foundation models and their ability to learn biology 🧬💻 But current tools for perturbation prediction perform worse than simple linear models! We need more careful benchmarking to make progress. https://t.co/lTJM7ghk2r

Are you missing cells and cell types in your single-cell multiome experiments @10xGenomics? Check out EmptyDropsMultiome @MarioniLab

$ILMN - Illumina buys Fluent Biosciences to accelerate single-cell analysis

Systematic comparison of sequencing-based spatial transcriptomic methods in @naturemethods In summary: “- Stereo-seq, Slide-tag, Visium shows the better capture efficiency with raw sequencing depth - Slide-seq V2, Visium (probe), DynaSpatial gives the better capture efficiency

CellChat v2 pre-print: https://t.co/NkeKUatB9R Code and vignettes:

Thank you @suoqin_jin for a great presentation of CellChat at our NYU Single-Cell Club. CellChat v2 expands the underlying CellChatDB database, extends the inference of cell-cell communication to spatially-resolved datasets, and adds an interactive browser for output exploration.

Seems like you can determine sequencing platform from the quality scores alone using a GPT model

https://t.co/akePpnyGEd

I'm happy to share that out paper on Phantasus, a web-application for visual and interactive gene expression analysis, got out in @eLife https://t.co/fd99RQ2nIv Here, I'm using it to analyze a random GEO dataset, including basic QC to filter outliers, in under 100 seconds! 1/n

VeloCycle pre-print: https://t.co/4DTAbyYmC2 Code and vignettes:

Thank you @alexlederer19 (@GioeleLaManno lab) for a great presentation of VeloCycle at our NYU Single-Cell Club. VeloCycle is a new implementation of RNA velocity that combines velocity field and manifold estimation, tailored for the cell cycle.