Proud to be part of an amazing group of engineers, scientists, and program officers contributing to developing CELLxGENE to further cell bio research.
Now with gene expression for >85M cells, our free and open-source tools enable cell biologists to advance their research.
Chan Zuckerberg CELL by GENE Discover aims to be a one-stop shop for single-cell RNA sequencing data storage, access and analysis go.nature.com/4bfU7Xv
Pablo García-Nieto, Ph.D. retweeted
I'm excited to announce that starting this week CELLxGENE Census supports categorical variables for cell metadata of the now 59M cells . Fetching data will now utilize less memory!! However in some cases the change may break existing code. Details here: chanzuckerberg.github.io/cel…
I'm honored to have worked with an amazing team at @cziscience and comp bios to deliver the first marketplace of models and embeddings along one of the largest aggregations of standardized #singlecell data from #CELLxGENE Census.
Explore the models! bit.ly/census_models
🚨1/ New to CZ #CELLxGENE: models & embeddings that integrate up to 36M cells in the Census corpus.
Use embeddings to explore the corpus directly, or download the models to run your own data through them to enable direct comparisons to the reference. 🧵
bit.ly/census_models
Pablo García-Nieto, Ph.D. retweeted
1/6 The @cziscience team’s preprint on how CZ #CellxGene Discover serves as a centralized hub for standardized, interoperable and openly available #SingleCell matrices is out!
Really proud of what we’re building to address the community’s needs bit.ly/40kFX2W 🧵
New to CZ #CELLxGENE Census: A normalized expression layer & gene and cell stats for all 33M cells!
In Python and R you can work with #SingleCell CPM values and quickly filter genes and cells, for example get all cells with more than 100 expressed genes.
bit.ly/normalized_layer
🚨New #CZCellxGene Census Python package alert: Now you can calculate average & variance gene expression and find highly variable genes across 33M+ cells with just a standard laptop 💻!
Get started streaming large scale #SingleCell analyses:
bit.ly/44TwX5L
Pablo García-Nieto, Ph.D. retweeted
We are excited to share Geneformer, our foundation model pretrained on 30 million single cell transcriptomes to enable predictions in network biology.
Manuscript: rdcu.be/ddrx0
Model: huggingface.co/ctheodoris/Ge…
Pretraining corpus: huggingface.co/datasets/cthe…
Pablo García-Nieto, Ph.D. retweeted
I am beyond excited to share that SIMBA, a versatile single-cell graph embedding method, has been published @naturemethods nature.com/articles/s41592-0… (1/6)
Excited for the new opportunities that the #CZCellxGene Census opens up to the Single-Cell community. Made from community-contributed data, e.g. @humancellatlas, the Census expedites single-cell data access!
I am so proud of the Census team at CZI to get this out in the open.
Big News🚨: #CZCellxGene Discover Census launched today! czi.co/3Nzpfsk
Built from >500 datasets, Census gives you efficient access to the largest aggregation of #SingleCell RNA data that’s immediately ready for analysis with harmonized labels for cell and gene metadata
Pablo García-Nieto, Ph.D. retweeted
We are excited to release Seurat v5- with new methods for multimodal, spatially resolved, and massively scalable single-cell analysis. satijalab.org/seurat
Pablo García-Nieto, Ph.D. retweeted
“Where’s my gene?”
A tweetorial 🧵on how to use #CZCellxGene to answer this question using data from multiple datasets
Pablo García-Nieto, Ph.D. retweeted
Happy to share our latest work (w/ @tagasovska, @stephenrra, @kchonyc, @jkpritch and Aviv Regev) about learning causal representations of single-cell perturbation data sets arxiv.org/pdf/2211.03553.pdf.
We had seen embryonic expression of transposable elements as means of protecting the genome of plants before. Amazing to see a similar process in humans!!
Super excited and thankful to finally see my graduate work out in the wild. science.org/doi/10.1126/scie…
We wanted to know if ancient viral protein-coding sequences that are embedded within our genomes can protect us from infection by circulating viruses. (1/8)
Pablo García-Nieto, Ph.D. retweeted
Amazing week for #DeepLearning in #spatial #singlecell biology, with 2🔥new Graph Neural Networks methods!
1.STELLAR🇺🇸 @jure: a cell type annotation & discovery atlas-type framework
2.NCEM🇪🇺 @fabian_theis: an approach to infer cellular communication patterns
Deep dive below🧵
Pablo García-Nieto, Ph.D. retweeted
2/n Here, we propose NCEM (nature.com/articles/s41587-0…) and leverage spatial proximity of cells in tissues as a prior for statistical dependency. We use graph neural networks to capture this dependency and relate this statistical phenomenon to cell communication and tissue niches.
I’m thrilled to be at the #SACNAS conference this year in Puerto Rico. If you are here stop by the CZI booth 1115 to learn more about our science team. I’m happy to share about our work in #singlecell and #cellxgene.
Pablo García-Nieto, Ph.D. retweeted
Excited to present our new preprint! We sequenced 2.4 million marmoset brain cell nuclei across major neocortical and subcortical structures. Here’s what we learned.
biorxiv.org/content/10.1101/…
Pablo García-Nieto, Ph.D. retweeted
So excited our single-cell transcriptomic survey of the adult human brain is on bioRxiv! @slinnarsson @MossiAlejandro @ka_wai_rine @ErnestArenasSWE A really fun collaboration with Rebecca Hodge, Trygve Bakken, Ed Lein and others at the @AllenInstitute doi.org/10.1101/2022.10.12.5…
Pablo García-Nieto, Ph.D. retweeted
Excited to share the single-cell adult #macaque brain atlas—our contribution to a big week of #brainatlas preprints! W/ @XingfanH, @MichaelLouisPl1, @JShendure, & @SMack_Lab—we profiled 4.2M nuclei across 30 regions from females+males. Here’s what we found bit.ly/3CL0zpQ
Pablo García-Nieto, Ph.D. retweeted
Census: accurate, automated, deep, fast, and hierarchical scRNA-seq cell-type annotation biorxiv.org/content/10.1101/…
