Igor Dolgalev (@bioigor) 's Twitter Profile
Igor Dolgalev

@bioigor

Assistant Professor @NYUGSOM_PMED @nyugrossman

see also: igord.bsky.social

ID: 870030789483868161

linkhttps://igor.pub calendar_today31-05-2017 21:34:39

927 Tweet

372 Followers

287 Following

Alexey Sergushichev (@assaron) 's Twitter Profile Photo

I'm happy to share that out paper on Phantasus, a web-application for visual and interactive gene expression analysis, got out in eLife - the journal elifesciences.org/articles/85722 Here, I'm using it to analyze a random GEO dataset, including basic QC to filter outliers, in under 100 seconds! 1/n

Igor Dolgalev (@bioigor) 's Twitter Profile Photo

Seems like you can determine sequencing platform from the quality scores alone using a GPT model open.substack.com/pub/aseq/p/usi…

Ankur Sharma (@asharmaiisc) 's Twitter Profile Photo

Systematic comparison of sequencing-based spatial transcriptomic methods in Nature Methods 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

Systematic comparison of sequencing-based spatial transcriptomic methods in <a href="/naturemethods/">Nature Methods</a> 

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
Constantin Ahlmann-Eltze (@const-ae.bsky.social) (@const_ae) 's Twitter Profile Photo

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. biorxiv.org/content/10.110…

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.

biorxiv.org/content/10.110…
Luciano Martelotto 🛠🧬💻🇦🇺 (@lgmartelotto) 's Twitter Profile Photo

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! 10x Genomics @nanostringtech Akoya Biosciences Bruker

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!

<a href="/10xGenomics/">10x Genomics</a> @nanostringtech <a href="/AkoyaBio/">Akoya Biosciences</a> <a href="/bruker/">Bruker</a>
Christoph Bock Lab @ CeMM & MedUni Vienna (@bocklab) 's Twitter Profile Photo

🗨️ 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)

Nature Methods (@naturemethods) 's Twitter Profile Photo

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. nature.com/articles/s4159…

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. 

nature.com/articles/s4159…
Yusuf Roohani (@yusufroohani) 's Twitter Profile Photo

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

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