Vincent Gardeux (@gardeux_vincent) 's Twitter Profile
Vincent Gardeux

@gardeux_vincent

Senior Scientist

ID: 246186099

calendar_today02-02-2011 09:34:54

89 Tweet

74 Followers

185 Following

Vincent Gardeux (@gardeux_vincent) 's Twitter Profile Photo

Nb Peaks called by MACS2 on ATAC-seq data vs depth of sample (from 100k reads to 100M). Does it make any sense? Is there an euclidean division/modulo in the model?

Nb Peaks called by MACS2 on ATAC-seq data vs depth of sample (from 100k reads to 100M). Does it make any sense? Is there an euclidean division/modulo in the model?
EPFL Life Sciences (@epflsv) 's Twitter Profile Photo

A new paper in eLife - the journal by David Suter shows how by #pluripotency transcription factors dynamically regulate #chromatin accessibility across the cell cycle doi.org/10.7554/eLife.…

Rahul Satija (@satijalab) 's Twitter Profile Photo

We’re excited to release a Seurat update with support for Spatial Transcriptomics data! Includes clustering, interactive visualization, and integration with scRNA-seq references. Check out our vignette on 10x Genomics Visium data: satijalab.org/seurat/v3.1/sp…

We’re excited to release a Seurat update with support for Spatial Transcriptomics data! Includes clustering, interactive visualization, and integration with scRNA-seq references. Check out our vignette on <a href="/10xGenomics/">10x Genomics</a> Visium data:  satijalab.org/seurat/v3.1/sp…
Daniel Alpern (@satyagrakha) 's Twitter Profile Photo

Our exciting work together with Ronald Dijkman shows that we can reliably detect #HCoV2 viral transcripts in infected human cells using BRB-seq (tinyurl.com/y3l5hut8) even at the early stage. Affordable #transcriptomics opens a great perspective for mass #COVID19 diagnostics

Our exciting work together with <a href="/DijkmanRonald/">Ronald Dijkman</a> shows that we can reliably detect #HCoV2 viral transcripts in infected human cells using BRB-seq (tinyurl.com/y3l5hut8) even at the early stage. Affordable #transcriptomics opens a great perspective for mass #COVID19 diagnostics
EPFL (@epfl_en) 's Twitter Profile Photo

EPFL Professor Andrea Ablasser from EPFL Life Sciences shares the Leenaards Foundation 2020 Science Prize with Professor Michel Gilliet from the CHUV / Centre hospitalier universitaire vaudois. Their project aims to gain insight into the causes and effects of an overactive innate immune system. actu.epfl.ch/news/andrea-ab…

SIB (@isbsib) 's Twitter Profile Photo

Among the new SIB Resources, ASAP, the Automated #Singlecell Analysis Portal, is now presented at the SAB meeting, with SIB's Vincent Gardeux from @BartDeplancke's group EPFL. Looking to analyze single-cell #omics data? Check the resource out: asap.epfl.ch

Among the new SIB Resources, ASAP, the Automated #Singlecell Analysis Portal, is now presented at the SAB meeting, with SIB's <a href="/gardeux_vincent/">Vincent Gardeux</a> from @BartDeplancke's group <a href="/EPFL/">EPFL</a>. Looking to analyze single-cell #omics data? Check the resource out: asap.epfl.ch
Judith Kribelbauer (@jkribelbauer) 's Twitter Profile Photo

Happy to finally share this work. It started as a side project during the lockdown, but then took on a life of its own. doi.org/10.1101/2023.0… A thread 🧵 :

Bgee database (@bgeedb) 's Twitter Profile Photo

A few days left to register with early bird rate to our [BC]2 SIB workshop "Standardization of single-cell metadata: an Open Research Data initiative" with Jason Hilton of cellxgene & David Osumi-Sutherland EMBL-EBI #SingleCell #FAIRdata bc2.ch/tutorials-work…

A few days left to register with early bird rate to our <a href="/BC2Conference/">[BC]2</a> <a href="/ISBSIB/">SIB</a> workshop "Standardization of single-cell metadata: an Open Research Data initiative" with Jason Hilton of <a href="/cellxgene/">cellxgene</a> &amp; David Osumi-Sutherland <a href="/emblebi/">EMBL-EBI</a>  #SingleCell #FAIRdata  bc2.ch/tutorials-work…
Anders Eklund (@wandedob) 's Twitter Profile Photo

I am often invited to review papers on deep learning for medical images. Unfortunately many papers do the same mistake; they split data into training/validation/test on the slice/image/patch level instead of on the patient level. This will lead to inflated test scores, as images