I've been reminded of SATURN, which aligns #scRNAseq between species. https://www.biorxiv.org/content/10.1101/2023.02.03.526939v1 #EvoDevo
https://ecoevo.social/@marcrr/111051263604179367

Marius Lange #BC2basel: increasing complexity of #SingleCell data leads to problems of temporal, spatial and spatio-temporal mapping. The solution proposed is optimal transport applications, implemented in Moscot:
https://moscot.readthedocs.io/en/latest/
https://www.biorxiv.org/content/10.1101/2023.05.11.540374v2 #SpatialTranscriptomics #scRNAseq

Excellent conclusions of Jean Fan #BC2basel on #machinelearning applied to #scRNAseq #SpatialTranscriptomics

Impressive work by Jean Fan's lab #BC2basel: aligning different spatial transcriptomics results, not only from different experiments but different protocols, e.g. MERFISH - Visium. Combining with #scRNAseq clustering and her lab's spot deconvolution allows to compare directly gene expression between these different experiments, https://www.biorxiv.org/content/10.1101/2023.04.11.534630v2.abstract #SpatialTranscriptomics

Last talk of the #CSSingleCells23 conference:
Karin Pelka, Gladstone/UCSF
https://gladstone.org/people/karin-pelka

Spatially organized #immune hubs in #colon #cancer
#ColonCancer
#scRNAseq

https://pubmed.ncbi.nlm.nih.gov/34450029/

Then: predict cellular interaction networks via correlations of gene program activities across #tumors

anti-tumor hubs in tumors, ISGs including CXCR3 ligands, differs between tumor types (MMR+ or -)

have data from clinical trial (biopsies before and after treatment)
https://pubmed.ncbi.nlm.nih.gov/36702949/

1/2

First speaker of the session (Cell identity in situ)
is Elana Fertig
of Convergence Institute at Johns Hopkins
https://fertiglab.com/

She has background in weather prediction (!) ⛈️ which has convergence with predictive medicine (such as huge data sets)

Looks at #PancreaticCancer with spatial #proteomics
now #scRNAseq

Developed CoGAPS matrix factorization, can identify cell state transitions

Using PhysiCell to build models
http://physicell.org/

#cancer
#SingleCell
#CSSingleCells23

1/2

"predicts new biology missed by today's genomics"
#spatial #transcriptomics #genomics
#scrnaseq
#CSSingleCells23

works by unified detection of sample-specific sequence variation... sample can be different cells, sequence is raw reads

method chooses an "anchor" sequence then looks for neighboring sequence that varies. thus is independent of any reference genome

https://www.biorxiv.org/content/10.1101/2022.06.24.497555v4
https://www.biorxiv.org/content/10.1101/2023.03.17.533189v2

New insights into universally expressed human genes
https://www.biorxiv.org/content/10.1101/2022.12.06.519414v1

2/3

GEARS for predicting transcriptional outcomes of gene perturbation. Check it out👇 :
https://www.nature.com/articles/s41587-023-01905-6
#scRNAseq #python #bioinformatics

Bioinformatician Hematology

Erasmus Medical Center

Come work on single cell data analysis in multiple myeloma at the Erasmus MC. #bioinformatics #MM #scRNAseq

See the full job description on jobRxiv: https://jobrxiv.org/job/erasmus-medical-center-27778-bioinformatician-hematology/?feed_id=55133

#ScienceJobs #hiring #research
Rotterdam #Netherlands #Bioinformatician
https://jobrxiv.org/job/erasmus-medical-center-27778-bioinformatician-hematology/?feed_id=55133

#GPT4 for cell type #annotation in single-cell RNA-seq (#scRNAseq) analysis.

"Tell me why my sample is so weird"

#bioinformatics

https://doi.org/10.1101/2023.04.16.537094

#scRNAseq #singlecell #pathwayAnalysis
Seems really easy to use, and return good results:

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis - ScienceDirect

https://www.sciencedirect.com/science/article/pii/S1535947620600159?via%3Dihub

I’m starting #scRNAseq and IMC #experiments soon and I’m distraught by the fact that I have zero dry-lab experience 😫😩😭

PIPseq performs comparably to existing scRNA methodologies, but at a significantly reduced cost.

Read more at the recent preprint on benchmarking: https://www.biorxiv.org/content/10.1101/2023.07.14.549051v1

For the original PIPseq paper, visit: https://www.nature.com/articles/s41587-023-01685-z #PIPseq #scRNAseq #Genomics

Interesting new #preprint in #immunology performing a meta-analysis of human #scRNAseq data across tissues. Surprising they could integrate so many diverse samples and sequencing technologies without losing all of the biological signal!

https://www.biorxiv.org/content/10.1101/2023.06.08.542671v1.full

So excited to share MMoCHi: a tool I've built for multi-modal cell type classification for CITE-seq data
The pre-print can be found here:
https://www.biorxiv.org/content/10.1101/2023.07.06.547944v1

And we've put it out on #GitHub so that you can try it out on your own data:
https://mmochi.readthedocs.io
#scRNAseq #scRNA #CITEseq #sequencing #analysis #ColumbiaUniversity #immunology

We welcome attendees, particularly with a view to developing collaborations relating to #sequencing, #genomics, #transcriptomics, #spatial omics and #scRNAseq. At the Pirbright Institute, we generate a lot of #biological and omic data and wish to work more effectively to deliver data-driven insights into tackling livestock #disease.

Novel cell type in the early human embryo: @manvendr7 @Prof_LDHurst @izsvaks_mdc &co use #scRNAseq & #embryo visualization to reveal a novel class of non-committed cells that undergo #apoptosis and may reflect a QC screening process #PLOSBiology https://plos.io/469tTEx

#scRNAseq #dataviz tricks: want to calculate {monocle3} trajectories in 20D, but plot them in 2D? It's easy enough to do, but the result may be messy, because of well-known UMAP distortions. Here's an example on the Nestorowa haematopoiesis dataset: (1/3)

I... think it is working? Left is conventional log1pP10KC, right is PFlog1pPF. Attaching also the source code that I came up with, with the help of ChatGPT, after checking how it was done by the authors of https://www.biorxiv.org/content/10.1101/2022.05.06.490859v1.

(see https://github.com/pachterlab/BHGP_2022/blob/ddf404ac58874bca768736728667369f81fffb60/scripts/norm_sparse.py#L10 )

#bioinformatics #scRNAseq #singlecell #Seurat

I finally started working with single cell datasets and I am trying different ways to normalise the data. I recently found a Pachter Lab paper recomending proportional fitting and log1p normalisation (i.e. PFlog1pPF) and I've been trying to implement a function that is able to do this of a counts matrix in a way that makes Seurat happy. But I do not know enough about the Seurat object structure. I'll keep this updated in case I see some progress.

#bioinformatics #singlecell #scRNAseq #Seurat