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, 6 tweets, 2 min read
Sharing our preprint on single-cell RNA-seq analysis of tumor-infiltrating CD8 lymphocytes in B16 murine melanoma tumors biorxiv.org/content/10.110…
We defined a transcriptomic map of CD8 TIL states, with unbiased identification of terminally exhausted (PD-1+ Tcf1-), memory-like / progenitor exhausted (PD-1+ Tcf1+), naïve and effector memory-like (EM-like) states, including clonality and tumor-specificity information
We describe novel surface markers for the EM-like population, including Ly6C and CXCR3. Re-analysis of public bulk and single-cell RNA-seq data suggests that EM-like cells expand upon Checkpoint Blockade
We built a novel predictor of CD8 TIL states for scRNA-seq data, TILPRED, that is available as an R package at github.com/carmonalab/TIL…
A reproducible R workflow using #Seurat to define our CD8 T cell clusters is available at gitlab.unil.ch/carmona/workfl… and data is available at ncbi.nlm.nih.gov/geo/query/acc.…
An #iSEE Shiny web tool to interact with these data is available at tilatlas.shinyapps.io/B16_CD8TIL_10X/ . Have fun!
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Joshua Batson
@thebasepoint
Single-Cell RNA-seq data is notoriously noisy. There are dozens of methods for cleaning it, but how do you know when they’re working right?

How clean is too clean?

Presenting...Molecular Cross-Validation

biorxiv.org/content/10.110…
Where does the noise come from? If a cell has 500k molecules and a droplet experiment nets you 5k, then you’re looking at just 1% of what was there. Lots of lowly-expressed genes will be missing by chance (the phenom formerly known as "dropout").
Luckily you can get a lot of cells now. Hundreds of thousands. Millions. Some of them are doing similar things to each other.

If you could somehow combine the individual sketchy pictures in the right way, you might learn better what each cell was doing.
Read 19 tweets
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Lior Pachter
@lpachter
A thread on how to analyze *any* single-cell RNA-seq dataset *without a computer* !! This 🤯 magic made possible thanks to insights of @vntranos, @sinabooeshaghi and @pmelsted + work on kallisto | bustools 🐻🚌🛠️ by the authors of biorxiv.org/content/10.110… 1/12
@vntranos @sinabooeshaghi @pmelsted First, a demo: open the website below, click on connect, and then run the @ProjectJupyter notebook on the website by repeatedly pressing shift-enter:
colab.research.google.com/github/BUStool… The notebook should run in less than 10 minutes. 2/12
@vntranos @sinabooeshaghi @pmelsted @ProjectJupyter Congratulations! You’ve just run an analysis of a single-cell RNA-seq dataset from scratch. The notebook downloaded the required software, streamed reads from SRA accession SRR8599150 (mouse retina), and ran kallisto|bustools for you.
ncbi.nlm.nih.gov/sra/?term=SRR8… 3/12
Read 12 tweets
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Florian Wagner
@flo_compbio
If you’re interested in clustering single-cell RNA-Seq data, please check out my new paper “Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN”! biorxiv.org/content/10.110…

Working PBMC example: github.com/flo-compbio/ga…

Quick Twitter summary below: 1/
I propose “Galapagos”, a clustering workflow designed to reliably separate the main cell types in scRNA-Seq data in a straightforward and transparent manner. 2/
The workflow consists of a simple series of steps and does not require gene selection. Essentially, clustering is performed by applying DBSCAN directly to t-SNE results. 3/
Read 17 tweets
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Will
@sandakano
Do you work with single cell RNA-Seq data (or any count data with large numbers of zeros)? You may be interested in our preprint now available on @biorxiv_genomic : biorxiv.org/content/10.110… in collaboration with @rafalab @stephaniehicks and Martin Aryee
I will try to describe here the main points. 1. Sampling distribution of unique molecular identifier counts (UMI) is NOT zero inflated (see also nxn.se/valent/2017/11… from @vallens ). Apparent zero inflation in read counts is due to PCR duplicates.
2. UMI counts are well-described by multinomial sampling ("competition to be counted") and only contain relative information (ie like microbiome they are compositional). This has been pointed out before by @inschool4life , see eg biorxiv.org/content/10.110…
Read 16 tweets
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Anthony Gitter
@anthonygitter
Our preprint on gene regulatory network inference from single-cell RNA-seq is up doi.org/10.1101/534834

The SCINGE method uses ensembles of Granger Causality models to estimate regulator-gene interactions from single-cell data with pseudotimes. @atulpdeshpande @ron_stewart2 1/9
Granger Causality works well for long time series, which we rarely have with bulk gene expression data. Single-cell data is appealing because we have many samples making a long (pseudo) time series. However, the samples are irregularly-spaced in pseudotime. 2/9
We adopt the Generalized Lasso Granger test to smooth the irregular pseudo time series data with a kernel function. This can also smooth missing values in the single-cell data. 3/9
Read 9 tweets
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Shahar Bracha
@shaharbrr
So so so so excited to share this!
This preprint summarizes my journey of trying to engineer the brain parasite Toxoplasma gondii to deliver therapeutic proteins into neurons, together with @OdedRechavi and @SheinerLab 1/n biorxiv.org/content/early/… #bioRxiv
You probably heard of Toxoplasma because of the way it cycles between cats and their prey. However, Toxoplasma also infects people. 2/n
Toxoplasmosis can be dangerous in people with weakened immune systems, but in most people infections are asymptomatic, and aren’t even noticed. In fact, it is estimated that a third of the human population are chronically infected with Toxoplasma. 3/n
Read 15 tweets

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