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cnio-bu / beyondcell

Beyondcell is a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq and Spatial Transcriptomics data.
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Drug screening against cells in a pathological state #9

Closed tdido closed 2 years ago

tdido commented 2 years ago

In GitLab by @rpmoraga on Jan 4, 2022, 13:15

Hello, congratulations for the publication and the package :smile:

I was wondering that if will be possible to use the package in a scenario to find a drug capable to reverse the transcriptomic signature of a diseases cell to the healthy cell state.

In this case, I could use the L1000 level 5 information using the z-scores for each of the drugs tested on cell lines considered healthy. Using the suggested procedure in this issue to generate a custom GeneSet of signatures. However, my main question would be if there is any way to verify, in a in silico way, that the list of drugs "effective" according to the BCS against the group of pathological cells. It is also capable of reversing the transcriptomic signal from pathological to healthy.

Thank you very much in advance

tdido commented 2 years ago

In GitLab by @cfustero on Jan 4, 2022, 14:31

Hola @rpmoraga,

First of all, thanks for your interest in Beyondcell :)

The LINCS signatures are already included in the Beyondcell package, under the name Perturbation Signatures collection or PSc. So in case you are interested in exploring the signature reversion paradigm, the analysis should be pretty straightforward. You can load the collection using the following command: 

# Generate geneset object with one of the ready to use signature collections.
gs <- GenerateGenesets(PSc)

If I understand you correctly, to do the analysis you’re interested in: first, you would need to run Beyondcell including both healthy and unhealthy cells in the same set. Make sure there is a metadata column specifying the origin/status of the cells (healthy/unhealthy). After you calculate the scores, a regression of the main sources of variation should be applied to obtain the QR residuals. Then, you should use the bcRanks function in order to classify the drugs based on their effect in healthy/unhealthy cells.

# Obtain statistics for the 'status' metadata column.
bc <- bcRanks(bc, idents = "status")

We also recommend using the bc4Squares function to visualize the hits. Using this plot, you'll be able to easily detect the homogeneously sensitive/insensitive drugs, as well as the differentially sensitive/insensitive drugs between conditions (in your case, between healthy and unhealthy cells).

# Ranking visualizaiton for unhealthy vs. healthy cells
bc4Squares(bc, idents = "status", lvl = "unhealty", top = 5)

Let us know if you need any more information!

Best,

Coral