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dhimmel / lincs

Library of Integrated Cellular Signatures L1000
https://think-lab.github.io/d/43/
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Where can i find the ligand consensus signature ? and the file that lits the number of gold signatures used of the analysis ? #6

Open abappal opened 4 years ago

dhimmel commented 4 years ago

Copying more information from the email, since it will help me answer this:

Three years back when i had come across this data i had downloaded the consensus signature for overexpression, knockdown and ligand perturbation. However, recently when i revisited this repository i am not able to find any consensus signature for ligand perturbation (attached). Would appreciate if you can let me know if the ligand signature is still available?

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dhimmel commented 4 years ago

The file attached to the email with filename LINCS_cons_trt_lig.tsv appears to be similar to data/pertinfo/lincs_small_molecules.tsv, which has information on L1000 ligands.

Check out the figshare dataset https://doi.org/10.6084/m9.figshare.3085426.v1. I think you may want the consensi-pert_id.tsv.bz2 file (445.32 MB). As mentioned in this repos README:

consensi-pert_id.tsv.bz2 with consensus signatures for each L1000 pert_id. This file is too large for GitHub (500 MB), but is available on figshare.

saisaitian commented 2 years ago

Dear author： If I want to cite your 'consensi-pert_id.tsv.bz2' data, how can I write in paper? In addition, the all pert_id were from GSE92742 dataset or GSE70138 (aka LINCS Phase II L1000 dataset) or both datasets?

saisaitian commented 2 years ago

Dear author： Could I guess the consensi data from your research was simliar to Level 5 data(SIG)?

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dhimmel commented 2 years ago

If I want to cite your 'consensi-pert_id.tsv.bz2' data, how can I write in paper?

You can cite any of the following (1 for data, 2 for code, 3 for the study for which this work was done, 4 for supplemental discussion).

  1. Consensus signatures for LINCS L1000 perturbations Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini Figshare (2016-03-08) https://doi.org/f3mqvs DOI: 10.6084/m9.figshare.3085426.v1

  2. dhimmel/lincs v2.0: Refined Consensus Signatures From Lincs L1000 Daniel Himmelstein, Leo Brueggeman, Sergio Baranzini Zenodo (2016-03-08) https://doi.org/f3mqvr DOI: 10.5281/zenodo.47223

  3. Systematic integration of biomedical knowledge prioritizes drugs for repurposing Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini eLife (2017-09-22) https://doi.org/cdfk DOI: 10.7554/elife.26726 · PMID: 28936969 · PMCID: PMC5640425

  4. Computing consensus transcriptional profiles for LINCS L1000 perturbations Daniel Himmelstein, Caty Chung ThinkLab (2015-03-26) https://doi.org/f3mqwc DOI: 10.15363/thinklab.d43

Also point you to the manuscript section on how we processed the LINCS data in case its helpful.

dhimmel commented 2 years ago

In addition, the all pert_id were from GSE92742 dataset or GSE70138 (aka LINCS Phase II L1000 dataset)

Not sure, we didn't access the data through GEO (which I don't think was available when we were doing this project). If you figure out, leave a comment.

Could I guess the consensi data from your research was simliar to Level 5 data(SIG)?

I think it's similar, although, if I remember correctly, LINCS rarely aggregated signatures to the level of a perturbagen (across all cell types and dosages). But the methods should be similar.

saisaitian commented 2 years ago

Thanks for your reply. How can I describe the raw data come from if I use the data? Can I just describe that from LINCS L1000 plateform? Not to describe to come form GEO ?

dhimmel commented 2 years ago

How can I describe the raw data come from if I use the data?

If you're writing a manuscript or report, you could say something like: We used consensus expression signatures of molecular perturbations from LINCS L1000 (cite 10.1016/j.cell.2017.10.049), as computed by Project Rephetio (cite 10.7554/elife.26726).