Where can i find the ligand consensus signature ? and the file that lits the number of gold signatures used of the analysis ?

[abappal]

No description provided.

[dhimmel]

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?

[dhimmel]

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]

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]

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

[dhimmel]

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]

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]

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]

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).