dhimmel / lincs Goto Github PK

I can't figure out where to get modzs.gctx, which is needed to construct the signature dataframe sig_expr_df in consensi.ipynb. From here, you say:

The z-score signature vectors are retrieved from the /xchip/cogs/data/build/a2y13q1/modzs.gctx file on the C3 cloud.

But this was 2 years ago and the link doesn't work anymore. Also, I'm not exactly sure what this file is exactly or how it was generated.

I appreciate your help in advance!

I am trying to plot some genes using data level 4 for my compound (BRD-K55591206) on HepG2 cells.

There are two signatures with HepG2 cells at level 5:
LJP008_HEPG2_24H:J01
POL001_HEPG2_24H:J09
To make sure I was using the same data from these level 5 signatures I checked the replicates at level 4 of each of these signatures above. The average of the two LJP008 experiments (distil_ids: LJP008_HEPG2_24H_X2_B20:J01|LJP008_HEPG2_24H_X3_B20:J01) matches the signature of each gene at level 5. Perfect.

However, the level 4 data for signature POL001_HEPG2 (distil_ids: POL001_HEPG2_24H_X1.L2_B23:J07|POL001_HEPG2_24H_X2.L2_B23:J07|POL001_HEPG2_24H_X3.L2_B23:J07) does not match level 5.

If we use the NAT2 gene as an example, we have the following level 5 value: 0.004413

On the other hand, the values for the level 4 replicates are:
POL001_HEPG2_24H_X1.L2_B23:J07 = -0.386299998
POL001_HEPG2_24H_X2.L2_B23:J07 = 0.110600002
POL001_HEPG2_24H_X3.L2_B23:J07 =0.38409999
The avg 0.036133 does not match level 5 0.004413

The compound is BRD-K55591206, 10 µM, 24 h.

Why don’t they match?

I am using cmapR to retrieve the data from these files:
https://clue.io/releases/data-dashboard
https://s3.amazonaws.com/macchiato.clue.io/builds/LINCS2020/level5/level5_beta_trt_cp_n720216x12328.gctx
https://s3.amazonaws.com/macchiato.clue.io/builds/LINCS2020/level4/level4_beta_all_n3026460x12328.gctx
https://s3.amazonaws.com/macchiato.clue.io/builds/LINCS2020/siginfo_beta.txt
https://s3.amazonaws.com/macchiato.clue.io/builds/LINCS2020/instinfo_beta.txt

Hi Daniel,

thank you very much for sharing this work. As a computational biologist, this data seems very interesting for lookup of hypothesis won in another dataset in a wet lab data, great!

I had a look at the datasets you kindly provided in https://github.com/dhimmel/lincs/tree/gh-pages/data/consensi and checked the effect of overexpression/underexpression of a gene as perturbagen on itself:

About a third of the genes showed nominal significant (z score <= -1.96) underexpression when it was itself the repressing perturbagen. When looking on overexpression, about 10 percent of genes showed overexpression when they were the overexpressed perturbagen itself.

My first question is: While this is truly a clear enrichment in the right direction, is this rather low efficiency of a gene as perturbagen on itself expected?

My second question is: Do you suggest to filter for genes that have an effect as perturbagen on itself for quality control?

To illustrate this issue, here is a histogram of z-scores showing effect as perturbagen on itselves vs. effect on other genes:

Thanks and best, Holger

Hello Daniel,

I am in the process of creating auto-update scripts for all the nodes with hetio, and in order to do that, I will need a copy of the most up-to-date modzs.gctx file. I know that GSEA has some LINCS datasets in there, and I was curious which files best correspond to the modzs file that you used in this repository. Any input or feedback would be greatly appreciated. Thank you!

Best,
Krish

Hey @dhimmel,

Thank you for such amazing work putting together the scripts to process and analyze the Lincs dataset. If it is not too much, could you add a README to the repo to guide us through the process?

Thank You.

Regards,
Yojana Gadiya

As shown in database.ipynb, there is a large-size l1000.db file, containing the gene expression profiles and meta data of lincs. Here could you publish the code to produce the l1000.db and/or the l1000.db itself? Thank you.