csoneson / conquer_comparison Goto Github PK

This is amazing work!! I really appreciate that you made the expression data and the source codes available.

I was trying to apply your function on my data and was having a hard time constructing an "L" object to feed into any apply*.R function. Say, I would like to apply run_BPSC() on data set GSE74596, so I did the following things:

GSE74596 <- readRDS("GSE74596.rds")
GSE74596 <- updateObject(GSE74596)
experiments(GSE74596)
(GSE74596_gene <- experiments(GSE74596)[["gene"]])
L <- list()
L$count <- assays(GSE74596_gene)[["count"]]
L$condt <- colData(GSE74596)$characteristics_ch1.5

Could you please let me know if this is the right way to prepare an "L" object or if I need another function to do it? Any comments would be greatly appreciated.

Please let me know if this is not the right place to post questions,

Thanks,

Gabby

Thank you for this great work, I expect it will save people like me a lot of time on testing out differential analysis method.

I was trying to just run this code on the provided data, but I am running into few issues.

First of all, I can't seem to install powsim as one of its dependencies, gu-mi/NBGOF cannot be installed due to ELF header error. Instead, I installed powsimR, powsim's update, which only runs on 3.4.0 or newer versions of R. As such, running powsimR introduces another issue of opening rds file, which I presume was created in R version 3.3.2, in R version 3.4.0. Using readRDS on the config$mae produces error stating the RDS file is outdated and suggests using updatedObject function. However, using updatedObject after readRDS results in an error below.

Error in validObject(.Object) :
class “MultiAssayExperiment” object: 'sampleMap' assay column not a factor

Please let me know if you know a fix to either installing powsim, or loading provided rds data in newer versions of R.

Additionally, I could not install two additional packages, DEsingle and NODES. These are two of the methods that are being tested, but I cannot find sources that has these packages.

Thank you for reading.

Hi,

An rds file is missing. data/cell_cycle_geneids.rds .

Dear Charlotte,

Thanks for your intensive work on the evaluation of DE methods. When reading the paper https://www.nature.com/articles/nmeth.4612, we found it unclear on how did you simulate the datasets with signals, which contain 10% DE genes. Could you describe it in details?

Thank you very much for putting the results available to download: http://imlspenticton.uzh.ch/robinson_lab/conquer_de_comparison/

I would like to regenerate Fig.1 in your paper: https://www.nature.com/articles/nmeth.4612
It seems to me that the data of fig 1 is in the folder: conquer_comparison/results
But in the results there are 1754 rds files, it's not clear for me how to generate Fig.1 from this result.

Could you clarify?

Thank you very much!

I'm trying to run the Makefile, but there are lots of issues. Could you update the script to R3.5 or provide a docker image of your working environment? Also, can you provide a testing include_datasets.mk containing minimum amount of datasets to test the Makefile?

Here are some issues:

1. Under R3.3, it's very difficult to install some packages; for example, "SingleCellExperiment". Even its old version requires R>=3.4. Some packages reside only in github and do not provide old versions compatible with R3.3
2. Since your scripts were written, some packages have changed their name; for example, powsim is now powsimR. And some packages have changed their function parameters or return values.
3. The provided data bundle does not have some of the text files required by the Makefile. And the RDS files do not work with new version of MultiAssayExperiment, for example, on the pData slot.

I can't find L, a data obj used in the almost all the apply- method in the repository. It will great if you could post it or a script used to generate L posted in the repository.

Thanks.

To create a new Apply_*.R, what should be the return value of the method? I see that those Apply_*.R returns a list(session_info,timing, res, df), what should be the content of the res?

Thanks a lot!

In the paper, you described "prefiltering of lowly expressed genes" will improve edgeR/QLF method. Could you tell me what filtering criteria you applied to improve the results?

Thank you very much.