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License: GNU General Public License v3.0
BEELINE: evaluation of algorithms for gene regulatory network inference
License: GNU General Public License v3.0
Hi!
I am quite interested in the evaluation of different GRN inference models. However, I have a question. What is the point of having duplicated rows in the refNetwork.csv example file?
For example, the first 3 interactions are duplicated (SOX9 is also autoregulated). Sometimes there are also interactions that appear twice in different order, are the interactions in this file directional and this mean the regulate each other?
Thanks!
Line 122: ordering_genes not defined.
Hi,
I am doing an internship and my supervisor told me to read your paper and try to compare your results with newer gene regulatory networking algorithms like CellOracle on the same datasets.
The cluster I'm currently working with only supports Singulartiy and not Docker.
But i found out that Singularity is capable of pulling docker images.
After many attempts it always says:
FATAL: While making image from oci registry: failed to get checksum for
docker://grnbeeline/pidc:latest: Error reading manifest latest in
docker.io/grnbeeline/pidc: manifest unknown: manifest unknown
I wanted to make sure if anyone had the same problems working with Singularity or if theres even a solution for my problem
many thanks!
Hi all,
I'm trying to run the latest iteration of Beeline using the pre-build Docker container on a Linux Server running Fedora 27. Unfortunately, I'm still running into a few errors even when trying the simple example "GSD" provided in the repository.
First off, from Genie3 and GRNBoost2, i.e. the arboreto Docker, container I'm getting the following error:
docker run --rm -v /localscratch/home/ies/niclas.popp/BEELINE:/data/ --expose=41269 grnbeeline/arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GENIE3/time.txt python runArboreto.py --algo=GENIE3 --inFile=data/inputs/example/GSD/GENIE3/ExpressionData.csv --outFile=data/outputs/example/GSD/GENIE3/outFile.txt " docker: Error response from daemon: linux spec user: unable to find user root: no matching entries in passwd file. docker run --rm -v /localscratch/home/ies/niclas.popp/BEELINE:/data/ --expose=41269 grnbeeline/arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GRNBOOST2/time.txt python runArboreto.py --algo=GRNBoost2 --inFile=data/inputs/example/GSD/GRNBOOST2/ExpressionData.csv --outFile=data/outputs/example/GSD/GRNBOOST2/outFile.txt " docker: Error response from daemon: linux spec user: unable to find user root: no matching entries in passwd file. ERRO[0000] error waiting for container: context canceled
We already tried this but could not resolve the problem.
Also the output file "rankedEdges.csv" is not produced for some of the remaining algorithms (Scode, Sincerities, Leap, Grisli, Singe and Scribe) when running the config file provided in the repository and therefore the evaluation afterwards fails.
Any help from your side on how to fix this would be greatly appreciated, thank you very much in advance!
Curate 10, 30 and 50 node boolean GRN models, use our code to generate input data that includes ExpressionData.csv (a gene by cell matrix) and pseudotime ordering of cells.
Hi,
where do I find the ground truth for the example dataset in your paper?
thank you very much
Hey all!
I read your bioRxiv on "Benchmarking algorithms for gene regulatory network inferencefrom single-cell transcriptomic data" and was quite intrigued. I've been toying around with various forms of NI myself, and think that a benchmark for scNI methods is highly warranted.
I read that the synthetic networks are being generated by taking the module networks from dynverse/dyngen, converting them to ODEs using BoolODE and then running GeneNetWeaver. I was wondering why GNW is being used at all, since dyngen is also able to perform all of these steps. One of the benefits of dyngen is that it uses Gillespie's SSA instead of ODE's. SSA simulations keep track of the number of molecules in your cell (mrna, proteins), and simulates at each step which reaction takes place (e.g. transcribe a new mrna). This way it doesn't need need to generate random noise at each time step in order to simulate stochasticity. Instead, the stochasticity comes from which reactions are being triggered at each of the time steps.
Have you tried dyngen instead of GNW in order to perform the simulations?
What are your thoughts on this?
Robrecht
Synthetic Datasets (dynverse networks)
Hi Aditya,
I would like to ask what if the ground truth network I have does not have "+" (activation) or "-" (repression). Is it still possible to run evaluation process? I only know that there exist regulation relationship between Gene1 and Gene2, but are not sure about the gene pairs interaction type.
Thank you.
Best,
Che-Wei
There are two issues:
In runScribe.R, I'm getting this error at estimateDispersions:
https://github.com/Murali-group/Beeline/blob/master/Algorithms/SCRIBE/runScribe.R#L119
Error in `[.data.frame`(`*tmp*`, res$mu == 0) :
undefined columns selected
Calls: estimateDispersions ... eval_tidy -> disp_calc_helper_NB -> [ -> [.data.frame
Looks like this is the line in their code where the problem is happening:
https://github.com/cole-trapnell-lab/monocle-release/blob/7df105006756801a305ff43321b26d289cd6e890/R/expr_models.R#L470
According to this stack overflow question, I think they forgot a comma for indexing the dataframe.
I tried removing cells with all 0s and genes with all 0s, but that didn't help. Looks like there a couple of issues on Monocle with a similar problem, but there's no response from the developers.
This is the command I was running on csb2:
docker run --rm -v /data/jeff-law/projects/2019-04-single-cell/Beeline:/data/ monocle:base /bin/sh -c "time -v -o data/inputs/datasets/li-2017/SCRIBE/time0.txt Rscript runMonocle.R -e data/inputs/datasets/li-2017/SCRIBE/ExpressionData0.csv -c data/inputs/datasets/li-2017/SCRIBE/CellData0.csv -g data/inputs/datasets/li-2017/SCRIBE/GeneData.csv -o data/inputs/datasets/li-2017/SCRIBE/ -d 5 -l 0 -m ucRDI -x negbinomial.size --outFile outFile0.csv"
Check how long each method takes to run on different sizes of input: 10, 30, 50 nodes
Hi,
Thanks for the interesting package and collection of GRN methods! I ran into the following error when trying to run the eval.py
script on the included sample data:
$ pwd
/media/data/chris/beeline/Beeline
$ python eval.py --config config-files/config.yaml
eval.py:126: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details
.
config_map = yaml.load(config_file_handle)
<__main__.Evaluation object at 0x7fee34158cf8>
Evaluation started
Input folder for PIDC does not exist, creating input folder...
Input folder for GRNVBEM does not exist, creating input folder...
Input folder for GENIE3 does not exist, creating input folder...
Input folder for GRNBOOST2 does not exist, creating input folder...
Input folder for PPCOR does not exist, creating input folder...
Input folder for SCODE does not exist, creating input folder...
Input folder for SCNS does not exist, creating input folder...
Input folder for SINCERITIES does not exist, creating input folder...
Input folder for LEAP does not exist, creating input folder...
Input folder for GRISLI does not exist, creating input folder...
Input folder for SCINGE does not exist, creating input folder...
Input folder for SCRIBE does not exist, creating input folder...
Traceback (most recent call last):
File "eval.py", line 215, in <module>
main()
File "eval.py", line 206, in main
evaluation.runners[idx].run()
File "/media/data/chris/beeline/Beeline/src/runner.py", line 86, in run
AlgorithmMapper[self.name](self)
File "/media/data/chris/beeline/Beeline/src/pidcRunner.py", line 26, in run
inputPath = "data/" + str(RunnerObj.inputDir).split("RNMethods/")[1] + \
IndexError: list index out of range
It's not clear to me where RNMethods
is supposed to come from, but I was able to work around this by changing the inputPath derivation in each of the runner files:
inputPath = "data/" + str(RunnerObj.inputDir).split(str(Path.cwd()))[1] + "/PIDC/ExpressionData.csv"
The methods appear to be running on the sample data after this change, so I'm not sure if I missed a step somewhere.
Hi,
Thank you very much for this tool!!
I am wondering if one can generate the curated datasets (Fig. 3 of your paper) with less noise.
Best
Hi All,
Thanks for using SINGE (rebranded from SCINGE) in this paper. We read your paper recently, and some of the benchmarking figures are visuall very appealing. We have been updating SINGE quite a lot in the past couple of months, and wanted to update you on the same.
In the recent versions, we have also added guidance about parameter combinations and ensembling. This includes an updated default_hyperparameters.txt file which ensures that the first instinct of a new user to run would be to replicate the hyperparameters used in our paper. For users who would like to generate their own hyperparameter files, we have added s few scripts to do so in the scripts folder.
Based on your workflow, as well as dynverse, we were inspired to support our own Docker image, which is nearing its completion. We'd like to coordinate so that BEELINE users can have access to the latest version of SINGE as we continue our development.
By the way, one key attribute of SINGE is the aggregation stage on the ensemble generated. Do you think providing the aggregated scores in a matrix form (in addition to the ranked lists) would make it easier for the users to evaluate SINGE using BEELINE?
(I realize that the last couple of points may also be pertinent to #15 )
Color edges by the number of methods that recovered them. Post to GraphSpace.
Use as many edges from the ranked list in the reconstruction as are in the original wiring diagram.
Hi,
I reinstalled BEELINE, and it seems that BEELINE has been installed successfully. The command line output is as below:
zengyp@ubuntu:~/Single_cell/1/Beeline-master$ . setupAnacondaVENV.sh
Setting up Anaconda Python virtual environment...
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /home/zengyp/anaconda3/envs/BEELINE
added / updated specs:
- matplotlib==3.0.2
- networkx==2.2
- numpy==1.15.4
- pandas==0.23.4
- python=3.7.1
- pyyaml==5.1.1
- r=3.5.0
- rpy2==2.9.1
- scikit-learn==0.21.2
- seaborn==0.9.0
- tqdm==4.28.1
The following NEW packages will be INSTALLED:
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Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate BEELINE
#
# To deactivate an active environment, use
#
# $ conda deactivate
R version 3.5.0 (2018-04-23) -- "Joy in Playing"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'q()' to quit R.
> install.packages('https://cran.r-project.org/src/contrib/PRROC_1.3.1.tar.gz', type = 'source')
inferring 'repos = NULL' from 'pkgs'
trying URL 'https://cran.r-project.org/src/contrib/PRROC_1.3.1.tar.gz'
Content type 'application/x-gzip' length 335708 bytes (327 KB)
==================================================
downloaded 327 KB
* installing *source* package ‘PRROC’ ...
** package ‘PRROC’ successfully unpacked and MD5 sums checked
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (PRROC)
>
>
when I try to run 'python BLRunner.py --config config-files/config.yaml' for SCRIBE, it output an error, the command line output is as below:
(BEELINE) zengyp@ubuntu:~/Single_cell/1/Beeline-master$ python BLRunner.py --config config-files/config.yaml
Skipping PIDC
Skipping GRNVBEM
Skipping GENIE3
Skipping GRNBOOST2
Skipping PPCOR
Skipping SCODE
Skipping SCNS
Skipping SINCERITIES
Skipping LEAP
Skipping GRISLI
Skipping SINGE
<BLRun.BLRun object at 0x7fc6bb52c208>
Evaluation started
docker run --rm -v /home/zengyp/Single_cell/1/Beeline-master:/data/ scribe:base /bin/sh -c "time -v -o data/outputs/example/GSD/SCRIBE/time0.txt Rscript runScribe.R -e data/inputs/example/GSD/SCRIBE/ExpressionData0.csv -c data/inputs/example/GSD/SCRIBE/CellData0.csv -g data/inputs/example/GSD/SCRIBE/GeneData.csv -o data/outputs/example/GSD/SCRIBE/ -d 5 -l 0 -m ucRDI -x uninormal --outFile outFile0.csv -i"
Error in library(monocle, warn.conflicts = FALSE, quietly = TRUE) :
there is no package called ‘monocle’
Calls: suppressPackageStartupMessages -> withCallingHandlers -> library
Execution halted
docker run --rm -v /home/zengyp/Single_cell/1/Beeline-master:/data/ scribe:base /bin/sh -c "time -v -o data/outputs/example/GSD/SCRIBE/time1.txt Rscript runScribe.R -e data/inputs/example/GSD/SCRIBE/ExpressionData1.csv -c data/inputs/example/GSD/SCRIBE/CellData1.csv -g data/inputs/example/GSD/SCRIBE/GeneData.csv -o data/outputs/example/GSD/SCRIBE/ -d 5 -l 0 -m ucRDI -x uninormal --outFile outFile1.csv -i"
Error in library(monocle, warn.conflicts = FALSE, quietly = TRUE) :
there is no package called ‘monocle’
Calls: suppressPackageStartupMessages -> withCallingHandlers -> library
Execution halted
outputs/example/GSD/SCRIBE/outFile0.csv does not exist, skipping...
Evaluation complete
Hi,
I recently downloaded Beeline for my Bachelor thesis and Beeline comes along with example Datasets already which i could calculate.
I wanted to use my own Datasets or from the https://zenodo.org/record/3701939 website.
I wanted to make sure which zip, from the two listed there, i should download and where to implement them or in which files i need to change the exaple datasets with the new ones
Thanks for your help
Hi,
Thanks for this great tool.
In your documentation is says that it is possible to run base pipeline without a refNetwork.csv file. However when I try to run:
python BLRunner.py --config config-files/config_d0.yaml
I get the following error:
Traceback (most recent call last):
File "BLRunner.py", line 77, in <module>
main()
File "BLRunner.py", line 59, in main
evaluation = br.ConfigParser.parse(conf)
File "/scratch/projects/GRN/Beeline/BLRun/__init__.py", line 137, in parse
config_map['output_settings']))
File "/scratch/projects/GRN/Beeline/BLRun/__init__.py", line 67, in __init__
self.runners: Dict[int, Runner] = self.__create_runners()
File "/scratch/projects/GRN/Beeline/BLRun/__init__.py", line 88, in __create_runners
data['trueEdges'] = dataset['trueEdges']
KeyError: 'trueEdges'
Any help with this would be greatly appreciated.
Thanks
Hi,
When I try to run GENIE3 and GRNBOOST, it outputs an error: AttributeError: 'DataFrame' object has no attribute 'to_numpy'. The command line output is as below:
(BEELINE) zengyp@ubuntu:~/Single_cell/1/Beeline-master$ python BLRunner.py --config config-files/config.yaml
Skipping PIDC
Skipping GRNVBEM
Skipping PPCOR
Skipping SCODE
Skipping SCNS
Skipping SINCERITIES
Skipping LEAP
Skipping GRISLI
Skipping SINGE
Skipping SCRIBE
<BLRun.BLRun object at 0x7f15d1eae240>
Evaluation started
docker run --rm -v /home/zengyp/Single_cell/1/Beeline-master:/data/ --expose=41269 arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GENIE3/time.txt python runArboreto.py --algo=GENIE3 --inFile=data/inputs/example/GSD/GENIE3/ExpressionData.csv --outFile=data/outputs/example/GSD/GENIE3/outFile.txt "
Traceback (most recent call last):
File "runArboreto.py", line 43, in <module>
main(sys.argv)
File "runArboreto.py", line 32, in main
network = genie3(inDF.to_numpy(), client_or_address = client, gene_names = inDF.columns)
File "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py", line 3614, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'to_numpy'
docker run --rm -v /home/zengyp/Single_cell/1/Beeline-master:/data/ --expose=41269 arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GRNBOOST2/time.txt python runArboreto.py --algo=GRNBoost2 --inFile=data/inputs/example/GSD/GRNBOOST2/ExpressionData.csv --outFile=data/outputs/example/GSD/GRNBOOST2/outFile.txt "
Traceback (most recent call last):
File "runArboreto.py", line 43, in <module>
main(sys.argv)
File "runArboreto.py", line 36, in main
network = grnboost2(inDF.to_numpy(), client_or_address = client, gene_names = inDF.columns)
File "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py", line 3614, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'to_numpy'
Traceback (most recent call last):
File "BLRunner.py", line 79, in <module>
main()
File "BLRunner.py", line 73, in main
evaluation.runners[idx].parseOutput()
File "/home/zengyp/Single_cell/1/Beeline-master/BLRun/runner.py", line 90, in parseOutput
OutputParser[self.name](self)
File "/home/zengyp/Single_cell/1/Beeline-master/BLRun/genie3Runner.py", line 60, in parseOutput
OutDF = pd.read_csv(outDir+'outFile.txt', sep = '\t', header = 0)
File "/home/zengyp/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 678, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/zengyp/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 440, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/zengyp/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 787, in __init__
self._make_engine(self.engine)
File "/home/zengyp/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 1014, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/home/zengyp/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 1708, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 384, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 695, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: File b'outputs/example/GSD/GENIE3/outFile.txt' does not exist
These ground-truth networks are not a part of any of our supplemental materials, including Zenodo. We will add scripts to download and pre-process them.
I quite liked Figure 3 from the bioRxiv paper:
I had a few remarks / questions:
Hello,
I followed your instructions and have successfully installed BEELINE. Yet when I run python BLRunner.py --config config-files/config.yaml
, only two algorithm, PIDC and GRNVBEM are successfully run. The others have error messages such as:
(BEELINE) raphael830102@beeline-cwh:~/Beeline$ python BLRunner.py --config=config-files/new_config.yaml Skipping PIDC Skipping GRNVBEM Skipping GRNBOOST2 Skipping PPCOR Skipping SCODE Skipping SCNS Skipping LEAP Skipping GRISLI Skipping SINGE Skipping SCRIBE <BLRun.BLRun object at 0x7f29f320a080> Evaluation started docker run --rm -v /home/raphael830102/Beeline:/data/ --expose=41269 arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GENIE3/time.txt python runArboreto.py --algo=GENIE3 --inFile=data/inputs/example/G SD/GENIE3/ExpressionData.csv --outFile=data/outputs/example/GSD/GENIE3/outFile.txt " Traceback (most recent call last): File "runArboreto.py", line 43, in <module> main(sys.argv) File "runArboreto.py", line 32, in main network = genie3(inDF, client_or_address = client) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 73, in genie3 limit=limit, seed=seed, verbose=verbose) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 115, in diy expression_matrix, gene_names, tf_names = _prepare_input(expression_data, gene_names, tf_names) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 214, in _prepare_input expression_matrix = expression_data.as_matrix() File "/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py", line 5130, in __getattr__ return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'as_matrix' docker run --rm -v /home/raphael830102/Beeline:/SINCERITIES/data/ sincerities:base /bin/sh -c "time -v -o data/outputs/example/GSD/SINCERITIES/time0.txt Rscript MAIN.R data/inputs/example/GSD/SINCERITIES/Expressio nData0.csv data/outputs/example/GSD/SINCERITIES/outFile0.txt " Loading required package: SuppDists Loading required package: Matrix Loaded glmnet 4.0-2 Loading required package: MASS [1] "data/inputs/example/GSD/SINCERITIES/ExpressionData0.csv" [2] "data/outputs/example/GSD/SINCERITIES/outFile0.txt" Error in coef.cv.glmnet(CV_results, s = "lambda.min") : could not find function "coef.cv.glmnet" Calls: SINCERITITES In addition: There were 21 warnings (use warnings() to see them) Execution halted docker run --rm -v /home/raphael830102/Beeline:/SINCERITIES/data/ sincerities:base /bin/sh -c "time -v -o data/outputs/example/GSD/SINCERITIES/time1.txt Rscript MAIN.R data/inputs/example/GSD/SINCERITIES/Expressio nData1.csv data/outputs/example/GSD/SINCERITIES/outFile1.txt " Loading required package: SuppDists Loading required package: Matrix Loaded glmnet 4.0-2 Loading required package: MASS [1] "data/inputs/example/GSD/SINCERITIES/ExpressionData1.csv" [2] "data/outputs/example/GSD/SINCERITIES/outFile1.txt" Error in coef.cv.glmnet(CV_results, s = "lambda.min") : could not find function "coef.cv.glmnet" Calls: SINCERITITES In addition: There were 21 warnings (use warnings() to see them) Execution halted Traceback (most recent call last): File "BLRunner.py", line 77, in <module> main() File "BLRunner.py", line 71, in main evaluation.runners[idx].parseOutput() File "/home/raphael830102/Beeline/BLRun/runner.py", line 90, in parseOutput OutputParser[self.name](self) File "/home/raphael830102/Beeline/BLRun/genie3Runner.py", line 60, in parseOutput OutDF = pd.read_csv(outDir+'outFile.txt', sep = '\t', header = 0) File "/home/raphael830102/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 678, in parser_f return _read(filepath_or_buffer, kwds) File "/home/raphael830102/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 440, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "/home/raphael830102/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 787, in __init__ self._make_engine(self.engine) File "/home/raphael830102/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 1014, in _make_engine self._engine = CParserWrapper(self.f, **self.options) File "/home/raphael830102/anaconda3/envs/BEELINE/lib/python3.7/site-packages/pandas/io/parsers.py", line 1708, in __init__ self._reader = parsers.TextReader(src, **kwds) File "pandas/_libs/parsers.pyx", line 384, in pandas._libs.parsers.TextReader.__cinit__ File "pandas/_libs/parsers.pyx", line 695, in pandas._libs.parsers.TextReader._setup_parser_source FileNotFoundError: File b'outputs/example/GSD/GENIE3/outFile.txt' does not exist
I wonder if I have missed out any critical step, would be nice if you would provide some suggestions.
Thanks a lot.
Hello,
I tried running all the methods supported in BEELINE on your example GSD data following the steps you provide in the documentation. I am getting this error for GRNBoost2 and GENIE3:
docker run --rm -v /Users/sotolm/Documents/PR-scGRN/Beeline:/data pidc:base /bin/sh -c "time -v -o data/outputs/example/GSD/PIDC/time.txt julia runPIDC.jl data/inputs/example/GSD/PIDC/ExpressionData.csv data/outputs/example/GSD/PIDC/outFile.txt " 4.710384 seconds (11.83 M allocations: 3.117 GiB, 7.00% gc time) 3.156385 seconds (10.83 M allocations: 531.753 MiB, 7.40% gc time) docker run --rm -v /Users/sotolm/Documents/Beeline:/data/ --expose=41269 arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GENIE3/time.txt python runArboreto.py --algo=GENIE3 --inFile=data/inputs/example/GSD/GENIE3/ExpressionData.csv --outFile=data/outputs/example/GSD/GENIE3/outFile.txt " Traceback (most recent call last): File "runArboreto.py", line 43, in <module> main(sys.argv) File "runArboreto.py", line 32, in main network = genie3(inDF, client_or_address = client) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 73, in genie3 limit=limit, seed=seed, verbose=verbose) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 115, in diy expression_matrix, gene_names, tf_names = _prepare_input(expression_data, gene_names, tf_names) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 214, in _prepare_input expression_matrix = expression_data.as_matrix() File "/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py", line 5274, in __getattr__ return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'as_matrix'
docker run --rm -v /Users/sotolm/Documents/PR-scGRN/Beeline:/data/ --expose=41269 arboreto:base /bin/sh -c "time -v -o data/outputs/example/GSD/GRNBOOST2/time.txt python runArboreto.py --algo=GRNBoost2 --inFile=data/inputs/example/GSD/GRNBOOST2/ExpressionData.csv --outFile=data/outputs/example/GSD/GRNBOOST2/outFile.txt " Traceback (most recent call last): File "runArboreto.py", line 43, in <module> main(sys.argv) File "runArboreto.py", line 36, in main network = grnboost2(inDF, client_or_address = client) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 41, in grnboost2 early_stop_window_length=early_stop_window_length, limit=limit, seed=seed, verbose=verbose) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 115, in diy expression_matrix, gene_names, tf_names = _prepare_input(expression_data, gene_names, tf_names) File "/opt/conda/lib/python3.7/site-packages/arboreto/algo.py", line 214, in _prepare_input expression_matrix = expression_data.as_matrix() File "/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py", line 5274, in __getattr__ return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'as_matrix'
Additionally, I get this error for GENIE3:
Traceback (most recent call last): File "BLRunner.py", line 77, in <module> main() File "BLRunner.py", line 71, in main evaluation.runners[idx].parseOutput() File "/Users/sotolm/Documents/Beeline/BLRun/runner.py", line 90, in parseOutput OutputParser[self.name](self) File "/Users/sotolm/Documents/Beeline/BLRun/genie3Runner.py", line 60, in parseOutput OutDF = pd.read_csv(outDir+'outFile.txt', sep = '\t', header = 0) File "/Users/sotolm/usr/local/bin/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 676, in parser_f return _read(filepath_or_buffer, kwds) File "/Users/sotolm/usr/local/bin/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 448, in _read parser = TextFileReader(fp_or_buf, **kwds) File "/Users/sotolm/usr/local/bin/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 880, in __init__ self._make_engine(self.engine) File "/Users/sotolm/usr/local/bin/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1114, in _make_engine self._engine = CParserWrapper(self.f, **self.options) File "/Users/sotolm/usr/local/bin/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1891, in __init__ self._reader = parsers.TextReader(src, **kwds) File "pandas/_libs/parsers.pyx", line 374, in pandas._libs.parsers.TextReader.__cinit__ File "pandas/_libs/parsers.pyx", line 673, in pandas._libs.parsers.TextReader._setup_parser_source FileNotFoundError: [Errno 2] File outputs/example/GSD/GENIE3/outFile.txt does not exist: 'outputs/example/GSD/GENIE3/outFile.txt'
I believe the first two are issues with the version of pandas, the attribute as.martix
was deprecated since version 0.23.0 and the one listed is 0.23.4. I tried changing the version of pandas in the requirements.txt
but it clashes with the versions of other packages.
Compute spearman's rank correlation for each pair of datasets.
We have shown that GNW output does not display the any meaningful qualitative structure (like linear or bifurcating trajectories) for a given Dynverse network. This is our argument for using the dynverse and literature curated models in order to see "meaningful" trajectories. However, we have not yet demonstrated that giving the network inference methods such biologically interpretable trajectories leads to better results. For this we have to compare performance of the methods on dynverse and GNW data for a given model.
cc @adyprat
Hello again, I was wondering if you've posted the ground truth networks for the experimental single-cell RNA-seq datasets from the paper anywhere. Thanks for your help!
Hello.
In the paper(https://www.biorxiv.org/content/10.1101/642926v3.full),
"We simulate these networks using BoolODE, a method we have developed to convert Boolean models into systems of differential equations (Supplementary Section S1)", but I can't see the Supplementary Section1.
So, I cant't know how to create the input data for synthetic network.
I'd like to use the synthetic data which used in this paper.
Could you upload Supplementary Section1 and Table2 ?
Thank you.
Look for motifs specific to cell differentiation (like the one described in: "https://www.nature.com/articles/nature08533#gene-regulatory-networks-and-cell-fate-attractors"
Hi Beeline team,
I am currently using your neat pipeline, while I have encountered a very wird typo in the rankedEdges.csv file of PIDC results. It seems that on my datasets, the edge weights measured by PIDC is all nan values, like this
But! after I used a search algorithm developed by myself ( this algorithm need to repeat run PIDC, which could not be applied on the large-scale scRNA-seq datasets), I found that just delete some of the genes (in my cases, the 441th,865th,866th genes), the edge weights are back to normal ??
I originally thought that may be these genes have some bad statistical characteristics, but regretly that I didn't find any special properties of those genes. (e.g. average expression, variance, coefficients of variation, etc...)
I found this thing is happened in most of my datasets, so I think its really important to be figured out, but I have no idea about how to solve it.
In order to let your team to check this typo, I have create a repo and upload the ExpressionData.csv, https://github.com/WWXkenmo/PIDC_bug
Best,
Ken
Hello. Thank you for answering my question before.
Please teach me whether the program consider the edge's direction or not in calculating roc_curve .
In /BLEval/computeDGAUC.py line221, I can see the only 1 or 0 in the dataframe (outDF["TrueEDges"]).
but in the paper(https://www.biorxiv.org/content/10.1101/642926v3.full#ref-10) about the Synthetic data it shows that there are activation and repression edges.
I think there are algorithms that consider the edge's direction and not, that's why they don't consider the direction in evaluation.
I'm sorry if I'm saying too directly or impolitely.
I appreciate for you and this program.
Building docker images from Dockerfile no longer works easily. There are several reasons for this:
RUN apt-get update
in the Dockerfiles is needed to install some packages/libraries, and can potentially break things down the line).The only way I can think of getting around this issue is to make the images built at the time of manuscript preparation available via Docker hub. While building an image using Dockerfile should work (until it wont), the initial set-up going forward is to going to pull an existing image instead of relying on Dockerfiles.
An example image is already available here: https://hub.docker.com/repository/docker/grnbeeline/scribe
Here are the steps needed to push existing images (for future reference):
docker login --username <your username>
docker images
docker tag <image ID> <docker hub username>/<algorithm name>:<tag>
docker tag <algorithm name>:<tag> <docker hub username>/<algorithm name>:<tag>
docker push <docker hub username>/<algorithm name>:<tag>
If for some reason you need to update an existing image to create new versions:
docker run -it <docker hub username>/<algorithm name>:<tag> /bin/bash
--rm
flag while running docker in the previous step, the modified container will be available for later use, even after exiting.docker ps -a
docker commit <container ID> <docker hub username>/<algorithm name>:<new tag>
docker push <docker hub username>/<algorithm name>:<new tag>
PCA or tSNE on the adjacency matrix (converted to a "vector").
Hi,
I was trying to run grnbeeline/arboreto:base through BLRunner.py as the following command.
docker run --rm -v /home/abc/projects/Beeline:/data/ --expose=41269 grnbeeline/arboreto:base /bin/sh -c "time -v -o data/outputs/Synthetic/dyn-LI/dyn-LI-100-1/GENIE3/time.txt python runArboreto.py --algo=GENIE3 --inFile=data/inputs/Synthetic/dyn-LI/dyn-LI-100-1/GENIE3/ExpressionData.csv --outFile=data/outputs/Synthetic/dyn-LI/dyn-LI-100-1/GENIE3/outFile.txt "
However, an error occurred and the program stuck.
Task exception was never retrieved
future: <Task finished coro=<connect.<locals>._() done, defined at /opt/conda/lib/python3.7/site-packages/distributed/comm/core.py:288> exception=CommClosedError()>
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 297, in _
handshake = await asyncio.wait_for(comm.read(), 1)
File "/opt/conda/lib/python3.7/asyncio/tasks.py", line 435, in wait_for
await waiter
concurrent.futures._base.CancelledError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 304, in _
raise CommClosedError() from e
distributed.comm.core.CommClosedError
tornado.application - ERROR - Exception in callback functools.partial(<bound method IOLoop._discard_future_result of <tornado.platform.asyncio.AsyncIOLoop object at 0x7f4a22da7250>>, <Task finished coro=<SpecCluster._correct_state_internal() done, defined at /opt/conda/lib/python3.7/site-packages/distributed/deploy/spec.py:320> exception=OSError("Timed out trying to connect to 'inproc://172.17.0.2/10/1' after 10 s: Timed out trying to connect to 'inproc://172.17.0.2/10/1' after 10 s: connect() didn't finish in time")>)
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 322, in connect
_raise(error)
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 275, in _raise
raise IOError(msg)
OSError: Timed out trying to connect to 'inproc://172.17.0.2/10/1' after 10 s: connect() didn't finish in time
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/tornado/ioloop.py", line 743, in _run_callback
ret = callback()
File "/opt/conda/lib/python3.7/site-packages/tornado/ioloop.py", line 767, in _discard_future_result
future.result()
File "/opt/conda/lib/python3.7/site-packages/distributed/deploy/spec.py", line 401, in _close
await self._correct_state()
File "/opt/conda/lib/python3.7/site-packages/distributed/deploy/spec.py", line 328, in _correct_state_internal
await self.scheduler_comm.retire_workers(workers=list(to_close))
File "/opt/conda/lib/python3.7/site-packages/distributed/core.py", line 810, in send_recv_from_rpc
comm = await self.live_comm()
File "/opt/conda/lib/python3.7/site-packages/distributed/core.py", line 772, in live_comm
**self.connection_args,
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 334, in connect
_raise(error)
File "/opt/conda/lib/python3.7/site-packages/distributed/comm/core.py", line 275, in _raise
raise IOError(msg)
OSError: Timed out trying to connect to 'inproc://172.17.0.2/10/1' after 10 s: Timed out trying to connect to 'inproc://172.17.0.2/10/1' after 10 s: connect() didn't finish in time
The error is not stable that there is a probability of the error in different places in multiple attempts.
Additionally, the containers are running under docker's bridge network.
Hello,
I encountered the following error when building the SCODE docker container:
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:
The following packages have unmet dependencies:
libc6-dev : Breaks: libgcc-8-dev (< 8.4.0-2~) but 8.3.0-3 is to be installed
E: Error, pkgProblemResolver::Resolve generated breaks, this may be caused by held packages.
To resolve it, I had to modify Dockerfile to install gcc-8-base
after running apt-get update
Add updated documentation for using BEELINE.
We should read each paper carefully to check that we are following the best practices suggested by the authors, if there are any. Use this issue to record what we read.
Hi,
I have successfully installed all the images, but I have some problems when I run BLRunner.py with 'should_run = [True]' for SCRIBE, GRISLI, SINGE, GRNVBEM, GENIE3 and GRNBOOST2. The problems are presented in the images. I do not know how to solve them. Hope that you can give me some advice. Thank you very much!
Best,
Yanping
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