├── .gitignore
├── Escher maps
├── Amino Acids Metabolism.json
├── Carbohydrates Metabolism.json
├── Energy & Nucleotide Metabolism.json
├── Glycan Metabolism.json
├── Lipids Metabolism.json
└── Vitamin & Cofactor Metabolism.json
├── Images
├── CellFie3.png
├── EscherTutorial
│ ├── image1.png
│ ├── image2.png
│ ├── image3.png
│ ├── image4.png
│ ├── image5.png
│ └── image6.png
├── GenePatternTutorial
│ ├── GP_image21.png
│ ├── GP_image22A.png
│ ├── GP_image22B.png
│ ├── GP_image22C.png
│ ├── GP_image3.png
│ └── GP_image31.png
└── LogoCellFie.png
├── LICENSE
├── README.md
├── docs
├── GenePattern_Info.docx
└── NCBI_Entrez_gene_ID_models.xlsx
├── genepattern
├── Dockerfile
├── bashwrapper.sh
├── build_docker_container.sh
├── cellfieout.mat
├── execCellfie.m
├── global_percentile.yaml
├── global_value.yaml
├── gpunit.yaml
├── local_mean.yaml
├── local_minmaxmean_percentile.yaml
├── local_minmaxmean_value.yaml
├── outtmp
│ ├── detailScoring.csv
│ ├── score.csv
│ ├── score_binary.csv
│ └── taskInfo.csv
├── run_cellfie_in_docker.sh
└── testexec.m
├── initCellFie.m
├── input
├── CONSENSUS_TASKS.xls
├── CONSENSUS_TASKS_210tasks.xls
├── List_GenesInModels.xlsx
├── MT_iCHOv1_final.mat
├── MT_iHsa.mat
├── MT_iMM1415.mat
├── MT_iRno.mat
├── MT_recon_2_2_entrez.mat
├── essentialRxns
│ ├── essRxns_210version
│ │ ├── essentialRxnsbyTask210_MT_iCHOv1_final.mat
│ │ ├── essentialRxnsbyTask210_MT_iHsa.mat
│ │ ├── essentialRxnsbyTask210_MT_iMM1415.mat
│ │ ├── essentialRxnsbyTask210_MT_iRno.mat
│ │ ├── essentialRxnsbyTask210_MT_inesMouseModel.mat
│ │ ├── essentialRxnsbyTask210_MT_quek14.mat
│ │ ├── essentialRxnsbyTask210_MT_recon_1.mat
│ │ ├── essentialRxnsbyTask210_MT_recon_2.mat
│ │ └── essentialRxnsbyTask210_MT_recon_2_2.mat
│ ├── essentialRxnsbyTask_Cho_gem.mat
│ ├── essentialRxnsbyTask_MT_iCHOv1_final.mat
│ ├── essentialRxnsbyTask_MT_iHsa.mat
│ ├── essentialRxnsbyTask_MT_iMM1415.mat
│ ├── essentialRxnsbyTask_MT_iRno.mat
│ ├── essentialRxnsbyTask_MT_inesMouseModel.mat
│ ├── essentialRxnsbyTask_MT_quek14.mat
│ ├── essentialRxnsbyTask_MT_recon_1.mat
│ ├── essentialRxnsbyTask_MT_recon_2.mat
│ ├── essentialRxnsbyTask_MT_recon_2_2.mat
│ └── essentialRxnsbyTask_MT_recon_2_2_entrez.mat
├── inactive_reactions.csv
├── parsedGPR
│ ├── parsedGPR_MT_iCHOv1_final.mat
│ ├── parsedGPR_MT_iHsa.mat
│ ├── parsedGPR_MT_iMM1415.mat
│ ├── parsedGPR_MT_iRno.mat
│ ├── parsedGPR_MT_recon_2_2_entrez.mat
│ └── test
├── taskStructure.mat
└── taskStructure210tasks.mat
├── matlab_compiled.zip
├── matlab_compiled
├── execCellfie.prj
└── execCellfie
│ ├── PackagingLog.html
│ ├── for_redistribution
│ ├── MyAppInstaller_web.app
│ │ └── Contents
│ │ │ ├── Info.plist
│ │ │ ├── MacOS
│ │ │ ├── setup
│ │ │ └── setup.dSYM
│ │ │ │ └── Contents
│ │ │ │ ├── Info.plist
│ │ │ │ └── Resources
│ │ │ │ └── DWARF
│ │ │ │ └── setup
│ │ │ └── Resources
│ │ │ ├── bundle.zip
│ │ │ ├── en.lproj
│ │ │ └── MainMenu.nib
│ │ │ ├── installer.icns
│ │ │ └── splash.png
│ └── MyAppInstaller_web.install
│ ├── for_redistribution_files_only
│ ├── default_icon.icns
│ ├── default_icon_48.png
│ ├── execCellfie
│ ├── execCellfie.app
│ │ └── Contents
│ │ │ ├── Info.plist
│ │ │ ├── MacOS
│ │ │ ├── applauncher
│ │ │ ├── execCellfie
│ │ │ └── prelaunch
│ │ │ └── Resources
│ │ │ ├── Base.lproj
│ │ │ ├── About.nib
│ │ │ ├── MWOpenAccessoryView.nib
│ │ │ ├── MWSaveAccessoryView.nib
│ │ │ └── MainMenu.nib
│ │ │ └── membrane.icns
│ ├── readme.txt
│ ├── run_execCellfie.sh
│ └── splash.png
│ └── for_testing
│ ├── execCellfie
│ ├── execCellfie.app
│ └── Contents
│ │ ├── Info.plist
│ │ ├── MacOS
│ │ ├── applauncher
│ │ ├── execCellfie
│ │ └── prelaunch
│ │ └── Resources
│ │ ├── Base.lproj
│ │ ├── About.nib
│ │ ├── MWOpenAccessoryView.nib
│ │ ├── MWSaveAccessoryView.nib
│ │ └── MainMenu.nib
│ │ └── membrane.icns
│ ├── mccExcludedFiles.log
│ ├── readme.txt
│ ├── requiredMCRProducts.txt
│ ├── run_execCellfie.sh
│ └── splash.png
├── src
├── CellFie.m
├── CellFie_slow.m
├── findRxnIDs.m
├── findUsedGenesLevels.m
├── findUsedGenesLevels_all.m
├── prctile.m
├── runCellFie.m
├── selectGeneFromGPR.m
├── selectGeneFromGPR_all.m
├── tabulate.m
└── wide2long.m
└── test
└── suite
├── EscherDataTest1.csv
├── dataRecon22_global_percentile.detailScoring.csv
├── dataRecon22_global_percentile.mat
├── dataRecon22_global_percentile.score.csv
├── dataRecon22_global_percentile.score_binary.csv
├── dataRecon22_global_percentile.taskInfo.csv
├── dataRecon22_global_value.detailScoring.csv
├── dataRecon22_global_value.mat
├── dataRecon22_global_value.score.csv
├── dataRecon22_global_value.score_binary.csv
├── dataRecon22_global_value.taskInfo.csv
├── dataRecon22_local_mean.detailScoring.csv
├── dataRecon22_local_mean.mat
├── dataRecon22_local_mean.score.csv
├── dataRecon22_local_mean.score_binary.csv
├── dataRecon22_local_mean.taskInfo.csv
├── dataRecon22_local_minmaxmean_percentile.detailScoring.csv
├── dataRecon22_local_minmaxmean_percentile.mat
├── dataRecon22_local_minmaxmean_percentile.score.csv
├── dataRecon22_local_minmaxmean_percentile.score_binary.csv
├── dataRecon22_local_minmaxmean_percentile.taskInfo.csv
├── dataRecon22_local_minmaxmean_value.detailScoring.csv
├── dataRecon22_local_minmaxmean_value.mat
├── dataRecon22_local_minmaxmean_value.score.csv
├── dataRecon22_local_minmaxmean_value.score_binary.csv
├── dataRecon22_local_minmaxmean_value.taskInfo.csv
├── dataTest.csv
├── dataTest.mat
├── dataTest.xlsx
├── detailScoring.csv
└── testCellFie.m
/.gitignore:
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1 |
2 | coverage.json
3 | coverage_html
4 | *.asv
5 | *.m~
6 | *.mex*
7 | .DS_Store
8 | shinyapp/CellFie/v94/
9 |
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/Images/CellFie3.png:
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/Images/LogoCellFie.png:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/Images/LogoCellFie.png
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/README.md:
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1 | # **CellFie**: Cellular Functions InferencE
2 |
3 | 
4 |
5 | CellFie is a computational framework to quantity a cells' metabolic functions.
6 |
7 |
8 |
9 | This framework is available as a web-based module in the list of tools of [GenePattern](https://www.genepattern.org). See [here](https://github.com/LewisLabUCSD/CellFie/wiki/Tutorial-:-GenePattern-module) information about the GenePattern module
10 |
11 | The [source code](https://github.com/LewisLabUCSD/CellFie/blob/master/src/CellFie.m) is running on Matlab. See [here](https://github.com/LewisLabUCSD/CellFie/wiki/Running-CellFie-in-Matlab) information about how to run Cellfie using Matlab
12 |
13 | Detailed explanations of the methods and tools related to CellFie are available on the [wiki section](https://github.com/LewisLabUCSD/CellFie/wiki) of this repository
14 |
15 |
16 |
17 | We welcome any comments, bug reports, and feature requests. Please send all feedback to arichelleres@gmail.com
18 |
19 |
20 |
21 | **How to cite?**
22 |
23 | A. Richelle, B.P. Kellman, A.T. Wenzel, A.W.T. Chiang, T.Reagan, J.M. Gutierrez, C. Joshi, S. Li, J.K. Liu, H. Masson, J. Lee, Z. Li, L. Heirendt, C. Trefois, E.F. Juarez, T. Bath, D. Borland, J.P. Mesirov, K. Robasky, N.E. Lewis. Model-based assessment of mammalian cells metabolic functionalities using omics data. Cell Reports Methods; doi: https://doi.org/10.1016/j.crmeth.2021.100040
24 |
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/docs/GenePattern_Info.docx:
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/docs/NCBI_Entrez_gene_ID_models.xlsx:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/docs/NCBI_Entrez_gene_ID_models.xlsx
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/genepattern/Dockerfile:
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1 | ## Copied from https://hub.docker.com/r/spacetimeanalytics/docker-matlab-runtime/dockerfile
2 | ## but changed to Ubuntu 18.04
3 |
4 | ## docker build -f genepattern/Dockerfile -t cellfie .
5 |
6 | FROM ubuntu:18.04
7 |
8 | RUN apt-get update && apt-get install -y zip unzip wget libc6 libxt-dev
9 |
10 | ## R2018a
11 | RUN mkdir /tmp/mcr_installer && \
12 | cd /tmp/mcr_installer && \
13 | wget http://ssd.mathworks.com/supportfiles/downloads/R2018a/deployment_files/R2018a/installers/glnxa64/MCR_R2018a_glnxa64_installer.zip && \
14 | unzip MCR_R2018a_glnxa64_installer.zip && \
15 | ./install -mode silent -agreeToLicense yes && \
16 | rm -Rf /tmp/mcr_installer
17 |
18 | RUN ln -sf /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /usr/local/MATLAB/MATLAB_Runtime/v94/sys/os/glnxa64/libstdc++.so.6
19 |
20 | ENV MCRROOT=/usr/local/MATLAB/MATLAB_Runtime/v94 MCR_CACHE_ROOT=/tmp
21 | ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$MCRROOT/runtime/glnxa64:$MCRROOT/bin/glnxa64:$MCRROOT/sys/os/glnxa64:$MCRROOT/sys/opengl/lib/glnxa64:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64/server:$MCRROOT/sys/java/jre/glnxa64/jre/lib/amd64
22 |
23 | RUN mkdir CellFie
24 |
25 | COPY ./ /CellFie/
26 |
27 | CMD []
28 |
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/genepattern/bashwrapper.sh:
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1 | # RUN:
2 | # cd genepattern/
3 | # ./bashwrapper.sh 'test/suite/dataTest.mat' 3 'test/suite/MT_recon_2_2_entrez.mat' 'local' 'value' 'minmaxmean' 25 75
4 | ## read parameters
5 | #load('test/suite/dataTest.mat')
6 | DATA=$1
7 | #SampleNumber=3;
8 | SAMP=$2
9 | #ref='test/suite/MT_recon_2_2_entrez.mat';
10 | REF=$3
11 | #param.ThreshType='local';
12 | pTHRESH=$4
13 | #param.percentile_or_value='value';
14 | pPERCVAL=$5
15 | #param.LocalThresholdType='minmaxmean';
16 | pTYPE=$6
17 | #param.value_low=25;
18 | pLOW=$7
19 | #param.value_high=75;
20 | pHIGH=$8
21 |
22 | #Run
23 | CMD="addpath(genpath('..'));execCellfie('$DATA',$SAMP,'$REF','$pTHRESH','$pPERCVAL','$pTYPE',$pLOW,$pHIGH);exit"
24 | echo $CMD
25 | matlab -nosplash -nodesktop -r $CMD
26 |
27 |
28 |
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/genepattern/build_docker_container.sh:
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1 | docker build -f genepattern/Dockerfile -t cellfie .
2 |
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/genepattern/cellfieout.mat:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/genepattern/cellfieout.mat
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/genepattern/execCellfie.m:
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1 | function []=execCellfie(DATA,SAMP,REF,pTHRESH,pPERCVAL,pGLOBAL,pTYPE,pLOW,pHIGH,outputdir)
2 | if contains(DATA,'mat')
3 | load(DATA);
4 | elseif( contains(DATA,'csv')||contains(DATA,'tsv')||contains(DATA,'xlsx')||contains(DATA,'xls'))
5 | datatmp=readtable(DATA);
6 | genetmp=table2array(datatmp(:,1));
7 | tmp = struct('gene',table2array(datatmp(:,1)),'value',table2array(datatmp(:,2:end)));
8 | tmp.gene = num2cell(tmp.gene);
9 | for i=1:length(genetmp)
10 | tmp.gene{i}=int2str(genetmp(i));
11 | end
12 | data=tmp;
13 | else
14 | error('Your data file must be formatted as: .mat, .csv, .tsv, .xlsx, or .xls with the correct suffix')
15 | end
16 | SampleNumber=str2num(SAMP);
17 | ref=REF;
18 | param.ThreshType=pTHRESH;
19 | param.percentile_or_value=pPERCVAL;
20 | param.LocalThresholdType=pTYPE;
21 | if strcmp(pTHRESH,'local')
22 | if strcmp(pTYPE,'minmaxmean')
23 | if strcmp(pPERCVAL,'percentile')
24 | param.percentile_low=str2num(pLOW);
25 | param.percentile_high=str2num(pHIGH);
26 | elseif strcmp(pPERCVAL,'value')
27 | param.value_low=str2num(pLOW);
28 | param.value_high=str2num(pHIGH);
29 | else
30 | error("cutoff type must be 'percentile' or 'value'")
31 | end
32 | end
33 | elseif strcmp(pTHRESH,'global')
34 | if strcmp(pPERCVAL,'percentile')
35 | param.percentile=str2num(pGLOBAL);
36 | elseif strcmp(pPERCVAL,'value')
37 | param.value=str2num(pGLOBAL);
38 | else
39 | error("cutoff type must be 'percentile' or 'value'")
40 | end
41 | else
42 | error("threshold type must be 'local' or 'global'")
43 | end
44 |
45 |
46 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
47 |
48 | save cellfieout score score_binary taskInfos detailScoring
49 | %saveas(figure(1),'histogram.png')
50 | %close(figure(1))
51 |
52 | csvwrite(strcat(outputdir,'/score.csv'),score);
53 | csvwrite(strcat(outputdir,'/score_binary.csv'),score_binary);
54 | T = cell2table(taskInfos);
55 | writetable(T,strcat(outputdir,'/taskInfo.csv'));
56 | Var={};
57 | for i=1:SampleNumber
58 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
59 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
60 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
61 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
62 | strcat('GeneExpressionValue_S',num2str(i))];
63 | end
64 | D = cell2table(detailScoring,'VariableNames',Var);
65 | writetable(D,strcat(outputdir,'/detailScoring.csv'));
66 |
67 | % ./matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh \
68 | % /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.mat 3 \
69 | % MT_recon_2_2_entrez.mat local value NA minmaxmean 25 75 outtmp
70 | % execCellfie('test/suite/dataTest.xlsx','3','MT_recon_2_2_entrez.mat','local','value','25','minmaxmean','25','75','outtmp')
71 | % execCellfie('test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','local','percentile','40','minmaxmean','15','85','outtmp')
72 | % execCellfie('test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','local','value','40','mean','15','85','outtmp')
73 | % execCellfie('test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','global','percentile','30','minmaxmean','15','85','outtmp')
74 |
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/genepattern/global_percentile.yaml:
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1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | #load('dataTest.mat')
8 | #SampleNumber=3;
9 | #ref='MT_recon_2_2_entrez.mat';
10 | #param.ThreshType='global';
11 | #param.percentile_or_value='percentile';
12 | #param.percentile=50;
13 |
14 | #csvwrite('dataRecon22_global_percentile.score.csv',score);
15 | #csvwrite('dataRecon22_global_percentile.score_binary.csv',score_binary);
16 | #writetable(T,'dataRecon22_global_percentile.taskInfo.csv');
17 |
18 | name: global_percentile
19 | module: CellFie
20 | params:
21 | "data": "../test/suite/dataTest.csv"
22 | "SampleNumber": 3
23 | "ref": "MT_recon_2_2_entrez.mat"
24 | "param.ThreshType": "global"
25 | "param.percentile_or_value": "percentile"
26 | "param.percentile_value_low": 50
27 |
28 | assertions:
29 | jobStatus: success
30 | files:
31 | "score.csv":
32 | diff: "../test/suite/dataRecon22_global_percentile.score.csv"
33 | "score_binary.csv":
34 | diff: "../test/suite/dataRecon22_global_percentile.score_binary.csv"
35 |
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/genepattern/global_value.yaml:
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1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | #load('dataTest.mat')
8 | #SampleNumber=3;
9 | #ref='MT_recon_2_2_entrez.mat';
10 | #param.ThreshType='global';
11 | #param.percentile_or_value='value';
12 | #param.value=50;
13 |
14 | #[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
15 | #save dataRecon22_global_value score score_binary taskInfos detailScoring
16 | #csvwrite('dataRecon22_global_value.score.csv',score);
17 | #csvwrite('dataRecon22_global_value.score_binary.csv',score_binary);
18 | #T = cell2table(taskInfos);
19 | #writetable(T,'dataRecon22_global_value.taskInfo.csv');
20 |
21 | name: global_value
22 | module: CellFie
23 | params:
24 | "data": "../test/suite/dataTest.csv"
25 | "SampleNumber": 3
26 | "ref": "MT_recon_2_2_entrez.mat"
27 | "param.ThreshType": "global"
28 | "param.percentile_or_value": "value"
29 | "param.percentile_value_low": 50
30 | job.memory: "8 Gb"
31 |
32 | assertions:
33 | jobStatus: success
34 | files:
35 | "score.csv":
36 | diff: "../test/suite/dataRecon22_global_value.score.csv"
37 | "score_binary.csv":
38 | diff: "../test/suite/dataRecon22_global_value.score_binary.csv"
39 |
--------------------------------------------------------------------------------
/genepattern/gpunit.yaml:
--------------------------------------------------------------------------------
1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | name: CellFie
8 | module: CellFie
9 | params:
10 | "data": "../test/suite/dataTest.csv"
11 | "SampleNumber": 3
12 | "ref": "MT_recon_2_2_entrez.mat"
13 |
14 | assertions:
15 | jobStatus: success
16 | files:
17 | "score.csv": !!null
18 | "cellfieout.mat": !!null
19 | "score_binary.csv": !!null
20 | "taskInfo.csv": !!null
21 | "stdout.txt": !!null
22 |
--------------------------------------------------------------------------------
/genepattern/local_mean.yaml:
--------------------------------------------------------------------------------
1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | #load('dataTest.mat')
8 | #SampleNumber=3;
9 | #ref='MT_recon_2_2_entrez.mat';
10 | #param.ThreshType='local';
11 | #param.LocalThresholdType='mean';
12 |
13 | #[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
14 | #save dataRecon22_local_mean score score_binary taskInfos detailScoring
15 | #csvwrite('dataRecon22_local_mean.score.csv',score);
16 | #csvwrite('dataRecon22_local_mean.score_binary.csv',score_binary);
17 | #T = cell2table(taskInfos);
18 | #writetable(T,'dataRecon22_local_mean.taskInfo.csv');
19 |
20 | name: local_mean
21 | module: CellFie
22 | params:
23 | "data": "../test/suite/dataTest.csv"
24 | "SampleNumber": 3
25 | "ref": "MT_recon_2_2_entrez.mat"
26 | "param.ThreshType": "local"
27 | "param.LocalThresholdType": "mean"
28 | job.memory: "8 Gb"
29 |
30 | assertions:
31 | jobStatus: success
32 | files:
33 | "score.csv":
34 | diff: "../test/suite/dataRecon22_local_mean.score.csv"
35 | "score_binary.csv":
36 | diff: "../test/suite/dataRecon22_local_mean.score_binary.csv"
37 |
--------------------------------------------------------------------------------
/genepattern/local_minmaxmean_percentile.yaml:
--------------------------------------------------------------------------------
1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | #load('dataTest.mat')
8 | #SampleNumber=3;
9 | #ref='MT_recon_2_2_entrez.mat';
10 | #param.ThreshType='local';
11 | #param.percentile_or_value='percentile';
12 | #param.LocalThresholdType='minmaxmean';
13 | #param.percentile_low=25;
14 | #param.percentile_high=75;
15 |
16 | #[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
17 | #save dataRecon22_local_minmaxmean_percentile score score_binary taskInfos detailScoring
18 | #csvwrite('dataRecon22_local_minmaxmean_percentile.score.csv',score);
19 | #csvwrite('dataRecon22_local_minmaxmean_percentile.score_binary.csv',score_binary);
20 | #T = cell2table(taskInfos);
21 | #writetable(T,'dataRecon22_local_minmaxmean_percentile.taskInfo.csv');
22 |
23 | name: local_minmaxmean_percentile
24 | module: CellFie
25 | params:
26 | "data": "../test/suite/dataTest.csv"
27 | "SampleNumber": 3
28 | "ref": "MT_recon_2_2_entrez.mat"
29 | "param.ThreshType": "local"
30 | "param.percentile_or_value": "percentile"
31 | "param.LocalThresholdType": "minmaxmean"
32 | "param.percentile_value_low": 25
33 | "param.percentile_value_high": 75
34 |
35 | assertions:
36 | jobStatus: success
37 | files:
38 | "score.csv":
39 | diff: "../test/suite/dataRecon22_local_minmaxmean_percentile.score.csv"
40 | "score_binary.csv":
41 | diff: "../test/suite/dataRecon22_local_minmaxmean_percentile.score_binary.csv"
42 |
--------------------------------------------------------------------------------
/genepattern/local_minmaxmean_value.yaml:
--------------------------------------------------------------------------------
1 | # Based on https://github.com/genepattern/STREAM.DetectTransitionGenes/blob/master/test/stream_detecttransitiongenes.yaml
2 |
3 | #
4 | # ant -f ${GPUNIT_HOME}/build.xml -Dgpunit.diffStripTrailingCR="--strip-trailing-cr" -Dgp.host="beta.genepattern.org" -Dgp.url="https://beta.genepattern.org" -Dgp.user="atwenzel2" -Dgp.password="" -Dgpunit.testfolder=`pwd` gpunit
5 | #
6 |
7 | #load('dataTest.mat')
8 | #SampleNumber=3;
9 | #ref='MT_recon_2_2_entrez.mat';
10 | #param.ThreshType='local';
11 | #param.percentile_or_value='value';
12 | #param.LocalThresholdType='minmaxmean';
13 | #param.value_low=25;
14 | #param.value_high=75;
15 |
16 | #[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
17 | #save dataRecon22_local_minmaxmean_value score score_binary taskInfos detailScoring
18 | #csvwrite('dataRecon22_local_minmaxmean_value.score.csv',score);
19 | #csvwrite('dataRecon22_local_minmaxmean_value.score_binary.csv',score_binary);
20 | #T = cell2table(taskInfos);
21 | #writetable(T,'dataRecon22_local_minmaxmean_value.taskInfo.csv');
22 |
23 | name: local_minmaxmean_value
24 | module: CellFie
25 | params:
26 | "data": "../test/suite/dataTest.csv"
27 | "SampleNumber": 3
28 | "ref": "MT_recon_2_2_entrez.mat"
29 | "param.ThreshType": "local"
30 | "param.percentile_or_value": "value"
31 | "param.LocalThresholdType": "minmaxmean"
32 | "param.percentile_value_low": 25
33 | "param.percentile_value_high": 75
34 |
35 | assertions:
36 | jobStatus: success
37 | files:
38 | "score.csv":
39 | diff: "../test/suite/dataRecon22_local_minmaxmean_value.score.csv"
40 | "score_binary.csv":
41 | diff: "../test/suite/dataRecon22_local_minmaxmean_value.score_binary.csv"
42 |
--------------------------------------------------------------------------------
/genepattern/outtmp/score.csv:
--------------------------------------------------------------------------------
1 | 1.9292,0.2188,1.7455
2 | 14.588,15.392,12.583
3 | 3.1564,4.8052,2.5293
4 | 7.9268,8.0834,7.0485
5 | 7.1914,7.51,7.189
6 | 12.883,13.731,13.692
7 | 5.2256,4.423,3.5845
8 | 4.2162,4.8506,4.0448
9 | 6.017,6.2811,6.6903
10 | 4.5492,4.4303,4.8479
11 | 6.1462,6.3968,6.7294
12 | 4.5862,4.4057,4.8334
13 | 5.3406,5.6554,6.0687
14 | 4.2999,4.1653,4.6177
15 | 5.9068,6.1794,6.5311
16 | 4.371,4.2487,4.6954
17 | 4.0952,3.9061,4.8207
18 | 6.5882,7.2214,7.5867
19 | 3.8951,5.557,5.3082
20 | 3.8951,5.557,5.3082
21 | 2.8509,4.4869,2.8784
22 | 1.8769,1.8856,2.3173
23 | 0.10462,1.4624,0.97617
24 | 6.4505,5.5063,6.7048
25 | 6.4505,5.5063,6.7048
26 | 10.674,10.611,10.545
27 | 10.902,10.841,10.764
28 | 8.6842,8.0524,7.705
29 | 10.399,10.114,10.047
30 | 8.1936,9.0347,7.4273
31 | 7.3102,4.7707,1.5054
32 | 11.67,12.437,12.344
33 | 8.9386,5.4695,6.0636
34 | 9.5685,7.8261,8.0304
35 | 6.9605,5.8251,6.8178
36 | 10.698,6.9422,8.3225
37 | 8.4999,7.6141,6.1638
38 | 6.3176,3.8984,4.2636
39 | 16.729,19.514,18.668
40 | 6.9969,6.9143,6.1284
41 | 10.317,7.8354,7.4263
42 | 6.3303,8.5976,6.8302
43 | 10.804,8.5024,8.2701
44 | 5.0927,6.2004,6.7366
45 | 6.1615,6.9849,5.9034
46 | 6.0376,6.2296,5.7791
47 | 10.538,8.8841,9.0001
48 | 7.7355,6.8219,6.6233
49 | 10.31,8.6176,8.6551
50 | 3.629,3.7933,2.8042
51 | 10.804,8.5024,8.2701
52 | 9.785,11.195,10.04
53 | 9.6052,10.902,9.896
54 | 4.7623,5.0341,3.6563
55 | 4.7269,5.5772,3.6359
56 | 14.036,14.135,11.452
57 | 4.0971,3.7116,4.7105
58 | 0.67058,0.44327,1.012
59 | 2.7169,2.9278,2.6754
60 | 8.0836,8.5836,7.2189
61 | 10.289,10.329,9.8422
62 | 6.5909,6.558,6.2197
63 | 5.6881,5.139,5.2241
64 | 7.795,8.0552,7.5058
65 | 6.5948,6.6297,6.531
66 | 7.0675,7.1749,6.6013
67 | 3.27,1.8698,2.948
68 | 8.0428,7.0569,6.7233
69 | 6.8023,8.7523,5.9155
70 | 9.7236,9.7102,9.3068
71 | 5.9461,6.0294,5.8358
72 | 9.8711,10.138,9.6575
73 | 6.6083,6.7962,6.3952
74 | 13.204,11.901,11.309
75 | 0.16014,1.0836,0.051989
76 | 6.6653,7.3622,9.9447
77 | 8.0961,8.3257,7.7802
78 | 6.3337,6.2978,6.0377
79 | 3.4814,2.9995,4.034
80 | 0.94934,3.7273,1.4979
81 | 4.5451,5.1485,4.6474
82 | 3.5967,3.3587,2.9536
83 | 11.385,6.6672,3.8786
84 | 10.51,10.75,10.24
85 | 5.9651,6.0122,5.8921
86 | 29.947,20.396,25.011
87 | 3.3175,2.5979,4.9637
88 | 3.3767,4.4693,3.1868
89 | 11.589,11.286,11.061
90 | 6.2266,6.481,6.1985
91 | 7.5895,8.324,6.9001
92 | 8.1487,9.7155,8.1748
93 | 10.754,11.427,10.526
94 | 3.5417,3.7928,3.6756
95 | 2.1407,2.5043,1.7436
96 | 6.226,6.3643,6.1371
97 | 0.67464,2.1184,1.1706
98 | 4.3151,1.6934,7.2033
99 | 6.295,6.5464,6.5616
100 | 1.0453,2.3525,0.81906
101 | 6.1774,6.1958,5.8849
102 | 6.4831,6.6408,6.133
103 | 5.7309,7.2759,4.5314
104 | 6.1034,6.067,5.7739
105 | 3.1046,2.6586,1.5409
106 | 2.1663,2.681,1.7066
107 | 5.2087,5.2132,5.0332
108 | 5.7678,7.0011,7.476
109 | 6.0505,6.1943,5.9823
110 | 7.7602,9.9385,8.5603
111 | 9.028,9.4909,10.476
112 | 11.397,12.289,11.219
113 | 3.7115,3.6473,3.5356
114 | 6.2643,6.3695,5.9965
115 | 0.89997,1.3746,0.42344
116 | 1.0489,1.125,0.54729
117 | 0.11071,1.826,0.0063698
118 | 9.8682,9.8319,9.7039
119 | 6.3544,6.4338,6.2826
120 | 6.3984,6.6847,7.8258
121 | 6.2359,6.2571,5.9842
122 | 5.9522,6.0248,5.7922
123 | 1.1448,0.32149,3.3967
124 | 2.3476,1.4609,2.9125
125 | 2.7748,1.7493,3.8465
126 | 1.2782,0.86482,3.4321
127 | 2.9911,2.2619,4.3839
128 | 0.18655,0.89091,0.50711
129 | 3.609,4.7403,1.8299
130 | 6.4202,6.4594,6.1467
131 | 1.4508,10.052,1.3445
132 | 0.065835,0.85025,0.19341
133 | 5.3724,5.2751,3.6765
134 | 6.0702,6.2311,5.8913
135 | 4.6946,5.6384,3.3233
136 | 10.124,11.541,10.42
137 | 4.2849,5.9043,4.0928
138 | 9.785,11.195,10.04
139 | 3.5673,6.4117,4.9972
140 | 4.0546,4.8494,4.2206
141 | 12.527,2.3026,1.0967
142 | 5.0283,4.7765,4.2615
143 | 4.9777,4.7342,4.1676
144 | 4.9588,4.7069,4.156
145 | 4.8684,4.5977,3.8401
146 | 4.9495,4.8165,4.06
147 | 4.8615,4.361,3.7296
148 | 4.984,4.8306,4.4564
149 | 4.7663,4.5818,4.0924
150 | 5.8508,6.2759,5.8278
151 | 2.0543,2.2747,1.7726
152 | 4.7957,5.1362,4.2664
153 | 2.1992,2.0852,1.5832
154 | 4.7771,5.218,4.4816
155 | 3.0509,3.455,2.6761
156 | 4.7771,5.218,4.4816
157 | 2.1411,2.4149,1.8531
158 | 2.228,2.4361,2.1027
159 | 3.3034,3.6629,2.8044
160 | 0.85116,1.361,0.88818
161 | 3.3526,3.6634,2.8259
162 | 6.1615,6.4208,6.233
163 | 2.1994,2.159,1.5809
164 | 8.9927,9.8046,8.7583
165 | 7.1432,7.4853,6.844
166 | 1.1418,1.1664,0.99696
167 | 2.6862,2.6859,2.7185
168 | 1.4441,1.2511,1.185
169 | 1.4441,1.2511,1.185
170 | 5.03,4.5181,6.7381
171 | 4.6117,4.3874,3.9211
172 | 3.4849,3.3713,2.6503
173 | 3.3702,3.2731,2.5968
174 | 10.932,8.6956,11.487
175 | 8.0951,9.2381,9.2406
176 | 3.2958,3.8485,3.4605
177 | 4.3755,6.1271,4.5156
178 | 1.9961,0.90326,1.9448
179 | 5.6369,4.1133,4.1513
180 | 15.682,12.044,12.914
181 | 6.6612,7.5934,6.2015
182 | 5.7534,5.9166,6.6948
183 | 1.9685,2.2515,2.2244
184 | 5.2044,5.3317,6.4975
185 | 3.8996,4.0564,4.8366
186 | 7.1174,6.3544,8.3787
187 | 4.0052,4.0915,6.2331
188 | 11.612,12.274,12.403
189 | 0.81884,1.1648,0.78307
190 | 0.81884,1.1648,0.78307
191 | 0.72173,0.81833,0.80956
192 | 0.57244,0.52871,0.54631
193 |
--------------------------------------------------------------------------------
/genepattern/outtmp/score_binary.csv:
--------------------------------------------------------------------------------
1 | 1,0,1
2 | 1,1,1
3 | 1,1,1
4 | 1,1,1
5 | 1,1,1
6 | 1,1,1
7 | 1,1,1
8 | 1,1,1
9 | 1,1,1
10 | 1,1,1
11 | 1,1,1
12 | 1,1,1
13 | 1,1,1
14 | 1,1,1
15 | 1,1,1
16 | 1,1,1
17 | 1,1,1
18 | 1,1,1
19 | 1,1,1
20 | 1,1,1
21 | 1,1,1
22 | 1,1,1
23 | 0,0,0
24 | 1,1,1
25 | 1,1,1
26 | 1,1,1
27 | 1,1,1
28 | 1,1,1
29 | 1,1,1
30 | 1,1,1
31 | 1,1,1
32 | 1,1,1
33 | 1,1,1
34 | 1,1,1
35 | 1,1,1
36 | 1,1,1
37 | 1,1,1
38 | 1,1,1
39 | 1,1,1
40 | 1,1,1
41 | 1,1,1
42 | 1,1,1
43 | 1,1,1
44 | 1,1,1
45 | 1,1,1
46 | 1,1,1
47 | 1,1,1
48 | 1,1,1
49 | 1,1,1
50 | 1,1,1
51 | 1,1,1
52 | 1,1,1
53 | 1,1,1
54 | 1,1,1
55 | 1,1,1
56 | 1,1,1
57 | 1,1,1
58 | 1,0,1
59 | 1,1,1
60 | 1,1,1
61 | 1,1,1
62 | 1,1,1
63 | 1,1,1
64 | 1,1,1
65 | 1,1,1
66 | 1,1,1
67 | 1,1,1
68 | 1,1,1
69 | 1,1,1
70 | 1,1,1
71 | 1,1,1
72 | 1,1,1
73 | 1,1,1
74 | 1,1,1
75 | 0,0,0
76 | 1,1,1
77 | 1,1,1
78 | 1,1,1
79 | 1,1,1
80 | 0,1,1
81 | 1,1,1
82 | 1,1,1
83 | 1,1,1
84 | 1,1,1
85 | 1,1,1
86 | 1,1,1
87 | 1,1,1
88 | 1,1,1
89 | 1,1,1
90 | 1,1,1
91 | 1,1,1
92 | 1,1,1
93 | 1,1,1
94 | 1,1,1
95 | 1,1,1
96 | 1,1,1
97 | 0,0,0
98 | 1,1,1
99 | 1,1,1
100 | 0,1,0
101 | 1,1,1
102 | 1,1,1
103 | 1,1,1
104 | 1,1,1
105 | 1,1,1
106 | 1,1,1
107 | 1,1,1
108 | 1,1,1
109 | 1,1,1
110 | 1,1,1
111 | 1,1,1
112 | 1,1,1
113 | 1,1,1
114 | 1,1,1
115 | 1,1,1
116 | 1,1,1
117 | 0,1,0
118 | 1,1,1
119 | 1,1,1
120 | 1,1,1
121 | 1,1,1
122 | 1,1,1
123 | 1,0,1
124 | 1,1,1
125 | 1,1,1
126 | 1,0,1
127 | 1,1,1
128 | 0,1,0
129 | 1,1,1
130 | 1,1,1
131 | 0,1,0
132 | 0,1,0
133 | 1,1,1
134 | 1,1,1
135 | 1,1,1
136 | 1,1,1
137 | 1,1,1
138 | 1,1,1
139 | 1,1,1
140 | 1,1,1
141 | 1,1,0
142 | 1,1,1
143 | 1,1,1
144 | 1,1,1
145 | 1,1,1
146 | 1,1,1
147 | 1,1,1
148 | 1,1,1
149 | 1,1,1
150 | 1,1,1
151 | 1,1,1
152 | 1,1,1
153 | 1,1,1
154 | 1,1,1
155 | 1,1,1
156 | 1,1,1
157 | 1,1,1
158 | 1,1,1
159 | 1,1,1
160 | 1,1,1
161 | 1,1,1
162 | 1,1,1
163 | 1,1,1
164 | 1,1,1
165 | 1,1,1
166 | 1,1,1
167 | 1,1,1
168 | 1,1,1
169 | 1,1,1
170 | 1,1,1
171 | 1,1,1
172 | 1,1,1
173 | 1,1,1
174 | 1,1,1
175 | 1,1,1
176 | 1,1,1
177 | 1,1,1
178 | 1,1,1
179 | 1,1,1
180 | 1,1,1
181 | 1,1,1
182 | 1,1,1
183 | 1,1,1
184 | 1,1,1
185 | 1,1,1
186 | 1,1,1
187 | 1,1,1
188 | 1,1,1
189 | 1,1,1
190 | 1,1,1
191 | 1,1,1
192 | 1,1,1
193 |
--------------------------------------------------------------------------------
/genepattern/run_cellfie_in_docker.sh:
--------------------------------------------------------------------------------
1 | docker run --rm -v $PWD:/IO atwenzel/cellfie:1.3 /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.mat 3 MT_recon_2_2_entrez.mat local value minmaxmean 25 75
2 |
--------------------------------------------------------------------------------
/genepattern/testexec.m:
--------------------------------------------------------------------------------
1 |
2 | % ./matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.mat 3 MT_recon_2_2_entrez.mat local value NA minmaxmean 25 75 outtmp
3 | % ./matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.csv 3 MT_recon_2_2_entrez.mat local value NA minmaxmean 25 75 outtmp
4 | % ./matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.xlsx 3 MT_recon_2_2_entrez.mat local value NA minmaxmean 25 75 outtmp
5 | % ./matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh /usr/local/MATLAB/MATLAB_Runtime/v94 test/suite/dataTest.mat 3 MT_recon_2_2_entrez.mat local value NA minmaxmean 25 75 outtmp
6 |
7 | execCellfie('../test/suite/dataTest.xlsx','3','MT_recon_2_2_entrez.mat','local','value','25','minmaxmean','25','75','outtmp')
8 | execCellfie('../test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','local','percentile','40','minmaxmean','15','85','outtmp')
9 | execCellfie('../test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','local','value','40','mean','15','85','outtmp')
10 | execCellfie('../test/suite/dataTest.csv','3','MT_recon_2_2_entrez.mat','global','percentile','30','minmaxmean','15','85','outtmp')
11 |
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/initCellFie.m:
--------------------------------------------------------------------------------
1 | % % set the cellfie directory
2 | % CELLFIEDIR = fileparts(mfilename('fullpath'));
3 | %
4 | % % initialize the COBRA Toolbox
5 | % if exist('CBTDIR', 'var')
6 | % cd(CBTDIR);
7 | % initCobraToolbox
8 | % else
9 | % error('Please initialize the COBRA Toolbox or set the main directory of the COBRA Toolbox CBTDIR');
10 | % end
11 | %
12 | % % change to the cellfie main directory
13 | % cd(CELLFIEDIR);
14 | %
15 | % % add the entire cellfie repository to the MATLAB path
16 | % addpath(genpath(CELLFIEDIR));
17 |
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/input/inactive_reactions.csv:
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1 | index,v1,v2,v3
2 | 7,ATP generation from ions,ENERGY METABOLISM,ATP GENERATION
3 | 82,Cysteine synthesis,AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
4 | 99,Histidine synthesis,AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
5 | 103,Homocysteine synthesis,AMINO ACIDS METABOLISM,HOMOCYSTEINE METABOLISM
6 | 106,Isoleucine synthesis,AMINO ACIDS METABOLISM,ISOLEUCINE METABOLISM
7 | 108,Leucine synthesis,AMINO ACIDS METABOLISM,LEUCINE METABOLISM
8 | 111,Lysine synthesis,AMINO ACIDS METABOLISM,LYSINE METABOLISM
9 | 115,Methionine synthesis,AMINO ACIDS METABOLISM,METHIONINE METABOLISM
10 | 123,Phenylalanine synthesis,AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
11 | 131,Threonine synthesis,AMINO ACIDS METABOLISM,THREONINE METABOLISM
12 | 133,Tryptophan synthesis,AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
13 | 141,Tyrosine synthesis,AMINO ACIDS METABOLISM,TYROSINE METABOLISM
14 | 147,Valine synthesis,AMINO ACIDS METABOLISM,VALINE METABOLISM
15 | 172,Linolenate synthesis,LIPIDS METABOLISM,FATTY ACID METABOLISM
16 | 174,Linoleate synthesis,LIPIDS METABOLISM,FATTY ACID METABOLISM
17 |
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/input/parsedGPR/test:
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2 |
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/matlab_compiled/execCellfie.prj:
--------------------------------------------------------------------------------
1 |
2 |
3 | execCellfie
4 |
5 |
6 | 0.7
7 | Richelle, Kellman
8 | bkellman@eng.ucsd.edu
9 |
10 | An alternative approach to capture the breadth of cellular functions by performing a functional analysis of existing biological networks.
11 | CellFie: Cellular Functions InferencE
12 |
13 |
14 | An alternative approach for the interpretation of omics data (e.g., differentially expressed genes) that captures the simplicity of enrichment analyses, while providing deeper mechanistic insights into how differential expression impacts specific cellular functions. This approach enables the investigation of hundreds of metabolic tasks curated from literature covering 7 major metabolic activities of a cell (energy generation, nucleotide, carbohydrate, amino acid, lipid, vitamin & cofactor and glycan metabolism) and 4 mammalian organisms (human, rat, mouse and CHO cells). This platform can be used to predict the activity of these metabolic functions from transcriptomic data to comprehensively quantify the propensity of a cell line or tissue to express a metabolic function.
15 |
16 | We welcome any comments, bug reports, and feature requests. Please send all feedback to arichelleres@gmail.com
17 |
18 | For issues with the GenePattern module, please contact atwenzel@eng.ucsd.edu
19 |
20 | Updates
21 | 8/04/20 - remove cobratoolbox dependency
22 | 3/26/2020 - added detailScores.csv output
23 | 4/09/2020 - fixed percentile setting in line 23-25
24 | 4/09/2020 - added explicit "global" parameter
25 | 4/19/2020 - fixed execCellfie parameter bug
26 |
27 |
28 | /execCellfie/
29 | option.installpath.systemwideapp
30 |
31 |
32 | In the following directions, replace MR/v95 by the directory on the target machine where MATLAB is installed, or MR by the directory where the MATLAB Runtime is installed.
33 |
34 | If the environment variable DYLD_LIBRARY_PATH is undefined, set it to the following string:
35 |
36 | MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
37 |
38 | If it is defined, set it to the following:
39 |
40 | ${DYLD_LIBRARY_PATH}:MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
41 | ${PROJECT_ROOT}/execCellfie/for_testing
42 | ${PROJECT_ROOT}/execCellfie/for_redistribution_files_only
43 | ${PROJECT_ROOT}/execCellfie/for_redistribution
44 | ${PROJECT_ROOT}/execCellfie
45 | false
46 |
47 | subtarget.standalone
48 |
49 | true
50 | false
51 | false
52 | MyAppInstaller_web
53 | MyAppInstaller_mcr
54 | MyAppInstaller_app
55 | false
56 | false
57 |
58 | false
59 | false
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
69 |
70 |
71 |
72 |
73 |
74 |
75 |
76 |
77 |
78 |
79 |
80 |
81 |
82 |
83 |
84 |
85 |
86 |
87 |
88 |
89 |
90 |
91 |
92 |
93 |
94 |
95 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/genepattern/execCellfie.m
96 |
97 |
98 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/input
99 |
100 |
101 |
102 | /Users/helenmasson/Dropbox/LewisLab/dev_CellFie/NewCellFie_May29/src/CellFie.m
103 | /Users/helenmasson/Dropbox/LewisLab/dev_CellFie/NewCellFie_May29/src/prctile.m
104 | /Users/helenmasson/Dropbox/LewisLab/dev_CellFie/NewCellFie_May29/src/tabulate.m
105 | /Users/helenmasson/cobratoolbox/src/analysis/exploration/findRxnIDs.m
106 | /Users/helenmasson/cobratoolbox/src/dataIntegration/transcriptomics/preprocessing/GPRparser.m
107 | /Users/helenmasson/cobratoolbox/src/dataIntegration/transcriptomics/preprocessing/findUsedGenesLevels.m
108 | /Users/helenmasson/cobratoolbox/src/dataIntegration/transcriptomics/preprocessing/mapExpressionToReactions.m
109 | /Users/helenmasson/cobratoolbox/src/dataIntegration/transcriptomics/preprocessing/selectGeneFromGPR.m
110 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/AndNode.m
111 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/FormulaParser.m
112 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/LiteralNode.m
113 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/Node.m
114 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/OrNode.m
115 | /Users/helenmasson/cobratoolbox/src/reconstruction/refinement/GPRLogic/combineChildren.m
116 |
117 |
118 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/run_execCellfie.sh
119 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/readme.txt
120 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/splash.png
121 | /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/execCellfie.app
122 |
123 |
124 |
125 | /Applications/MATLAB_R2018b.app
126 |
127 |
128 |
129 |
130 |
131 |
132 |
133 |
134 | true
135 |
136 |
137 |
138 |
139 | true
140 |
141 |
142 |
143 |
144 | true
145 |
146 |
147 |
148 |
149 | true
150 |
151 |
152 |
153 |
154 | true
155 | true
156 | false
157 | false
158 | false
159 | false
160 | false
161 | false
162 | 10.15.7
163 | false
164 | true
165 | maci64
166 | true
167 |
168 |
169 |
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/matlab_compiled/execCellfie/PackagingLog.html:
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1 |
2 | mcc -o execCellfie -W main:execCellfie -T link:exe -d /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing -v /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/genepattern/execCellfie.m -a /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/input
3 | Compiler version: 7.0 (R2018b)
4 |
5 | Dependency analysis by REQUIREMENTS.
6 |
7 | Parsing file "/Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/genepattern/execCellfie.m"
8 | (Referenced from: "Compiler Command Line").
9 | Deleting 0 temporary MEX authorization files.
10 | Generating file "/Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/readme.txt".
11 | Generating file "/Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/run_execCellfie.sh".
12 | Packaging...
13 | Adding custom icon /Applications/MATLAB_R2018b.app/toolbox/compiler/Resources/default_icon.icns to /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_testing/execCellfie.app.
14 | Creating the bundle...
15 | Copying /Applications/MATLAB_R2018b.app/toolbox/compiler/maci64/setup.app to /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/MyAppInstaller_web.app.
16 | Copying /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/bundle.zip to /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/MyAppInstaller_web.app/Contents/Resources/bundle.zip.
17 | Copying /Applications/MATLAB_R2018b.app/toolbox/compiler/Resources/default_splash.png to /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/MyAppInstaller_web.app/Contents/Resources/splash.png.
18 | Copying /Applications/MATLAB_R2018b.app/toolbox/compiler/Resources/default_icon.icns to /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/MyAppInstaller_web.app/Contents/Resources/installer.icns.
19 | Web based installer created at /Users/helenmasson/Desktop/SenateExam/Aim2_CellFie/Final_compiledCellFIE/CellFie/matlab_compiled/execCellfie/for_redistribution/MyAppInstaller_web.app.
20 | Packaging complete.
21 | Elapsed packaging time was: 1 minutes and 4 seconds.
22 |
23 |
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12 | com.mathworks.toolbox.compiler.setup
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25 | NSMainNibFile
26 | MainMenu
27 | NSPrincipalClass
28 | NSApplication
29 | NSAppTransportSecurity
30 |
31 | NSExceptionDomains
32 |
33 | mathworks.com
34 |
35 | NSIncludesSubdomains
36 |
37 | NSThirdPartyExceptionRequiresForwardSecrecy
38 |
39 |
40 |
41 |
42 |
43 |
44 |
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1 |
2 |
3 |
4 |
5 | CFBundleDevelopmentRegion
6 | English
7 | CFBundleExecutable
8 | prelaunch
9 | CFBundleIconFile
10 | membrane.icns
11 | CFBundleIdentifier
12 | execCellfie
13 | CFBundleInfoDictionaryVersion
14 | 6.0
15 | CFBundleName
16 | execCellfie
17 | CFBundlePackageType
18 | APPL
19 | CFBundleSignature
20 | execCellfie
21 | CFBundleVersion
22 | 1
23 | CFBundleVersionString
24 | 1.0
25 | CFResourcesFileMapped
26 |
27 | LSMinimumSystemVersion
28 | 10.9.0
29 | NSMainNibFile
30 | MainMenu
31 | NSPrincipalClass
32 | NSApplication
33 |
34 |
35 |
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/matlab_compiled/execCellfie/for_redistribution_files_only/readme.txt:
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1 | execCellfie Executable
2 |
3 | 1. Prerequisites for Deployment
4 |
5 | Verify that version 9.5 (R2018b) of the MATLAB Runtime is installed.
6 | If not, you can run the MATLAB Runtime installer.
7 | To find its location, enter
8 |
9 | >>mcrinstaller
10 |
11 | at the MATLAB prompt.
12 | NOTE: You will need administrator rights to run the MATLAB Runtime installer.
13 |
14 | Alternatively, download and install the Macintosh version of the MATLAB Runtime for R2018b
15 | from the following link on the MathWorks website:
16 |
17 | http://www.mathworks.com/products/compiler/mcr/index.html
18 |
19 | For more information about the MATLAB Runtime and the MATLAB Runtime installer, see
20 | "Distribute Applications" in the MATLAB Compiler documentation
21 | in the MathWorks Documentation Center.
22 |
23 | 2. Files to Deploy and Package
24 |
25 | Files to Package for Standalone
26 | ================================
27 | -run_execCellfie.sh (shell script for temporarily setting environment variables and
28 | executing the application)
29 | -to run the shell script, type
30 |
31 | ./run_execCellfie.sh
32 |
33 | at Linux or Mac command prompt. is the directory
34 | where version 9.5 of the MATLAB Runtime is installed or the directory where
35 | MATLAB is installed on the machine. is all the
36 | arguments you want to pass to your application. For example,
37 |
38 | If you have version 9.5 of the MATLAB Runtime installed in
39 | /mathworks/home/application/v95, run the shell script as:
40 |
41 | ./run_execCellfie.sh /mathworks/home/application/v95
42 |
43 | If you have MATLAB installed in /mathworks/devel/application/matlab,
44 | run the shell script as:
45 |
46 | ./run_execCellfie.sh /mathworks/devel/application/matlab
47 | -MCRInstaller.zip
48 | Note: if end users are unable to download the MATLAB Runtime using the
49 | instructions in the previous section, include it when building your
50 | component by clicking the "Runtime included in package" link in the
51 | Deployment Tool.
52 | -The Macintosh bundle directory structure execCellfie.app
53 | Note: this can be stored in an archive file with the zip command
54 | zip -r execCellfie.zip execCellfie.app
55 | or the tar command
56 | tar -cvf execCellfie.tar execCellfie.app
57 | -This readme file
58 |
59 |
60 |
61 | 3. Definitions
62 |
63 | For information on deployment terminology, go to
64 | http://www.mathworks.com/help and select MATLAB Compiler >
65 | Getting Started > About Application Deployment >
66 | Deployment Product Terms in the MathWorks Documentation
67 | Center.
68 |
69 | 4. Appendix
70 |
71 | A. Mac systems:
72 | In the following directions, replace MR/v95 by the directory on the target machine where
73 | MATLAB is installed, or MR by the directory where the MATLAB Runtime is installed.
74 |
75 | If the environment variable DYLD_LIBRARY_PATH is undefined, set it to the following
76 | string:
77 |
78 | MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
79 |
80 | If it is defined, set it to the following:
81 |
82 | ${DYLD_LIBRARY_PATH}:MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
83 |
84 | For more detailed information about setting the MATLAB Runtime paths, see Package and
85 | Distribute in the MATLAB Compiler documentation in the MathWorks Documentation Center.
86 |
87 |
88 |
89 | NOTE: To make these changes persistent after logout on Linux
90 | or Mac machines, modify the .cshrc file to include this
91 | setenv command.
92 | NOTE: The environment variable syntax utilizes forward
93 | slashes (/), delimited by colons (:).
94 | NOTE: When deploying standalone applications, you can
95 | run the shell script file run_execCellfie.sh
96 | instead of setting environment variables. See
97 | section 2 "Files to Deploy and Package".
98 |
99 |
100 |
101 | 5. Launching application using Macintosh finder
102 |
103 | If the application is purely graphical, that is, it doesn't read from standard in or
104 | write to standard out or standard error, it may be launched in the finder just like any
105 | other Macintosh application.
106 |
107 |
108 |
109 |
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/matlab_compiled/execCellfie/for_redistribution_files_only/run_execCellfie.sh:
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1 | #!/bin/sh
2 | # script for execution of deployed applications
3 | #
4 | # Sets up the MATLAB Runtime environment for the current $ARCH and executes
5 | # the specified command.
6 | #
7 | exe_name=$0
8 | exe_dir=`dirname "$0"`
9 | echo "------------------------------------------"
10 | if [ "x$1" = "x" ]; then
11 | echo Usage:
12 | echo $0 \ args
13 | else
14 | echo Setting up environment variables
15 | MCRROOT="$1"
16 | echo ---
17 | DYLD_LIBRARY_PATH=.:${MCRROOT}/runtime/maci64 ;
18 | DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:${MCRROOT}/bin/maci64 ;
19 | DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:${MCRROOT}/sys/os/maci64;
20 | export DYLD_LIBRARY_PATH;
21 | echo DYLD_LIBRARY_PATH is ${DYLD_LIBRARY_PATH};
22 | shift 1
23 | args=
24 | while [ $# -gt 0 ]; do
25 | token=$1
26 | args="${args} \"${token}\""
27 | shift
28 | done
29 | eval "\"${exe_dir}/execCellfie.app/Contents/MacOS/execCellfie\"" $args
30 | fi
31 | exit
32 |
33 |
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/matlab_compiled/execCellfie/for_testing/execCellfie.app/Contents/Info.plist:
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1 |
2 |
3 |
4 |
5 | CFBundleDevelopmentRegion
6 | English
7 | CFBundleExecutable
8 | prelaunch
9 | CFBundleIconFile
10 | membrane.icns
11 | CFBundleIdentifier
12 | execCellfie
13 | CFBundleInfoDictionaryVersion
14 | 6.0
15 | CFBundleName
16 | execCellfie
17 | CFBundlePackageType
18 | APPL
19 | CFBundleSignature
20 | execCellfie
21 | CFBundleVersion
22 | 1
23 | CFBundleVersionString
24 | 1.0
25 | CFResourcesFileMapped
26 |
27 | LSMinimumSystemVersion
28 | 10.9.0
29 | NSMainNibFile
30 | MainMenu
31 | NSPrincipalClass
32 | NSApplication
33 |
34 |
35 |
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/matlab_compiled/execCellfie/for_testing/mccExcludedFiles.log:
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1 | The List of Excluded Files
2 | Excluded files Exclusion Message ID Reason For Exclusion Exclusion Rule
3 |
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/matlab_compiled/execCellfie/for_testing/readme.txt:
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1 | execCellfie Executable
2 |
3 | 1. Prerequisites for Deployment
4 |
5 | Verify that version 9.5 (R2018b) of the MATLAB Runtime is installed.
6 | If not, you can run the MATLAB Runtime installer.
7 | To find its location, enter
8 |
9 | >>mcrinstaller
10 |
11 | at the MATLAB prompt.
12 | NOTE: You will need administrator rights to run the MATLAB Runtime installer.
13 |
14 | Alternatively, download and install the Macintosh version of the MATLAB Runtime for R2018b
15 | from the following link on the MathWorks website:
16 |
17 | http://www.mathworks.com/products/compiler/mcr/index.html
18 |
19 | For more information about the MATLAB Runtime and the MATLAB Runtime installer, see
20 | "Distribute Applications" in the MATLAB Compiler documentation
21 | in the MathWorks Documentation Center.
22 |
23 | 2. Files to Deploy and Package
24 |
25 | Files to Package for Standalone
26 | ================================
27 | -run_execCellfie.sh (shell script for temporarily setting environment variables and
28 | executing the application)
29 | -to run the shell script, type
30 |
31 | ./run_execCellfie.sh
32 |
33 | at Linux or Mac command prompt. is the directory
34 | where version 9.5 of the MATLAB Runtime is installed or the directory where
35 | MATLAB is installed on the machine. is all the
36 | arguments you want to pass to your application. For example,
37 |
38 | If you have version 9.5 of the MATLAB Runtime installed in
39 | /mathworks/home/application/v95, run the shell script as:
40 |
41 | ./run_execCellfie.sh /mathworks/home/application/v95
42 |
43 | If you have MATLAB installed in /mathworks/devel/application/matlab,
44 | run the shell script as:
45 |
46 | ./run_execCellfie.sh /mathworks/devel/application/matlab
47 | -MCRInstaller.zip
48 | Note: if end users are unable to download the MATLAB Runtime using the
49 | instructions in the previous section, include it when building your
50 | component by clicking the "Runtime included in package" link in the
51 | Deployment Tool.
52 | -The Macintosh bundle directory structure execCellfie.app
53 | Note: this can be stored in an archive file with the zip command
54 | zip -r execCellfie.zip execCellfie.app
55 | or the tar command
56 | tar -cvf execCellfie.tar execCellfie.app
57 | -This readme file
58 |
59 |
60 |
61 | 3. Definitions
62 |
63 | For information on deployment terminology, go to
64 | http://www.mathworks.com/help and select MATLAB Compiler >
65 | Getting Started > About Application Deployment >
66 | Deployment Product Terms in the MathWorks Documentation
67 | Center.
68 |
69 | 4. Appendix
70 |
71 | A. Mac systems:
72 | In the following directions, replace MR/v95 by the directory on the target machine where
73 | MATLAB is installed, or MR by the directory where the MATLAB Runtime is installed.
74 |
75 | If the environment variable DYLD_LIBRARY_PATH is undefined, set it to the following
76 | string:
77 |
78 | MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
79 |
80 | If it is defined, set it to the following:
81 |
82 | ${DYLD_LIBRARY_PATH}:MR/v95/runtime/maci64:MR/v95/sys/os/maci64:MR/v95/bin/maci64
83 |
84 | For more detailed information about setting the MATLAB Runtime paths, see Package and
85 | Distribute in the MATLAB Compiler documentation in the MathWorks Documentation Center.
86 |
87 |
88 |
89 | NOTE: To make these changes persistent after logout on Linux
90 | or Mac machines, modify the .cshrc file to include this
91 | setenv command.
92 | NOTE: The environment variable syntax utilizes forward
93 | slashes (/), delimited by colons (:).
94 | NOTE: When deploying standalone applications, you can
95 | run the shell script file run_execCellfie.sh
96 | instead of setting environment variables. See
97 | section 2 "Files to Deploy and Package".
98 |
99 |
100 |
101 | 5. Launching application using Macintosh finder
102 |
103 | If the application is purely graphical, that is, it doesn't read from standard in or
104 | write to standard out or standard error, it may be launched in the finder just like any
105 | other Macintosh application.
106 |
107 |
108 |
109 |
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/matlab_compiled/execCellfie/for_testing/requiredMCRProducts.txt:
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1 | 35000 35010
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/matlab_compiled/execCellfie/for_testing/run_execCellfie.sh:
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1 | #!/bin/sh
2 | # script for execution of deployed applications
3 | #
4 | # Sets up the MATLAB Runtime environment for the current $ARCH and executes
5 | # the specified command.
6 | #
7 | exe_name=$0
8 | exe_dir=`dirname "$0"`
9 | echo "------------------------------------------"
10 | if [ "x$1" = "x" ]; then
11 | echo Usage:
12 | echo $0 \ args
13 | else
14 | echo Setting up environment variables
15 | MCRROOT="$1"
16 | echo ---
17 | DYLD_LIBRARY_PATH=.:${MCRROOT}/runtime/maci64 ;
18 | DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:${MCRROOT}/bin/maci64 ;
19 | DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:${MCRROOT}/sys/os/maci64;
20 | export DYLD_LIBRARY_PATH;
21 | echo DYLD_LIBRARY_PATH is ${DYLD_LIBRARY_PATH};
22 | shift 1
23 | args=
24 | while [ $# -gt 0 ]; do
25 | token=$1
26 | args="${args} \"${token}\""
27 | shift
28 | done
29 | eval "\"${exe_dir}/execCellfie.app/Contents/MacOS/execCellfie\"" $args
30 | fi
31 | exit
32 |
33 |
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/src/CellFie.m:
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1 | function[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param)
2 | % Compute the score associated to each metabolic task listed in taskstructure based on transcriptomic data
3 | %
4 | % USAGE:
5 | % [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param)
6 | %
7 | % INPUTS:
8 | % data
9 | % .gene cell array containing GeneIDs in the same
10 | % format as model.genes
11 | % .value mRNA expression data structure (genes x samples)associated to each gene metioned in data.gene
12 | % SampleNumber Number of samples
13 | % ref Reference model used to compute the
14 | % metabolic task scores (e.g.,'MT_recon_2_2_entrez.mat')
15 | % OPTIONAL INPUTS:
16 | % param.ThreshType Type of thresholding approach used
17 | % (i.e.,'global' or 'local') (default - local)
18 | % related to the use of a GLOBAL thresholding approach - the threshold value is the same for all the genes
19 | % param.percentile_or_value the threshold can be defined using a value introduced by the user ('value')
20 | % or based on a percentile of the distribution of expression value for all the
21 | % genes and across all samples of your
22 | % dataset ('percentile')
23 | % param.percentile percentile from the distribution of
24 | % expression values for all the genes and across all samples that will be
25 | % used to define the threshold value
26 | % param.value expression value for which a gene is
27 | % considered as active or not (e.g., 5)
28 | %
29 | % related to the use of a LOCAL thresholding approach - the threshold value is different for all the genes
30 | % param.percentile_or_value the threshold can be defined using a value introduced by the user ('value')
31 | % or based on a percentile of the distribution of expression value of a
32 | % specific gene across all samples of your
33 | % dataset ('percentile'-default)
34 | % param.LocalThresholdType option to define the type of local thresholding approach to use
35 | % - 'minmaxmean' (default options )- the threshold for a gene is determined by the mean of expression
36 | % values observed for that gene among all the samples, tissues, or conditions BUT
37 | % the threshold :(i) must be higher or equal to a lower bound and (ii) must be lower
38 | % or equal to an upper bound.
39 | % - 'mean' -the threshold for a gene is defined as the mean expression value
40 | % of this gene across all the samples, tissues, or conditions
41 | % param.percentile_low lower percentile used to define which gene
42 | % are always inactive in the case of use 'MinMaxMean' local thresholding
43 | % approach (default = 25)
44 | % param.percentile_high upper percentile used to define which gene
45 | % are always active in the case of use 'MinMaxMean' local thresholding
46 | % approach (default= 75)
47 | % param.value_low lower expression value used to define which gene
48 | % are always inactive in the case of use 'MinMaxMean' local thresholding
49 | % approach (e.g., 5)
50 | % param.value_high upper expression value used to define which gene
51 | % are always active in the case of use 'MinMaxMean' local thresholding
52 | % approach (e.g., 5)
53 | %
54 | % related to the gene mapping approach used
55 | % param.minSum: instead of using min and max, use min for AND and Sum
56 | % for OR (default: false, i.e. use min)
57 | % OUTPUTS:
58 | % score relative quantification of the activity of a metabolic task in a specific condition
59 | % based on the availability of data for multiple conditions
60 | % score_binary binary version of the metabolic task score
61 | % to determine whether a task is active or inactive in specific
62 | % conditions
63 | % taskInfos Description of the metabolic task assessed
64 | % detailScoring Matrix detailing the scoring
65 | % 1st column = sample ID
66 | % 2nd column = task ID
67 | % 3th column = task score for this sample
68 | % 4th column = task score in binary version for this sample
69 | % 5th column = essential reaction associated to this task
70 | % 6th column = expression score associated to the reaction listed in the 5th column
71 | % 7th column = gene used to determine the expression of the reaction listed in the 5th column
72 | % 8th column = original expression value of the gene listed in the 7th column
73 | %
74 | % .. Authors:
75 | % - Anne Richelle, January 2019
76 | if size(data.value,2) ~= SampleNumber
77 | error('The number of samples defined is not the same as the size of the dataset')
78 | end
79 | if size(data.value,1) ~= length(data.gene)
80 | error('data.value does not have the same number of rows as data.gene')
81 | end
82 | if ~exist('ref','var')
83 | error('The reference model has not been defined - please choose a reference model')
84 | end
85 | if ~exist('param','var')
86 | param.ThreshType='local';
87 | param.percentile_or_value='percentile';
88 | param.LocalThresholdType='minmaxmean';
89 | param.percentile_low=25;
90 | param.percentile_high=75;
91 | end
92 |
93 | % f = waitbar(0,'Please wait...');
94 | % waitbar(.15,f,'Loading your data');
95 |
96 | %load the info about the task structure
97 | load('taskStructure')
98 | taskInfos=struct2cell(taskStructure);
99 | taskInfos=taskInfos';
100 | taskInfos(:,5:end)=[];
101 |
102 |
103 | %load the reference model in matlab
104 | load(ref);
105 |
106 | %% Depending on the model, load the list of reactions associated with the task
107 | % All these files have the following format
108 | % essentialRxnsbyTask_name_of_model and are located in essentialRxns folder
109 | load(strcat('essentialRxns/essentialRxnsbyTask_',ref));
110 |
111 | % check that at least part of list of gene provided are in the model loaded
112 | ID_model=[];
113 | gene_notInModel=[];
114 | for i=1:length(data.gene)
115 | if isempty(find(strcmp(data.gene{i},model.genes)))
116 | gene_notInModel(end+1)=i;
117 | else
118 | tmpid=find(strcmp(data.gene{i},model.genes)==1);
119 | ID_model(end+1)=tmpid(1);
120 | end
121 | end
122 |
123 | % introduce a warning in the webtool about how many of the genes provided are
124 | % actually mapped to the model
125 | if isempty(gene_notInModel)
126 | display('All genes provided in data are included in the reference model')
127 | else
128 | display([num2str(length(gene_notInModel)),' genes provided are not included in the reference model:'])
129 | data.gene(gene_notInModel)
130 | end
131 |
132 | % remove the gene and associated value provided by the user in data that are not in the model
133 | data.gene(gene_notInModel)=[];
134 | if SampleNumber==1
135 | data.value(gene_notInModel)=[];
136 | else
137 | data.value(gene_notInModel,:)=[];
138 | end
139 |
140 | % get the threshold value and the histogram for the complete dataset and
141 | % print a figure
142 | if SampleNumber>1
143 | linData = reshape(data.value,numel(data.value),1);
144 | else
145 | linData=data.value;
146 | end
147 | linData(linData==0)=[];
148 |
149 | % definition of the thresholds
150 | if strcmp(param.ThreshType,'global') && strcmp(param.percentile_or_value,'percentile')
151 | display('RUN - global: percentile')
152 | l_global = (prctile(log10(linData),param.percentile));
153 | data.ths=10^l_global;
154 | elseif strcmp(param.ThreshType,'global') && strcmp(param.percentile_or_value,'value')
155 | display('RUN - global: value')
156 | data.ths=param.value;
157 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'mean')
158 | display('RUN - local: mean')
159 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'minmaxmean')&& strcmp(param.percentile_or_value,'percentile')
160 | display('RUN - local: minmaxmean: percentile')
161 | l_high = (prctile(log10(linData),param.percentile_high));
162 | data.ths_high=10^l_high;
163 | l_low = (prctile(log10(linData),param.percentile_low));
164 | data.ths_low=10^l_low;
165 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'minmaxmean')&& strcmp(param.percentile_or_value,'value')
166 | display('RUN - local: minmaxmean: value')
167 | data.ths_high=param.value_high;
168 | data.ths_low=param.value_low;
169 | else
170 | error('No analysis triggered')
171 | end
172 |
173 | %% Compute the threshold(s) depending on the approach used
174 | Gene_score=[];
175 | switch param.ThreshType
176 | case 'local'
177 | if strcmp(param.LocalThresholdType,'mean')
178 | %the threshold for each gene is equal to its mean value over
179 | %all the samples
180 | threshold=mean(data.value,2)';
181 | else
182 | threshold=[];
183 | for i=1:length(data.gene)
184 | expressionValue=data.value(i,:);
185 | if mean(expressionValue)>=data.ths_high
186 | threshold(i)=data.ths_high;
187 | else
188 | threshold(i)=max(mean(expressionValue),data.ths_low);
189 | end
190 | end
191 | end
192 | % every single gene is associated to an expression score
193 | for i=1:SampleNumber
194 | Gene_score(:,i)=5.*log(1+(data.value(:,i)./threshold'));
195 | end
196 | case 'global'
197 | Gene_score=5.*log(1+(data.value./data.ths));
198 | end
199 |
200 | % Mapping of the expression data to the model
201 | expression.gene=data.gene;
202 | expression.Rxns=[];
203 | expression.gene_used=[];
204 | expression.count=[];
205 | minSum = false;
206 |
207 | %waitbar(.25,f,'Load GPR parse');
208 | %% load parsedGPR for each model
209 | load(strcat('parsedGPR/parsedGPR_',ref));
210 | %%parsedGPR = GPRparser(model,minSum);%code to compute the parsed GPR using
211 | %%cobratoolbox
212 |
213 | %waitbar(.45,f,'Mapping of the expression data to the model');
214 |
215 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
216 | % run all samples at the same time
217 | expression.value=Gene_score;
218 | % Find wich genes in expression data are used in the model
219 | [gene_id, gene_expr] = findUsedGenesLevels_all(model,expression);
220 | % Link the gene to the model reactions
221 | [expressionRxns, gene_used] = selectGeneFromGPR_all(model, gene_id, gene_expr, parsedGPR, minSum);
222 |
223 | for zz = 1:SampleNumber
224 | gene_all=[];
225 | for j=1:length(gene_used(:,zz))
226 | if ~isempty(gene_used{j,zz})
227 | gene_all(end+1)=str2num(gene_used{j,zz}{1});
228 | end
229 | end
230 | countGene = tabulate(gene_all);
231 | count=[];
232 | for k=1:length(gene_used(:,zz))
233 | if ~isempty(gene_used{k,zz})
234 | tmp=countGene(str2num(gene_used{k,zz}{1}),2);
235 | count(k)=tmp;
236 | else
237 | count(k)=0;
238 | end
239 | end
240 | expression.count=[expression.count count'];
241 | end
242 | expression.Rxns = expressionRxns;
243 | expression.gene_used = gene_used;
244 |
245 |
246 |
247 | %% Compute the score
248 | expressionRxns=expression.Rxns;
249 | significance=1./expression.count;
250 | significance(isinf(significance))=0;
251 | ScorebyTask=[];
252 | ScorebyTask_binary=[];
253 | %waitbar(.75,f,'Compute the task activity score');
254 | for i=1:size(taskInfos,1)
255 | if ~isempty(essentialRxns{i})
256 | rxns=essentialRxns{i};
257 | rxnID=findRxnIDs(model,rxns);
258 | rxnID(rxnID==0)=[];
259 | if ~isempty(rxnID)
260 | expValue=expressionRxns(rxnID,:);
261 | signValue=significance(rxnID,:);
262 | % if no gene is associated with one of the reaction -
263 | % remove the reactions from the count
264 | if ~isempty(find(sum(expValue,2)==-SampleNumber))
265 | signValue(find(sum(expValue,2)==-SampleNumber),:)=[];
266 | expValue(find(sum(expValue,2)==-SampleNumber),:)=[];
267 | end
268 | if ~isempty(expValue)
269 | if size(expValue,1)>1
270 | ScorebyTask(i,:)=sum(expValue.*signValue)./size(expValue,1);
271 | Val=sum(expValue)./size(expValue,1);
272 | ID_up=find(Val>=5*log(2));
273 | ScorebyTask_binary(i,:)=zeros(1,SampleNumber);
274 | ScorebyTask_binary(i,ID_up)=1;
275 | else
276 | ScorebyTask(i,:)=expValue.*signValue;
277 | ID_up=find(expValue>=5*log(2));
278 | ScorebyTask_binary(i,:)=zeros(1,SampleNumber);
279 | ScorebyTask_binary(i,ID_up)=1;
280 | end
281 | else
282 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
283 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
284 | end
285 | else
286 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
287 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
288 | end
289 | else
290 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
291 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
292 | end
293 | end
294 |
295 | detailScoring={};
296 |
297 | tmpSampNum = {};
298 | for j=1:SampleNumber
299 | tmpSampNum{j} = int2str(j);
300 | incR.(['incR',tmpSampNum{j}])=1;
301 | end
302 |
303 | for i=1:size(taskInfos,1)
304 | if ~isempty(essentialRxns{i})
305 | rxns=essentialRxns{i};
306 | rxnID=findRxnIDs(model,rxns);
307 | rxnID(rxnID==0)=[];
308 | for j=1:SampleNumber
309 | if ~isempty(rxnID)
310 | for k=1:length(rxnID)
311 | %1st column = sample ID
312 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+1}=j;
313 | %2nd column = task ID
314 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+2}=i;
315 | %3th column = task score for this sample
316 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+3}=ScorebyTask(i,j);
317 | %4th column = task score in binary version for this sample
318 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+4}=ScorebyTask_binary(i,j);
319 | %5th column = essential reaction associated to this
320 | %task
321 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+5}=rxns(k);
322 | %6th column = expression score associated to the
323 | %reaction listed in the 5th column
324 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+6}=expression.Rxns(rxnID(k),j);
325 | %7th column = gene used to determine the expression of the
326 | %reaction listed in the 5th column
327 | geneName=expression.gene_used(rxnID(k),j);
328 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+7}=geneName;
329 | %8th column = original expression value of the gene
330 | %listed in the 7th column
331 | detailScoring{incR.(['incR',tmpSampNum{j}]),((j-1)*8)+8}=data.value((strcmp(data.gene,geneName{1})),j); % 8.64
332 | incR.(['incR',tmpSampNum{j}])=incR.(['incR',tmpSampNum{j}])+1;
333 | end
334 | end
335 | end
336 | end
337 | score=ScorebyTask;
338 | score_binary=ScorebyTask_binary;
339 | end
340 | end
341 |
342 |
343 | %close(f)
--------------------------------------------------------------------------------
/src/CellFie_slow.m:
--------------------------------------------------------------------------------
1 | function[score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param)
2 | % Compute the score associated to each metabolic task listed in taskstructure based on transcriptomic data
3 | %
4 | % USAGE:
5 | % [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param)
6 | %
7 | % INPUTS:
8 | % data
9 | % .gene cell array containing GeneIDs in the same
10 | % format as model.genes
11 | % .value mRNA expression data structure (genes x samples)associated to each gene metioned in data.gene
12 | % SampleNumber Number of samples
13 | % ref Reference model used to compute the
14 | % metabolic task scores (e.g.,'MT_recon_2_2_entrez.mat')
15 | % OPTIONAL INPUTS:
16 | % param.ThreshType Type of thresholding approach used
17 | % (i.e.,'global' or 'local') (default - local)
18 | % related to the use of a GLOBAL thresholding approach - the threshold value is the same for all the genes
19 | % param.percentile_or_value the threshold can be defined using a value introduced by the user ('value')
20 | % or based on a percentile of the distribution of expression value for all the
21 | % genes and across all samples of your
22 | % dataset ('percentile')
23 | % param.percentile percentile from the distribution of
24 | % expression values for all the genes and across all samples that will be
25 | % used to define the threshold value
26 | % param.value expression value for which a gene is
27 | % considered as active or not (e.g., 5)
28 | %
29 | % related to the use of a LOCAL thresholding approach - the threshold value is different for all the genes
30 | % param.percentile_or_value the threshold can be defined using a value introduced by the user ('value')
31 | % or based on a percentile of the distribution of expression value of a
32 | % specific gene across all samples of your
33 | % dataset ('percentile'-default)
34 | % param.LocalThresholdType option to define the type of local thresholding approach to use
35 | % - 'minmaxmean' (default options )- the threshold for a gene is determined by the mean of expression
36 | % values observed for that gene among all the samples, tissues, or conditions BUT
37 | % the threshold :(i) must be higher or equal to a lower bound and (ii) must be lower
38 | % or equal to an upper bound.
39 | % - 'mean' -the threshold for a gene is defined as the mean expression value
40 | % of this gene across all the samples, tissues, or conditions
41 | % param.percentile_low lower percentile used to define which gene
42 | % are always inactive in the case of use 'MinMaxMean' local thresholding
43 | % approach (default = 25)
44 | % param.percentile_high upper percentile used to define which gene
45 | % are always active in the case of use 'MinMaxMean' local thresholding
46 | % approach (default= 75)
47 | % param.value_low lower expression value used to define which gene
48 | % are always inactive in the case of use 'MinMaxMean' local thresholding
49 | % approach (e.g., 5)
50 | % param.value_high upper expression value used to define which gene
51 | % are always active in the case of use 'MinMaxMean' local thresholding
52 | % approach (e.g., 5)
53 | %
54 | % related to the gene mapping approach used
55 | % param.minSum: instead of using min and max, use min for AND and Sum
56 | % for OR (default: false, i.e. use min)
57 | % OUTPUTS:
58 | % score relative quantification of the activity of a metabolic task in a specific condition
59 | % based on the availability of data for multiple conditions
60 | % score_binary binary version of the metabolic task score
61 | % to determine whether a task is active or inactive in specific
62 | % conditions
63 | % taskInfos Description of the metabolic task assessed
64 | % detailScoring Matrix detailing the scoring
65 | % 1st column = sample ID
66 | % 2nd column = task ID
67 | % 3th column = task score for this sample
68 | % 4th column = task score in binary version for this sample
69 | % 5th column = essential reaction associated to this task
70 | % 6th column = expression score associated to the reaction listed in the 5th column
71 | % 7th column = gene used to determine the expression of the reaction listed in the 5th column
72 | % 8th column = original expression value of the gene listed in the 7th column
73 | %
74 | % .. Authors:
75 | % - Anne Richelle, January 2019
76 | if size(data.value,2) ~= SampleNumber
77 | error('The number of samples defined is not the same as the size of the dataset')
78 | end
79 | if size(data.value,1) ~= length(data.gene)
80 | error('data.value does not have the same number of rows as data.gene')
81 | end
82 | if ~exist('ref','var')
83 | error('The reference model has not been defined - please choose a reference model')
84 | end
85 | if ~exist('param','var')
86 | param.ThreshType='local';
87 | param.percentile_or_value='percentile';
88 | param.LocalThresholdType='minmaxmean';
89 | param.percentile_low=25;
90 | param.percentile_high=75;
91 | end
92 |
93 | f = waitbar(0,'Please wait...');
94 | waitbar(.15,f,'Loading your data');
95 |
96 | %load the info about the task structure
97 | load('taskStructure')
98 | taskInfos=struct2cell(taskStructure);
99 | taskInfos=taskInfos';
100 | taskInfos(:,5:end)=[];
101 |
102 |
103 | %load the reference model in matlab
104 | load(ref);
105 |
106 | %% Depending on the model, load the list of reactions associated with the task
107 | % All these files have the following format
108 | % essentialRxnsbyTask_name_of_model and are located in essentialRxns folder
109 | load(strcat('essentialRxns/essentialRxnsbyTask_',ref));
110 |
111 | % check that at least part of list of gene provided are in the model loaded
112 | ID_model=[];
113 | gene_notInModel=[];
114 | for i=1:length(data.gene)
115 | if isempty(find(strcmp(data.gene{i},model.genes)))
116 | gene_notInModel(end+1)=i;
117 | else
118 | tmpid=find(strcmp(data.gene{i},model.genes)==1);
119 | ID_model(end+1)=tmpid(1);
120 | end
121 | end
122 |
123 | % introduce a warning in the webtool about how many of the genes provided are
124 | % actually mapped to the model
125 | if isempty(gene_notInModel)
126 | display('All genes provided in data are included in the reference model')
127 | else
128 | display([num2str(length(gene_notInModel)),' genes provided are not included in the reference model:'])
129 | data.gene(gene_notInModel)
130 | end
131 |
132 | % remove the gene and associated value provided by the user in data that are not in the model
133 | data.gene(gene_notInModel)=[];
134 | if SampleNumber==1
135 | data.value(gene_notInModel)=[];
136 | else
137 | data.value(gene_notInModel,:)=[];
138 | end
139 |
140 | % get the threshold value and the histogram for the complete dataset and
141 | % print a figure
142 | if SampleNumber>1
143 | linData = reshape(data.value,numel(data.value),1);
144 | else
145 | linData=data.value;
146 | end
147 | linData(linData==0)=[];
148 |
149 | % definition of the thresholds
150 | if strcmp(param.ThreshType,'global') && strcmp(param.percentile_or_value,'percentile')
151 | display('RUN - global: percentile')
152 | l_global = (prctile(log10(linData),param.percentile));
153 | data.ths=10^l_global;
154 | elseif strcmp(param.ThreshType,'global') && strcmp(param.percentile_or_value,'value')
155 | display('RUN - global: value')
156 | data.ths=param.value;
157 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'mean')
158 | display('RUN - local: mean')
159 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'minmaxmean')&& strcmp(param.percentile_or_value,'percentile')
160 | display('RUN - local: minmaxmean: percentile')
161 | l_high = (prctile(log10(linData),param.percentile_high));
162 | data.ths_high=10^l_high;
163 | l_low = (prctile(log10(linData),param.percentile_low));
164 | data.ths_low=10^l_low;
165 | elseif strcmp(param.ThreshType,'local') && strcmp(param.LocalThresholdType,'minmaxmean')&& strcmp(param.percentile_or_value,'value')
166 | display('RUN - local: minmaxmean: value')
167 | data.ths_high=param.value_high;
168 | data.ths_low=param.value_low;
169 | else
170 | error('No analysis triggered')
171 | end
172 |
173 | %% Compute the threshold(s) depending on the approach used
174 | Gene_score=[];
175 | switch param.ThreshType
176 | case 'local'
177 | if strcmp(param.LocalThresholdType,'mean')
178 | %the threshold for each gene is equal to its mean value over
179 | %all the samples
180 | threshold=mean(data.value,2)';
181 | else
182 | threshold=[];
183 | for i=1:length(data.gene)
184 | expressionValue=data.value(i,:);
185 | if mean(expressionValue)>=data.ths_high
186 | threshold(i)=data.ths_high;
187 | else
188 | threshold(i)=max(mean(expressionValue),data.ths_low);
189 | end
190 | end
191 | end
192 | % every single gene is associated to an expression score
193 | for i=1:SampleNumber
194 | Gene_score(:,i)=5.*log(1+(data.value(:,i)./threshold'));
195 | end
196 | case 'global'
197 | Gene_score=5.*log(1+(data.value./data.ths));
198 | end
199 |
200 | % Mapping of the expression data to the model
201 | expression.gene=data.gene;
202 | expression.Rxns=[];
203 | expression.gene_used=[];
204 | expression.count=[];
205 | minSum = false;
206 |
207 | waitbar(.25,f,'Load GPR parse');
208 | %% load parsedGPR for each model
209 | load(strcat('parsedGPR/parsedGPR_',ref));
210 | %%parsedGPR = GPRparser(model,minSum);%code to compute the parsed GPR using
211 | %%cobratoolbox
212 |
213 | waitbar(.45,f,'Mapping of the expression data to the model');
214 | for i=1:SampleNumber
215 | if SampleNumber==1
216 | expression.value=Gene_score;
217 | else
218 | expression.value=Gene_score(:,i);
219 | end
220 | % Find wich genes in expression data are used in the model
221 | [gene_id, gene_expr] = findUsedGenesLevels(model,expression);
222 | % Link the gene to the model reactions
223 | [expressionRxns, gene_used] = selectGeneFromGPR(model, gene_id, gene_expr, parsedGPR, minSum);
224 |
225 | gene_all=[];
226 | for j=1:length(gene_used)
227 | if ~isempty(gene_used{j})
228 | gene_all(end+1)=str2num(gene_used{j}{1});
229 | end
230 | end
231 | countGene = tabulate(gene_all);
232 | count=[];
233 | for k=1:length(gene_used)
234 | if ~isempty(gene_used{k})
235 | tmp=countGene(str2num(gene_used{k}{1}),2);
236 | count(k)=tmp;
237 | else
238 | count(k)=0;
239 | end
240 | end
241 | expression.Rxns=[expression.Rxns expressionRxns];
242 | expression.gene_used=[expression.gene_used gene_used'];
243 | expression.count=[expression.count count'];
244 | end
245 |
246 | %% Compute the score
247 | expressionRxns=expression.Rxns;
248 | significance=1./expression.count;
249 | significance(isinf(significance))=0;
250 | ScorebyTask=[];
251 | ScorebyTask_binary=[];
252 | waitbar(.75,f,'Compute the task activity score');
253 | for i=1:size(taskInfos,1)
254 | if ~isempty(essentialRxns{i})
255 | rxns=essentialRxns{i};
256 | rxnID=findRxnIDs(model,rxns);
257 | rxnID(rxnID==0)=[];
258 | if ~isempty(rxnID)
259 | expValue=expressionRxns(rxnID,:);
260 | signValue=significance(rxnID,:);
261 | % if no gene is associated with one of the reaction -
262 | % remove the reactions from the count
263 | if ~isempty(find(sum(expValue,2)==-SampleNumber))
264 | signValue(find(sum(expValue,2)==-SampleNumber),:)=[];
265 | expValue(find(sum(expValue,2)==-SampleNumber),:)=[];
266 | end
267 | if ~isempty(expValue)
268 | if size(expValue,1)>1
269 | ScorebyTask(i,:)=sum(expValue.*signValue)./size(expValue,1);
270 | Val=sum(expValue)./size(expValue,1);
271 | ID_up=find(Val>=5*log(2));
272 | ScorebyTask_binary(i,:)=zeros(1,SampleNumber);
273 | ScorebyTask_binary(i,ID_up)=1;
274 | else
275 | ScorebyTask(i,:)=expValue.*signValue;
276 | ID_up=find(expValue>=5*log(2));
277 | ScorebyTask_binary(i,:)=zeros(1,SampleNumber);
278 | ScorebyTask_binary(i,ID_up)=1;
279 | end
280 | else
281 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
282 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
283 | end
284 | else
285 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
286 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
287 | end
288 | else
289 | ScorebyTask(i,:)=-1.*ones(1,SampleNumber);
290 | ScorebyTask_binary(i,:)=-1.*ones(1,SampleNumber);
291 | end
292 | end
293 |
294 | detailScoring={};
295 | waitbar(1,f,'Format the score');
296 | for j=1:SampleNumber
297 | incR=1;
298 | for i=1:size(taskInfos,1)
299 | if ~isempty(essentialRxns{i})
300 | rxns=essentialRxns{i};
301 | rxnID=findRxnIDs(model,rxns);
302 | rxnID(rxnID==0)=[];
303 | if ~isempty(rxnID)
304 | for k=1:length(rxnID)
305 | %1st column = sample ID
306 | detailScoring{incR,((j-1)*8)+1}=j;
307 | %2nd column = task ID
308 | detailScoring{incR,((j-1)*8)+2}=i;
309 | %3th column = task score for this sample
310 | detailScoring{incR,((j-1)*8)+3}=ScorebyTask(i,j);
311 | %4th column = task score in binary version for this sample
312 | detailScoring{incR,((j-1)*8)+4}=ScorebyTask_binary(i,j);
313 | %5th column = essential reaction associated to this
314 | %task
315 | detailScoring{incR,((j-1)*8)+5}=rxns(k);
316 | %6th column = expression score associated to the
317 | %reaction listed in the 5th column
318 | detailScoring{incR,((j-1)*8)+6}=expression.Rxns(rxnID(k),j);
319 | %7th column = gene used to determine the expression of the
320 | %reaction listed in the 5th column
321 | geneName=expression.gene_used(rxnID(k),j);
322 | detailScoring{incR,((j-1)*8)+7}=geneName;
323 | %8th column = original expression value of the gene
324 | %listed in the 7th column
325 | detailScoring{incR,((j-1)*8)+8}=data.value(find(strcmp(data.gene,geneName{1})),j);
326 | incR=incR+1;
327 | end
328 | end
329 | end
330 | end
331 | score=ScorebyTask;
332 | score_binary=ScorebyTask_binary;
333 | end
334 | close(f)
335 |
--------------------------------------------------------------------------------
/src/findRxnIDs.m:
--------------------------------------------------------------------------------
1 | function rxnID = findRxnIDs(model, rxnList)
2 | % Finds reaction numbers in a model
3 | %
4 | % USAGE:
5 | %
6 | % rxnID = findRxnIDs(model, rxnList)
7 | %
8 | % INPUTS:
9 | % model: COBRA model strcture
10 | % rxnList: List of reactions
11 | %
12 | % OUTPUT:
13 | % rxnID: IDs for reactions corresponding to rxnList
14 | %
15 | % .. Author: - Markus Herrgard 4/21/06
16 |
17 | if (iscell(rxnList))
18 | [tmp,rxnID] = ismember(rxnList,model.rxns);
19 | else
20 | rxnID = find(strcmp(model.rxns,rxnList));
21 | if (isempty(rxnID))
22 | rxnID = 0;
23 | end
24 | if (length(rxnID) > 1)
25 | rxnID = rxnID(1);
26 | end
27 | end
28 |
--------------------------------------------------------------------------------
/src/findUsedGenesLevels.m:
--------------------------------------------------------------------------------
1 | function [gene_id, gene_expr] = findUsedGenesLevels(model, exprData, printLevel)
2 | % Returns vectors of gene identifiers and corresponding gene expression
3 | % levels for each gene present in the model ('model.genes').
4 | %
5 | % USAGE:
6 | % [gene_id, gene_expr] = findUsedGenesLevels(model, exprData)
7 | %
8 | % INPUTS:
9 | %
10 | % model: input model (COBRA model structure)
11 | %
12 | % exprData: mRNA expression data structure
13 | % .gene cell array containing GeneIDs in the same
14 | % format as model.genes
15 | % .value Vector containing corresponding expression value (FPKM)
16 | %
17 | % OPTIONAL INPUTS:
18 | % printLevel: Printlevel for output (default 0);
19 | %
20 | % OUTPUTS:
21 | %
22 | % gene_id: vector of gene identifiers present in the model
23 | % that are associated with expression data
24 | %
25 | % gene_expr: vector of expression values associated to each
26 | % 'gened_id'
27 | %
28 | %
29 | % Authors: - S. Opdam & A. Richelle May 2017
30 |
31 | if ~exist('printLevel','var')
32 | printLevel = 0;
33 | end
34 |
35 | gene_expr=[];
36 | gene_id = model.genes;
37 |
38 | for i = 1:numel(gene_id)
39 |
40 | cur_ID = gene_id{i};
41 | dataID=find(ismember(exprData.gene,cur_ID)==1);
42 | if isempty (dataID)
43 | gene_expr(i)=-1;
44 | elseif length(dataID)==1
45 | gene_expr(i)=exprData.value(dataID);
46 | elseif length(dataID)>1
47 | if printLevel > 0
48 | disp(['Double for ',num2str(cur_ID)])
49 | end
50 | gene_expr(i)=mean(exprData.value(dataID));
51 | end
52 | end
53 |
54 | end
55 |
--------------------------------------------------------------------------------
/src/findUsedGenesLevels_all.m:
--------------------------------------------------------------------------------
1 | function [gene_id, gene_expr] = findUsedGenesLevels_all(model, exprData, printLevel)
2 | % Returns vectors of gene identifiers and corresponding gene expression
3 | % levels for each gene present in the model ('model.genes').
4 | %
5 | % USAGE:
6 | % [gene_id, gene_expr] = findUsedGenesLevels(model, exprData)
7 | %
8 | % INPUTS:
9 | %
10 | % model: input model (COBRA model structure)
11 | %
12 | % exprData: mRNA expression data structure
13 | % .gene cell array containing GeneIDs in the same
14 | % format as model.genes
15 | % .value Vector containing corresponding expression value (FPKM)
16 | %
17 | % OPTIONAL INPUTS:
18 | % printLevel: Printlevel for output (default 0);
19 | %
20 | % OUTPUTS:
21 | %
22 | % gene_id: vector of gene identifiers present in the model
23 | % that are associated with expression data
24 | %
25 | % gene_expr: vector of expression values associated to each
26 | % 'gened_id'
27 | %
28 | %
29 | % Authors: - S. Opdam & A. Richelle May 2017
30 |
31 | if ~exist('printLevel','var')
32 | printLevel = 0;
33 | end
34 |
35 | gene_expr=[];
36 | gene_id = model.genes;
37 | tmpb = length(exprData.value(1,:));
38 | for i = 1:numel(gene_id)
39 |
40 | cur_ID = gene_id{i};
41 | dataID=find(ismember(exprData.gene,cur_ID)==1);
42 | if isempty (dataID)
43 | gene_expr(i,:)=-ones(1,tmpb);
44 | elseif length(dataID)==1
45 | gene_expr(i,:)=exprData.value(dataID,:);
46 | elseif length(dataID)>1
47 | if printLevel > 0
48 | disp(['Double for ',num2str(cur_ID)])
49 | end
50 | gene_expr(i)=mean(exprData.value(dataID));
51 | end
52 | end
53 |
54 | end
55 |
--------------------------------------------------------------------------------
/src/prctile.m:
--------------------------------------------------------------------------------
1 | function y = prctile(x,p,dim)
2 | %PRCTILE Percentiles of a sample.
3 | % Y = PRCTILE(X,P) returns percentiles of the values in X. P is a scalar
4 | % or a vector of percent values. When X is a vector, Y is the same size
5 | % as P, and Y(i) contains the P(i)-th percentile. When X is a matrix,
6 | % the i-th row of Y contains the P(i)-th percentiles of each column of X.
7 | % For N-D arrays, PRCTILE operates along the first non-singleton
8 | % dimension.
9 | %
10 | % Y = PRCTILE(X,P,DIM) calculates percentiles along dimension DIM. The
11 | % DIM'th dimension of Y has length LENGTH(P).
12 | %
13 | % Percentiles are specified using percentages, from 0 to 100. For an N
14 | % element vector X, PRCTILE computes percentiles as follows:
15 | % 1) The sorted values in X are taken as the 100*(0.5/N), 100*(1.5/N),
16 | % ..., 100*((N-0.5)/N) percentiles.
17 | % 2) Linear interpolation is used to compute percentiles for percent
18 | % values between 100*(0.5/N) and 100*((N-0.5)/N)
19 | % 3) The minimum or maximum values in X are assigned to percentiles
20 | % for percent values outside that range.
21 | %
22 | % PRCTILE treats NaNs as missing values, and removes them.
23 | %
24 | % Examples:
25 | % y = prctile(x,50); % the median of x
26 | % y = prctile(x,[2.5 25 50 75 97.5]); % a useful summary of x
27 | %
28 | % See also IQR, MEDIAN, NANMEDIAN, QUANTILE.
29 |
30 | % Copyright 1993-2015 The MathWorks, Inc.
31 |
32 |
33 | if ~isvector(p) || numel(p) == 0 || any(p < 0 | p > 100) || ~isreal(p)
34 | error(message('stats:prctile:BadPercents'));
35 | end
36 |
37 | % Figure out which dimension prctile will work along.
38 | sz = size(x);
39 | if nargin < 3
40 | dim = find(sz ~= 1,1);
41 | if isempty(dim)
42 | dim = 1;
43 | end
44 | dimArgGiven = false;
45 | else
46 | % Permute the array so that the requested dimension is the first dim.
47 | nDimsX = ndims(x);
48 | perm = [dim:max(nDimsX,dim) 1:dim-1];
49 | x = permute(x,perm);
50 | % Pad with ones if dim > ndims.
51 | if dim > nDimsX
52 | sz = [sz ones(1,dim-nDimsX)];
53 | end
54 | sz = sz(perm);
55 | dim = 1;
56 | dimArgGiven = true;
57 | end
58 |
59 | % If X is empty, return all NaNs.
60 | if isempty(x)
61 | if isequal(x,[]) && ~dimArgGiven
62 | y = nan(size(p),'like',x);
63 | else
64 | szout = sz; szout(dim) = numel(p);
65 | y = nan(szout,'like',x);
66 | end
67 |
68 | else
69 | % Drop X's leading singleton dims, and combine its trailing dims. This
70 | % leaves a matrix, and we can work along columns.
71 | nrows = sz(dim);
72 | ncols = numel(x) ./ nrows;
73 | x = reshape(x, nrows, ncols);
74 |
75 | x = sort(x,1);
76 | n = sum(~isnan(x), 1); % Number of non-NaN values in each column
77 |
78 | % For columns with no valid data, set n=1 to get nan in the result
79 | n(n==0) = 1;
80 |
81 | % If the number of non-nans in each column is the same, do all cols at once.
82 | if all(n == n(1))
83 | n = n(1);
84 | if isequal(p,50) % make the median fast
85 | if rem(n,2) % n is odd
86 | y = x((n+1)/2,:);
87 | else % n is even
88 | y = (x(n/2,:) + x(n/2+1,:))/2;
89 | end
90 | else
91 | y = interpColsSame(x,p,n);
92 | end
93 |
94 | else
95 | % Get percentiles of the non-NaN values in each column.
96 | y = interpColsDiffer(x,p,n);
97 | end
98 |
99 | % Reshape Y to conform to X's original shape and size.
100 | szout = sz; szout(dim) = numel(p);
101 | y = reshape(y,szout);
102 | end
103 | % undo the DIM permutation
104 | if dimArgGiven
105 | y = ipermute(y,perm);
106 | end
107 |
108 | % If X is a vector, the shape of Y should follow that of P, unless an
109 | % explicit DIM arg was given.
110 | if ~dimArgGiven && isvector(x)
111 | y = reshape(y,size(p));
112 | end
113 |
114 |
115 | function y = interpColsSame(x, p, n)
116 | %INTERPCOLSSAME An aternative approach of 1-D linear interpolation which is
117 | % faster than using INTERP1Q and can deal with invalid data so long as
118 | % all columns have the same number of valid entries (scalar n).
119 |
120 | % Make p a column vector. Note that n is assumed to be scalar.
121 | if isrow(p)
122 | p = p';
123 | end
124 |
125 | % Form the vector of index values (numel(p) x 1)
126 | r = (p/100)*n;
127 | k = floor(r+0.5); % K gives the index for the row just before r
128 | kp1 = k + 1; % K+1 gives the index for the row just after r
129 | r = r - k; % R is the ratio between the K and K+1 rows
130 |
131 | % Find indices that are out of the range 1 to n and cap them
132 | k(k<1 | isnan(k)) = 1;
133 | kp1 = bsxfun( @min, kp1, n );
134 |
135 | % Use simple linear interpolation for the valid precentages
136 | y = bsxfun(@times, 0.5-r, x(k,:)) + bsxfun(@times, 0.5+r, x(kp1,:));
137 |
138 | % Make sure that values we hit exactly are copied rather than interpolated
139 | exact = (r==-0.5);
140 | if any(exact)
141 | y(exact,:) = x(k(exact),:);
142 | end
143 |
144 | function y = interpColsDiffer(x, p, n)
145 | %INTERPCOLSDIFFER A simple 1-D linear interpolation of columns that can
146 | %deal with columns with differing numbers of valid entries (vector n).
147 |
148 | [nrows, ncols] = size(x);
149 |
150 | % Make p a column vector. n is already a row vector with ncols columns.
151 | if isrow(p)
152 | p = p';
153 | end
154 |
155 | % Form the grid of index values (numel(p) x numel(n))
156 | r = (p/100)*n;
157 | k = floor(r+0.5); % K gives the index for the row just before r
158 | kp1 = k + 1; % K+1 gives the index for the row just after r
159 | r = r - k; % R is the ratio between the K and K+1 rows
160 |
161 | % Find indices that are out of the range 1 to n and cap them
162 | k(k<1 | isnan(k)) = 1;
163 | kp1 = bsxfun( @min, kp1, n );
164 |
165 | % Convert K and Kp1 into linear indices
166 | offset = nrows*(0:ncols-1);
167 | k = bsxfun( @plus, k, offset );
168 | kp1 = bsxfun( @plus, kp1, offset );
169 |
170 | % Use simple linear interpolation for the valid precentages.
171 | % Note that NaNs in r produce NaN rows.
172 | y = (0.5-r).*x(k) + (0.5+r).*x(kp1);
173 |
174 | % Make sure that values we hit exactly are copied rather than interpolated
175 | exact = (r==-0.5);
176 | if any(exact(:))
177 | y(exact) = x(k(exact));
178 | end
179 |
--------------------------------------------------------------------------------
/src/runCellFie.m:
--------------------------------------------------------------------------------
1 | addpath(genpath('/var/www/pinapl-py-site/'))
2 | addpath(genpath('/var/www/MATLAB/lib/'))
3 | addpath(genpath('/var/www/MATLAB/lib/MetTasks/'))
4 | addpath(genpath('/var/www/MATLAB/lib/MetTasks/matlab'))
5 | addpath(genpath('/var/www/MATLAB/lib//MetTasks/matlab/yaml'))
6 | addpath(genpath('/var/www/MATLAB/lib/cobratoolbox/'))
7 |
8 |
9 | yml = YAML.read(config_file);
10 |
11 | dataAll = readtable(data_file);
12 | 'input data head:'
13 | dataAll(1:10,:)
14 |
15 | ThreshType = yml.ThreshType
16 | SampleNumber = yml.SampleNumber
17 | ref = yml.ref
18 |
19 | percentile_or_value = yml.percentile_or_value
20 | percentile = yml.percentile
21 | value = yml.value
22 |
23 | EnoughSamples = yml.EnoughSamples
24 | LocalThresholdType = yml.LocalThresholdType
25 | percentile_low = yml.percentileLow
26 | percentile_high = yml.percentileHigh
27 | value_low = yml.valueLow
28 | value_high = yml.valueHigh
29 |
30 | data = {};
31 | data.gene = string( table2array( dataAll(:,1) ) );
32 | data.value = table2array( dataAll(:,2:end) );
33 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,ThreshType,SampleNumber,ref,percentile_or_value,percentile,value,LocalThresholdType,percentile_low,percentile_high,value_low,value_high)
34 |
35 | rownames=taskInfos(:,2)
36 | colnames=1:SampleNumber
37 | score_long=wide2long(score,rownames,colnames)
38 |
39 | fname='Analysis/Output/'
40 | save 'Analysis/Output/score.mat' score
41 | tab=table(taskInfos,score)
42 | writetable(tab,fullfile(fname, 'score.csv'))
43 |
44 | % write names if present
45 | tableNames = dataAll.Properties.VariableNames
46 | writetable(tab,fullfile(fname, 'annotation.csv'))
47 |
48 | D2 = pdist(score);
49 | Z2 = linkage(D2,'average');
50 | order2 = optimalleaforder(Z2, D2);
51 | D3 = pdist(score');
52 | Z3 = linkage(D3,'average');
53 | order3 = optimalleaforder(Z3, D3);
54 |
55 | score_cluster = score(order2,order3);
56 |
57 | figure(2)
58 | imagesc(score_cluster)
59 | ax=gca;
60 | ax.YTick=1:length(taskInfos(:,2));
61 | ax.YTickLabel=taskInfos(order2,2);
62 |
63 | ax.XTick=1:length(colnames);
64 | ax.XTickLabel=colnames(order3);
65 | colorbar
66 | saveas(figure(2),'Analysis/Figures/score_heatmap.png')
67 |
68 | figure(3)
69 | dendrogram(Z3)
70 | saveas(figure(3),'Analysis/Figures/score_dendro.png')
71 |
72 | fname='Analysis/Output/'
73 | save 'Analysis/Output/score_binary.mat' score_binary
74 | tab=table(taskInfos,score_binary)
75 | writetable(tab,fullfile(fname, 'score_binary.csv'))
76 |
77 | D2 = pdist(score_binary);
78 | Z2 = linkage(D2,'average');
79 | order2 = optimalleaforder(Z2, D2);
80 | D3 = pdist(score_binary');
81 | Z3 = linkage(D3,'average');
82 | order3 = optimalleaforder(Z3, D3);
83 |
84 | score_bin_cluster = score_binary(order2,order3);
85 |
86 | figure('units','normalized','outerposition',[0 0 1 1])
87 | imagesc(score_bin_cluster)
88 | ax=gca;
89 |
90 | ax.XTick=1:length(colnames);
91 | ax.XTickLabel=colnames(order3);
92 | colorbar
93 | fig=gcf;
94 | saveas(figure(fig.Number),'Analysis/Figures/score_bin_heatmap.png')
95 |
96 | figure(3)
97 | dendrogram(Z3)
98 | saveas(figure(3),'Analysis/Figures/score_bin_dendro.png')
99 |
100 | save 'Analysis/Output/taskInfos.mat' taskInfos
101 | fname='Analysis/Output/'
102 | tab=table(taskInfos)
103 | writetable(tab,fullfile(fname, 'taskInfos.csv'))
104 |
105 | exit
106 | exit
--------------------------------------------------------------------------------
/src/selectGeneFromGPR.m:
--------------------------------------------------------------------------------
1 | function [expressionCol, gene_used] = selectGeneFromGPR(model, gene_names, gene_exp, parsedGPR, minSum)
2 | % Map gene expression to reaction expression using the GPR rules. An AND
3 | % will be replaced by MIN and an OR will be replaced by MAX.
4 | %
5 | % USAGE:
6 | % expressionCol = selectGeneFromGPR(model, gene_names, gene_exp, parsedGPR, minMax)
7 | %
8 | % INPUTS:
9 | % model: COBRA model struct
10 | % gene_names: gene identifiers corresponding to gene_exp. Names must
11 | % be in the same format as model.genes (column vector)
12 | % (as returned by "findUsedGeneLevels.m")
13 | % gene_exp: gene FPKM/expression values, corresponding to names (column vector)
14 | % (as returned by "findUsedGeneLevels.m")
15 | % parsedGPR: GPR matrix as returned by "GPRparser.m"
16 | %
17 | % OPTIONAL INPUTS:
18 | % minSum: instead of using min and max, use min for AND and Sum
19 | % for OR
20 | %
21 | % OUTPUTS:
22 | % expressionCol: reaction expression, corresponding to model.rxns.
23 | % No gene-expression data and orphan reactions will
24 | % be given a value of -1.
25 | %
26 | % AUTHOR: Anne Richelle, May 2017
27 |
28 |
29 | if ~exist('minSum','var')
30 | minSum = false;
31 | end
32 | gene_used={};
33 | for i=1:length(model.rxns)
34 | gene_used{i}='';
35 | end
36 |
37 | % -1 means unknown/no data
38 | expressionCol = -1*ones(length(model.rxns),1);
39 | for i = 1:length(model.rxns)
40 | curExprArr=parsedGPR{i};
41 | curExpr= [];
42 | gene_potential=[];
43 | for j=1:length(curExprArr)
44 | if length(curExprArr{j})>=1
45 | geneID = find(ismember(gene_names,curExprArr{j}));
46 | %geneID = find(ismember(gene_names,str2num(curExprArr{j}{1})));
47 | % if the gene is measured
48 | if ~isempty(geneID)
49 | if minSum
50 | % This is an or rule, so we sum up all options.
51 | curExpr= [curExpr, sum(gene_exp(geneID))];
52 | gene_potential=[gene_potential, gene_names(geneID)'];
53 | else
54 | % If there is data for any gene in 'AND' rule, take the minimum value
55 | [minGenevalue, minID]=min(gene_exp(geneID));
56 | curExpr= [curExpr, minGenevalue]; %If there is data for any gene in 'AND' rule, take the minimum value
57 | gene_potential=[gene_potential, gene_names(geneID(minID))];
58 | end
59 | end
60 | end
61 | end
62 | if ~isempty(curExpr)
63 | if minSum
64 | % in case of min sum these are and clauses that are combined, so its the minimum.
65 | [expressionCol(i), ID_min]=min(curExpr);
66 | gene_used{i}=gene_potential(ID_min);
67 | else
68 | % if there is data for any gene in the 'OR' rule, take the maximum value
69 | [expressionCol(i), ID_max]=max(curExpr);
70 | gene_used{i}=gene_potential(ID_max);
71 | end
72 | end
73 | end
74 |
75 | end
--------------------------------------------------------------------------------
/src/selectGeneFromGPR_all.m:
--------------------------------------------------------------------------------
1 | function [expressionCol, gene_used] = selectGeneFromGPR_all(model, gene_names, gene_exp, parsedGPR, minSum)
2 | % Map gene expression to reaction expression using the GPR rules. An AND
3 | % will be replaced by MIN and an OR will be replaced by MAX.
4 | %
5 | % USAGE:
6 | % expressionCol = selectGeneFromGPR(model, gene_names, gene_exp, parsedGPR, minMax)
7 | %
8 | % INPUTS:
9 | % model: COBRA model struct
10 | % gene_names: gene identifiers corresponding to gene_exp. Names must
11 | % be in the same format as model.genes (column vector)
12 | % (as returned by "findUsedGeneLevels.m")
13 | % gene_exp: gene FPKM/expression values, corresponding to names (column vector)
14 | % (as returned by "findUsedGeneLevels.m")
15 | % parsedGPR: GPR matrix as returned by "GPRparser.m"
16 | %
17 | % OPTIONAL INPUTS:
18 | % minSum: instead of using min and max, use min for AND and Sum
19 | % for OR
20 | %
21 | % OUTPUTS:
22 | % expressionCol: reaction expression, corresponding to model.rxns.
23 | % No gene-expression data and orphan reactions will
24 | % be given a value of -1.
25 | %
26 | % AUTHOR: Anne Richelle, May 2017
27 |
28 |
29 | if ~exist('minSum','var')
30 | minSum = false;
31 | end
32 | numSamp = length(gene_exp(1,:));
33 | gene_used={};
34 | for i=length(model.rxns):-1:1
35 | for zz = 1:numSamp
36 | gene_used{i,zz}='';
37 | end
38 | end
39 |
40 | SampStr = {};
41 | for zz = 1:numSamp
42 | SampStr{zz} = int2str(zz);
43 | end
44 |
45 | % -1 means unknown/no data
46 | expressionCol = -1*ones(length(model.rxns),length(gene_exp(1,:)));
47 | for i = 1:length(model.rxns)
48 | curExprArr=parsedGPR{i};
49 | for zz = 1:numSamp
50 | % tmpzz = int2str(zz);
51 | tmpStuc.(['curExpr',SampStr{zz}])= [];
52 | tmpStuc.(['gene_potential',SampStr{zz}])=[];
53 | end
54 | for j=1:length(curExprArr)
55 | if length(curExprArr{j})>=1
56 | geneID = find(ismember(gene_names,curExprArr{j}));
57 | %geneID = find(ismember(gene_names,str2num(curExprArr{j}{1})));
58 | % if the gene is measured
59 | if ~isempty(geneID)
60 | for zz = 1:numSamp
61 | % tmpzz = int2str(zz);
62 | if minSum
63 | % This is an or rule, so we sum up all options.
64 | tmpStuc.(['curExpr',SampStr{zz}])= [tmpStuc.(['curExpr',SampStr{zz}]), sum(gene_exp(geneID,zz))];
65 | tmpStuc.(['gene_potential',SampStr{zz}])=[tmpStuc.(['gene_potential',SampStr{zz}]), gene_names(geneID,zz)'];
66 | else
67 | % If there is data for any gene in 'AND' rule, take the minimum value
68 | [minGenevalue, minID]=min(gene_exp(geneID,zz));
69 | tmpStuc.(['curExpr',SampStr{zz}])= [tmpStuc.(['curExpr',SampStr{zz}]), minGenevalue]; %If there is data for any gene in 'AND' rule, take the minimum value
70 | tmpStuc.(['gene_potential',SampStr{zz}])=[tmpStuc.(['gene_potential',SampStr{zz}]), gene_names(geneID(minID))];
71 | end
72 | end
73 | end
74 | end
75 | end
76 | for zz = 1:numSamp
77 | % tmpzz = int2str(zz);
78 | if ~isempty(tmpStuc.(['curExpr',SampStr{zz}]))
79 | if minSum
80 | % in case of min sum these are and clauses that are combined, so its the minimum.
81 | [expressionCol(i,zz), ID_min]=min(tmpStuc.(['curExpr',SampStr{zz}]));
82 | gene_used{i,zz}=tmpStuc.(['gene_potential',SampStr{zz}])(ID_min);
83 | else
84 | % if there is data for any gene in the 'OR' rule, take the maximum value
85 | [expressionCol(i,zz), ID_max]=max(tmpStuc.(['curExpr',SampStr{zz}]));
86 | gene_used{i,zz}=tmpStuc.(['gene_potential',SampStr{zz}])(ID_max);
87 | end
88 | end
89 | end
90 | end
91 |
92 | end
--------------------------------------------------------------------------------
/src/tabulate.m:
--------------------------------------------------------------------------------
1 | function table = tabulate(x)
2 | %TABULATE Frequency table.
3 | % TABLE = TABULATE(X) takes a vector X and returns a matrix, TABLE.
4 | % The first column of TABLE contains the unique values of X. The
5 | % second is the number of instances of each value. The last column
6 | % contains the percentage of each value. If the elements of X are
7 | % non-negative integers, then the output includes 0 counts for any
8 | % integers that are between 1 and max(X) but do not appear in X.
9 | %
10 | % TABLE = TABULATE(X), where X is a categorical variable, character
11 | % array, or a cell array of strings, returns TABLE as a cell array. The
12 | % first column contains the unique string values in X, and the other two
13 | % columns are as above.
14 | %
15 | % TABULATE with no output arguments returns a formatted table
16 | % in the command window.
17 | %
18 | % See also PARETO.
19 |
20 | % Copyright 1993-2011 The MathWorks, Inc.
21 |
22 |
23 | isnum = isnumeric(x);
24 | if isnum && ~isfloat(x)
25 | % use of hist() below requires float
26 | x = double(x);
27 | end
28 | if isnum
29 | if min(size(x)) > 1,
30 | error(message('stats:tabulate:InvalidData'));
31 | end
32 |
33 | y = x(~isnan(x));
34 | else
35 | y = x;
36 | end
37 |
38 | if ~isnum || any(y ~= round(y)) || any(y < 1);
39 | docell = true;
40 | [y,yn,yl] = grp2idx(y);
41 | maxlevels = length(yn);
42 | else
43 | docell = false;
44 | maxlevels = max(y);
45 | %yn = cellstr(num2str((1:maxlevels)'));
46 | end
47 |
48 | [counts values] = hist(y,(1:maxlevels));
49 |
50 | total = sum(counts);
51 | percents = 100*counts./total;
52 |
53 | if nargout == 0
54 | if docell
55 | width = max(cellfun('length',yn));
56 | width = max(5, min(50, width));
57 | else
58 | width = 5;
59 | end
60 |
61 | % Create format strings similar to: ' %5s %5d %6.2f%%\n'
62 | fmt1 = sprintf(' %%%ds %%5d %%6.2f%%%%\n',width);
63 | fmt2 = sprintf(' %%%ds %%5s %%6s\n',width);
64 | fprintf(1,fmt2,'Value','Count','Percent');
65 | if docell
66 | for j=1:maxlevels
67 | fprintf(1,fmt1,yn{j},counts(j),percents(j));
68 | end
69 | else
70 | fprintf(1,' %5d %5d %6.2f%%\n',[values' counts' percents']');
71 | end
72 | else
73 | if ~docell
74 | table = [values' counts' percents'];
75 | elseif isnum
76 | table = [yl(:) counts' percents'];
77 | else
78 | table = [yn num2cell([counts' percents'])];
79 | end
80 | end
81 |
--------------------------------------------------------------------------------
/src/wide2long.m:
--------------------------------------------------------------------------------
1 | function longm = wide2long(ds,rownames,colnames)
2 | % dstmp=mat2dataset(ds)
3 | % dstmp.row = rownames(:,2)
4 | % dsNew = stack(dstmp,dstmp.Properties.VarNames(1:end-1),...
5 | % 'newDataVarName','Scores')
6 | %ds.Properties.VarNames = colnames
7 | longm = {};
8 | longm.sample = [];
9 | longm.task = {};
10 | longm.value = [];
11 | count=1;
12 | for i=1:size(ds,2)
13 | for j=1:size(ds,1)
14 | longm.sample(count) = colnames(i);
15 | longm.task{count} = rownames{j};
16 | longm.value(count)= ds(j,i);
17 | count=count+1;
18 | end
19 | end
20 | end
21 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_percentile.mat:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataRecon22_global_percentile.mat
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_percentile.score.csv:
--------------------------------------------------------------------------------
1 | 0.49072,0.00016595,0.00023568
2 | 9.3058,10.029,7.5586
3 | 1.6873,2.584,1.2408
4 | 5.0692,5.1587,4.2539
5 | 5.6569,5.9512,5.7515
6 | 11.594,12.177,12.477
7 | 3.8049,3.0388,2.2707
8 | 2.8455,3.4442,2.6871
9 | 3.3483,3.5031,3.8042
10 | 2.4659,2.3686,2.6555
11 | 3.5262,3.6835,3.9291
12 | 2.4976,2.3548,2.6472
13 | 2.9415,3.1434,3.4465
14 | 2.3254,2.2245,2.5262
15 | 3.6881,3.8487,4.1232
16 | 2.6426,2.4814,2.8174
17 | 2.1769,2.0497,2.6668
18 | 4.0329,4.5881,4.9157
19 | 1.9297,3.1712,2.9727
20 | 1.9297,3.1712,2.9727
21 | 1.6443,3.0989,1.6668
22 | 1.233,1.2716,1.5653
23 | 0.032902,0.55107,0.35494
24 | 3.9082,3.1252,4.0782
25 | 3.9082,3.1252,4.0782
26 | 7.8721,7.7428,7.7295
27 | 8.0614,7.9328,7.9076
28 | 6.5057,5.8418,5.4441
29 | 7.5954,7.2938,7.2775
30 | 4.8427,5.7154,4.4122
31 | 5.8558,3.368,0.93205
32 | 10.463,10.971,11.216
33 | 4.6696,3.1009,3.5872
34 | 6.7618,5.1329,5.3198
35 | 4.1184,3.1738,3.9359
36 | 6.0099,3.2975,4.2281
37 | 5.1715,4.4874,3.2655
38 | 4.0906,2.0478,2.3297
39 | 11.256,13.884,13.077
40 | 4.7562,4.657,3.9332
41 | 7.3581,5.0441,4.7382
42 | 3.2496,4.9823,3.6265
43 | 7.3415,5.2471,5.0767
44 | 2.2144,2.8574,3.1666
45 | 3.6357,4.2634,3.4109
46 | 3.5549,3.6842,3.3231
47 | 6.6597,5.324,5.291
48 | 4.878,4.1579,3.9195
49 | 6.3822,5.0555,4.9789
50 | 1.9619,2.1542,1.4581
51 | 7.3415,5.2471,5.0767
52 | 7.6457,8.7332,7.8379
53 | 7.3095,8.2935,7.5293
54 | 2.5918,2.8649,1.918
55 | 2.8087,3.2589,1.9778
56 | 8.8155,8.9032,6.617
57 | 2.4373,2.1198,2.9667
58 | 0.30871,0.2157,0.43275
59 | 1.9196,2.0301,1.8458
60 | 5.764,6.038,4.9479
61 | 8.539,8.2484,7.9899
62 | 4.5082,4.3859,4.0992
63 | 3.2774,2.84,2.9064
64 | 5.9928,6.2025,5.7434
65 | 4.6707,4.6376,4.5634
66 | 4.1542,4.5535,3.8708
67 | 1.5213,0.74164,1.325
68 | 4.7017,3.8949,3.5856
69 | 3.2158,4.5496,2.7535
70 | 7.3014,7.0464,6.7987
71 | 4.187,4.1667,4.0053
72 | 8.2725,8.1988,7.9443
73 | 4.5584,4.6184,4.2798
74 | 8.3799,7.3012,6.5349
75 | 0.050813,0.38908,0.016253
76 | 3.1212,3.5718,5.4223
77 | 7.0733,7.1598,6.554
78 | 4.4648,4.3555,4.1145
79 | 2.1796,1.7667,2.6772
80 | 0.35616,2.1324,0.62167
81 | 2.644,2.981,2.6674
82 | 2.9177,2.7211,2.2149
83 | -1,-1,-1
84 | 10.601,4.5071,1.1237
85 | 8.9175,8.8144,8.539
86 | 4.1981,4.1678,4.0439
87 | 24.125,14.731,19.235
88 | 1.5403,1.2034,2.6011
89 | 1.4758,2.1935,1.3455
90 | 10.087,9.2695,9.3618
91 | 4.3277,4.5102,4.2267
92 | 4.5548,5.317,4.1596
93 | 4.1121,5.2746,4.1713
94 | 7.3062,7.868,7.1441
95 | 2.7958,2.911,2.9186
96 | 2.6364,1.2427,1.7277
97 | 4.2742,4.2994,4.0888
98 | 0.23174,1.0037,0.44504
99 | 1.772,1.0484,3.4671
100 | 3.7663,3.898,3.8501
101 | 0.39937,1.147,0.30196
102 | 4.3509,4.3071,4.0264
103 | 4.3801,4.43,3.9937
104 | 3.1303,4.3897,2.2731
105 | 4.1268,4.0847,3.8258
106 | 1.6486,1.3289,0.64437
107 | 1.2068,1.5802,0.81378
108 | 3.627,3.5802,3.616
109 | 3.3425,4.393,4.8159
110 | 4.2932,4.33,4.1534
111 | 4.5608,6.5114,5.2122
112 | 5.3288,5.4014,6.5762
113 | 7.7732,8.5305,7.6858
114 | 2.8343,2.7068,2.6962
115 | 4.3767,4.3896,4.0351
116 | 0.60252,0.85274,0.1835
117 | 0.67891,0.67049,0.28222
118 | 0.034891,0.72035,0.0019789
119 | 7.8978,7.6529,7.6359
120 | 4.5159,4.5276,4.372
121 | 3.535,4.1571,4.5668
122 | 4.412,4.3696,4.1235
123 | 4.0078,4.0118,3.7923
124 | 0.47751,0.10891,2.1055
125 | 1.1102,0.63961,1.5802
126 | 1.3827,0.78628,2.1577
127 | 0.52721,0.34203,2.0156
128 | 1.3524,0.94785,2.1926
129 | 0.060495,0.5381,0.17796
130 | 2.2608,2.8487,0.88481
131 | 4.5093,4.4696,4.1586
132 | 0.5429,7.0831,0.56908
133 | 0.021762,0.52531,0.071917
134 | 3.1859,2.9929,1.676
135 | 4.3561,4.4168,4.1221
136 | 3.1583,4.1999,1.9746
137 | 8.0899,9.1785,8.3218
138 | 2.4701,3.6634,2.3457
139 | 7.6457,8.7332,7.8379
140 | 1.7111,3.8814,2.7302
141 | 2.1962,2.8072,2.3354
142 | 9.6393,0.96194,0.39446
143 | 3.5976,3.289,2.855
144 | 3.5556,3.2535,2.7757
145 | 3.5601,3.2502,2.7816
146 | 3.353,3.0314,2.4169
147 | 3.4047,3.222,2.585
148 | 3.4195,2.8689,2.3573
149 | 3.4033,3.2169,2.9234
150 | 3.3528,3.1411,2.7321
151 | 4.2567,4.5556,4.2036
152 | 1.3438,1.4971,1.0853
153 | 3.392,3.4796,3.0537
154 | 1.403,1.3051,0.8988
155 | 4.024,4.4055,3.8
156 | 1.9302,2.2707,1.6153
157 | 4.024,4.4055,3.8
158 | 1.365,1.5766,1.0847
159 | 1.5221,1.7147,1.3997
160 | 2.1265,2.4075,1.6991
161 | 0.5315,0.98767,0.54825
162 | 2.1655,2.3978,1.7132
163 | 4.2877,4.4409,4.307
164 | 1.4041,1.3598,0.89755
165 | 6.5507,7.115,6.3307
166 | 4.9448,5.1112,4.6635
167 | 0.52472,0.45373,0.44809
168 | 1.6729,1.6245,1.7379
169 | 0.57897,0.51613,0.40707
170 | 0.57897,0.51613,0.40707
171 | 2.6123,2.252,3.9507
172 | 3.1727,2.9069,2.4571
173 | 2.3915,2.2634,1.6292
174 | 2.3127,2.1812,1.5802
175 | 7.2675,5.3227,8.363
176 | 4.0804,4.9348,4.8917
177 | 1.8308,2.2489,1.9538
178 | 2.0541,3.2725,2.1381
179 | 0.9981,0.35549,0.96297
180 | 3.8083,2.451,2.483
181 | 10.294,7.1071,7.8429
182 | 3.6186,4.442,3.1877
183 | 2.6575,2.7643,3.2177
184 | 0.65251,0.92051,0.77798
185 | 2.7585,2.8018,3.8014
186 | -1,-1,-1
187 | -1,-1,-1
188 | 2.0951,2.188,2.8557
189 | 3.8128,3.4169,4.6459
190 | 2.292,2.4326,3.3945
191 | 6.7481,7.2979,7.4058
192 | 0.45065,0.71456,0.42469
193 | 0.45065,0.71456,0.42469
194 | 0.41137,0.48702,0.47997
195 | 0.30559,0.28727,0.28394
196 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_percentile.score_binary.csv:
--------------------------------------------------------------------------------
1 | 0,0,0
2 | 1,1,1
3 | 1,1,1
4 | 1,1,1
5 | 1,1,1
6 | 1,1,1
7 | 1,1,1
8 | 1,1,1
9 | 1,1,1
10 | 1,1,1
11 | 1,1,1
12 | 1,1,1
13 | 1,1,1
14 | 1,1,1
15 | 1,1,1
16 | 1,1,1
17 | 1,1,1
18 | 1,1,1
19 | 1,1,1
20 | 1,1,1
21 | 1,1,1
22 | 1,1,1
23 | 0,0,0
24 | 1,1,1
25 | 1,1,1
26 | 1,1,1
27 | 1,1,1
28 | 1,1,1
29 | 1,1,1
30 | 1,1,1
31 | 1,1,1
32 | 1,1,1
33 | 1,1,1
34 | 1,1,1
35 | 1,1,1
36 | 1,0,1
37 | 1,1,1
38 | 1,1,1
39 | 1,1,1
40 | 1,1,1
41 | 1,1,1
42 | 1,1,1
43 | 1,1,1
44 | 0,0,0
45 | 1,1,1
46 | 1,1,1
47 | 1,1,1
48 | 1,1,1
49 | 1,1,1
50 | 1,1,1
51 | 1,1,1
52 | 1,1,1
53 | 1,1,1
54 | 1,1,1
55 | 1,1,1
56 | 1,1,1
57 | 1,1,1
58 | 0,0,0
59 | 1,1,1
60 | 1,1,1
61 | 1,1,1
62 | 1,1,1
63 | 1,1,1
64 | 1,1,1
65 | 1,1,1
66 | 1,1,1
67 | 0,0,0
68 | 1,1,1
69 | 0,1,0
70 | 1,1,1
71 | 1,1,1
72 | 1,1,1
73 | 1,1,1
74 | 1,1,1
75 | 0,0,0
76 | 0,1,1
77 | 1,1,1
78 | 1,1,1
79 | 1,1,1
80 | 0,1,0
81 | 1,1,1
82 | 1,1,1
83 | -1,-1,-1
84 | 1,1,0
85 | 1,1,1
86 | 1,1,1
87 | 1,1,1
88 | 0,0,1
89 | 0,1,0
90 | 1,1,1
91 | 1,1,1
92 | 1,1,1
93 | 1,1,1
94 | 1,1,1
95 | 1,1,1
96 | 1,0,0
97 | 1,1,1
98 | 0,0,0
99 | 0,1,1
100 | 1,1,1
101 | 0,0,0
102 | 1,1,1
103 | 1,1,1
104 | 1,1,1
105 | 1,1,1
106 | 1,1,0
107 | 1,1,0
108 | 1,1,1
109 | 1,1,1
110 | 1,1,1
111 | 1,1,1
112 | 1,1,1
113 | 1,1,1
114 | 1,1,1
115 | 1,1,1
116 | 1,1,0
117 | 1,1,1
118 | 0,0,0
119 | 1,1,1
120 | 1,1,1
121 | 1,1,1
122 | 1,1,1
123 | 1,1,1
124 | 0,0,1
125 | 0,0,1
126 | 0,0,1
127 | 0,0,1
128 | 0,0,1
129 | 0,1,0
130 | 1,1,0
131 | 1,1,1
132 | 0,1,0
133 | 0,1,0
134 | 1,1,0
135 | 1,1,1
136 | 1,1,1
137 | 1,1,1
138 | 1,1,1
139 | 1,1,1
140 | 0,1,1
141 | 1,1,1
142 | 1,0,0
143 | 1,1,1
144 | 1,1,1
145 | 1,1,1
146 | 1,1,1
147 | 1,1,1
148 | 1,1,1
149 | 1,1,1
150 | 1,1,1
151 | 1,1,1
152 | 1,1,1
153 | 1,1,1
154 | 1,1,1
155 | 1,1,1
156 | 1,1,1
157 | 1,1,1
158 | 1,1,1
159 | 1,1,1
160 | 1,1,1
161 | 1,1,1
162 | 1,1,1
163 | 1,1,1
164 | 1,1,1
165 | 1,1,1
166 | 1,1,1
167 | 1,1,1
168 | 1,1,1
169 | 1,1,1
170 | 1,1,1
171 | 1,1,1
172 | 1,1,1
173 | 1,1,1
174 | 1,1,1
175 | 1,1,1
176 | 1,1,1
177 | 1,1,1
178 | 0,1,0
179 | 1,0,1
180 | 1,1,1
181 | 1,1,1
182 | 1,1,1
183 | 0,0,0
184 | 0,1,0
185 | 1,1,1
186 | -1,-1,-1
187 | -1,-1,-1
188 | 1,1,1
189 | 1,1,1
190 | 0,0,1
191 | 1,1,1
192 | 1,1,1
193 | 1,1,1
194 | 1,1,1
195 | 1,1,1
196 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_value.mat:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataRecon22_global_value.mat
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_value.score.csv:
--------------------------------------------------------------------------------
1 | 0.11144,3.627e-05,5.151e-05
2 | 3.9127,4.3895,2.8619
3 | 0.54036,0.83375,0.36065
4 | 2.1966,2.2122,1.6435
5 | 2.8024,2.9952,2.8983
6 | 6.312,6.7769,7.1099
7 | 2.1007,1.4797,0.94264
8 | 1.3355,1.7994,1.2216
9 | 1.1914,1.2276,1.3632
10 | 0.86801,0.79657,0.91408
11 | 1.3185,1.3684,1.4783
12 | 0.887,0.79462,0.91379
13 | 1.0276,1.0913,1.2273
14 | 0.81704,0.74768,0.86791
15 | 1.38,1.4299,1.5531
16 | 0.93899,0.83569,0.97391
17 | 0.75223,0.6794,0.92785
18 | 1.576,1.9149,2.1284
19 | 0.56656,1.1093,1.0123
20 | 0.56656,1.1093,1.0123
21 | 0.58427,1.5257,0.59579
22 | 0.48427,0.51828,0.64998
23 | 0.0072247,0.13122,0.083741
24 | 1.5249,1.0981,1.6036
25 | 1.5249,1.0981,1.6036
26 | 4.0695,4.0316,4.1325
27 | 4.1849,4.1467,4.2377
28 | 3.3281,2.8467,2.5053
29 | 3.89,3.7721,3.8644
30 | 1.8145,2.4052,1.6658
31 | 3.9955,1.7377,0.37142
32 | 5.7134,6.1188,6.3949
33 | 1.4542,1.0745,1.3254
34 | 3.4961,2.2753,2.4048
35 | 1.6338,1.0851,1.4587
36 | 2.0556,0.92866,1.2754
37 | 2.0356,1.7109,1.0352
38 | 1.8329,0.64862,0.7788
39 | 5.2499,7.285,6.6352
40 | 2.3837,2.2636,1.7497
41 | 3.9141,2.1758,2.0667
42 | 0.99775,1.7931,1.1665
43 | 3.6142,2.1274,2.0739
44 | 0.58143,0.78689,0.88323
45 | 1.468,1.6979,1.3094
46 | 1.3948,1.4036,1.2382
47 | 2.9292,2.1908,2.0402
48 | 2.1367,1.7494,1.5352
49 | 2.7297,2.0277,1.8757
50 | 0.72787,0.78436,0.52609
51 | 3.6142,2.1274,2.0739
52 | 4.0333,4.6835,4.3125
53 | 3.7939,4.3872,4.067
54 | 0.9652,1.0442,0.69571
55 | 1.1254,1.245,0.69068
56 | 3.6032,3.6577,2.3574
57 | 0.90853,0.74236,1.2186
58 | 0.08753,0.063659,0.11452
59 | 1.1023,1.1411,1.0296
60 | 3.0895,3.1314,2.4527
61 | 4.4036,4.2909,4.2852
62 | 2.1075,1.9632,1.8425
63 | 1.1629,0.94966,0.98082
64 | 2.9673,3.1357,2.9018
65 | 2.1861,2.1126,2.0889
66 | 1.6503,2.014,1.454
67 | 0.4203,0.18192,0.3554
68 | 1.764,1.3299,1.145
69 | 0.90326,1.4141,0.77407
70 | 3.7827,3.504,3.4867
71 | 1.9386,1.8519,1.8096
72 | 4.2963,4.2859,4.2778
73 | 2.1608,2.1137,1.9562
74 | 3.7453,3.0754,2.3524
75 | 0.011194,0.090359,0.0035613
76 | 0.86745,1.0266,1.781
77 | 3.5798,3.6334,3.3987
78 | 2.0697,1.9489,1.8412
79 | 0.88581,0.64823,1.2146
80 | 0.0846,0.74869,0.15704
81 | 0.98509,1.1142,0.99161
82 | 1.1943,1.1142,0.84435
83 | -1,-1,-1
84 | 4.7826,1.3873,0.26807
85 | 4.6409,4.6152,4.6106
86 | 1.9494,1.8587,1.8291
87 | 16.663,7.9881,11.999
88 | 0.43223,0.34253,0.81645
89 | 0.38895,0.66775,0.34401
90 | 5.5657,4.8747,5.179
91 | 1.9286,2.0165,1.8639
92 | 1.778,2.2936,1.6313
93 | 1.233,1.7356,1.2786
94 | 3.6992,4.1145,3.7446
95 | 1.1799,1.2112,1.2272
96 | 1.0203,0.32917,0.49232
97 | 1.9808,1.9107,1.8427
98 | 0.052767,0.2871,0.107
99 | 0.44444,0.41776,0.98882
100 | 1.4297,1.5032,1.4518
101 | 0.095823,0.33625,0.07114
102 | 1.9977,1.9254,1.8052
103 | 2.0185,1.9683,1.7707
104 | 1.038,1.7018,0.68585
105 | 1.8668,1.7952,1.6811
106 | 0.52366,0.39382,0.16363
107 | 0.42555,0.62198,0.23754
108 | 1.7027,1.6917,1.7068
109 | 1.1962,1.7924,2.0623
110 | 2.0109,1.9521,1.8913
111 | 1.6955,2.9753,2.0412
112 | 1.9652,1.9083,2.6962
113 | 3.9671,4.4931,4.0699
114 | 1.1888,1.0962,1.0979
115 | 2.0104,1.9353,1.7881
116 | 0.29571,0.34101,0.049073
117 | 0.30714,0.25687,0.093697
118 | 0.0076674,0.17611,0.00043263
119 | 4.0601,3.98,4.0458
120 | 2.1062,2.0565,2.0005
121 | 1.2438,1.8258,1.7337
122 | 2.0256,1.9494,1.8552
123 | 1.8227,1.7704,1.6831
124 | 0.12104,0.024627,0.8407
125 | 0.32121,0.16994,0.54092
126 | 0.41438,0.21276,0.75833
127 | 0.13264,0.084182,0.74449
128 | 0.37342,0.24369,0.68208
129 | 0.013439,0.21193,0.040959
130 | 0.92924,1.1728,0.25357
131 | 2.0777,1.9784,1.8445
132 | 0.12884,3.6589,0.14597
133 | 0.0048709,0.20912,0.017001
134 | 1.3091,1.0778,0.4619
135 | 1.9903,1.9632,1.8422
136 | 1.1644,1.7711,0.5887
137 | 4.317,4.9764,4.6315
138 | 0.82427,1.439,0.76176
139 | 4.0333,4.6835,4.3125
140 | 0.48637,1.4886,0.89917
141 | 0.73507,1.0497,0.81551
142 | 6.0199,0.2441,0.091684
143 | 1.7137,1.5073,1.2559
144 | 1.6956,1.4919,1.2192
145 | 1.7054,1.4977,1.2267
146 | 1.6047,1.3636,1.011
147 | 1.6023,1.4547,1.0736
148 | 1.6591,1.2454,0.9696
149 | 1.5507,1.4307,1.3017
150 | 1.5562,1.424,1.2233
151 | 2.1455,2.3555,2.2073
152 | 0.65952,0.72687,0.47925
153 | 1.6868,1.7395,1.546
154 | 0.61123,0.55887,0.33087
155 | 2.0627,2.269,1.99
156 | 0.82171,1.0436,0.65444
157 | 2.0627,2.269,1.99
158 | 0.67435,0.77396,0.47424
159 | 0.72763,0.84239,0.63944
160 | 0.93094,1.0956,0.68819
161 | 0.24025,0.54775,0.21932
162 | 0.95124,1.0809,0.69492
163 | 2.2392,2.3003,2.2709
164 | 0.6124,0.58692,0.33053
165 | 3.2959,3.68,3.3594
166 | 2.4626,2.6041,2.4222
167 | 0.25587,0.22401,0.21183
168 | 0.78193,0.79298,0.81846
169 | 0.23431,0.19398,0.15634
170 | 0.23431,0.19398,0.15634
171 | 0.80817,0.66573,1.4453
172 | 1.4222,1.2457,1.0054
173 | 1.0605,0.95611,0.61137
174 | 1.008,0.90657,0.58376
175 | 3.5055,2.182,4.7583
176 | 1.2237,1.5977,1.5479
177 | 0.63533,0.83726,0.68956
178 | 0.57956,1.0745,0.60893
179 | 0.29593,0.086785,0.28258
180 | 1.791,0.91599,0.93366
181 | 4.5718,2.6171,3.0268
182 | 1.2567,1.8218,0.99765
183 | 0.74613,0.78633,0.93306
184 | 0.17449,0.26675,0.21607
185 | 0.89791,0.88487,1.4544
186 | -1,-1,-1
187 | -1,-1,-1
188 | 0.69104,0.71827,1.098
189 | 1.261,1.1652,1.591
190 | 0.81775,0.90595,1.1775
191 | 2.425,2.7179,2.7771
192 | 0.15355,0.29166,0.14292
193 | 0.15355,0.29166,0.14292
194 | 0.14858,0.18757,0.18179
195 | 0.10183,0.097685,0.092369
196 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_value.score_binary.csv:
--------------------------------------------------------------------------------
1 | 0,0,0
2 | 1,1,0
3 | 0,0,0
4 | 1,1,0
5 | 1,1,1
6 | 1,1,1
7 | 1,1,1
8 | 1,1,1
9 | 0,0,0
10 | 0,0,0
11 | 0,0,0
12 | 0,0,0
13 | 0,0,0
14 | 0,0,0
15 | 0,0,0
16 | 0,0,0
17 | 0,0,0
18 | 0,1,1
19 | 0,0,0
20 | 0,0,0
21 | 0,1,0
22 | 0,0,0
23 | 0,0,0
24 | 0,0,0
25 | 0,0,0
26 | 1,1,1
27 | 1,1,1
28 | 1,1,1
29 | 1,1,1
30 | 0,0,0
31 | 1,1,0
32 | 1,1,1
33 | 0,0,0
34 | 1,1,1
35 | 1,0,1
36 | 0,0,0
37 | 0,0,0
38 | 1,0,0
39 | 1,1,1
40 | 1,1,1
41 | 1,1,1
42 | 0,0,0
43 | 1,1,1
44 | 0,0,0
45 | 1,1,1
46 | 1,1,1
47 | 1,0,1
48 | 1,0,0
49 | 1,0,0
50 | 1,0,0
51 | 1,1,1
52 | 1,1,1
53 | 1,1,1
54 | 1,1,1
55 | 0,0,0
56 | 1,1,0
57 | 0,0,1
58 | 0,0,0
59 | 1,1,1
60 | 1,1,1
61 | 1,1,1
62 | 1,1,0
63 | 0,0,0
64 | 1,1,1
65 | 1,0,0
66 | 0,0,0
67 | 0,0,0
68 | 0,0,0
69 | 0,0,0
70 | 1,1,1
71 | 1,0,0
72 | 1,1,1
73 | 1,1,0
74 | 1,0,0
75 | 0,0,0
76 | 0,0,0
77 | 1,1,1
78 | 1,1,0
79 | 1,0,1
80 | 0,0,0
81 | 0,0,0
82 | 0,0,0
83 | -1,-1,-1
84 | 1,0,0
85 | 1,1,1
86 | 1,0,0
87 | 1,1,1
88 | 0,0,0
89 | 0,0,0
90 | 1,1,1
91 | 1,0,0
92 | 1,1,0
93 | 0,0,0
94 | 1,1,1
95 | 1,0,0
96 | 0,0,0
97 | 1,0,0
98 | 0,0,0
99 | 0,0,0
100 | 0,0,0
101 | 0,0,0
102 | 1,0,0
103 | 1,1,0
104 | 0,0,0
105 | 1,0,0
106 | 0,0,0
107 | 0,0,0
108 | 1,0,0
109 | 0,1,1
110 | 1,1,0
111 | 0,0,0
112 | 0,0,1
113 | 1,1,1
114 | 1,0,0
115 | 1,0,0
116 | 0,0,0
117 | 0,0,0
118 | 0,0,0
119 | 1,1,1
120 | 1,0,0
121 | 0,0,0
122 | 1,0,0
123 | 0,0,0
124 | 0,0,0
125 | 0,0,0
126 | 0,0,0
127 | 0,0,0
128 | 0,0,0
129 | 0,0,0
130 | 0,0,0
131 | 1,0,0
132 | 0,1,0
133 | 0,0,0
134 | 0,0,0
135 | 1,0,0
136 | 0,0,0
137 | 1,1,1
138 | 0,1,0
139 | 1,1,1
140 | 0,0,0
141 | 0,0,0
142 | 1,0,0
143 | 1,1,0
144 | 1,1,0
145 | 1,1,0
146 | 1,1,0
147 | 1,1,0
148 | 1,0,0
149 | 1,0,0
150 | 1,0,0
151 | 1,1,1
152 | 1,1,0
153 | 1,1,1
154 | 1,0,0
155 | 1,1,1
156 | 0,1,0
157 | 1,1,1
158 | 1,1,0
159 | 1,0,0
160 | 1,1,0
161 | 1,1,1
162 | 1,0,0
163 | 1,1,1
164 | 1,0,0
165 | 1,1,1
166 | 1,1,1
167 | 0,0,0
168 | 0,0,0
169 | 0,0,0
170 | 0,0,0
171 | 0,0,0
172 | 1,0,0
173 | 1,0,0
174 | 1,0,0
175 | 1,0,1
176 | 0,0,0
177 | 0,0,0
178 | 0,0,0
179 | 0,0,0
180 | 1,0,0
181 | 1,0,0
182 | 0,0,0
183 | 0,0,0
184 | 0,0,0
185 | 0,0,0
186 | -1,-1,-1
187 | -1,-1,-1
188 | 0,0,0
189 | 0,0,0
190 | 0,0,0
191 | 0,0,0
192 | 0,0,0
193 | 0,0,0
194 | 0,0,0
195 | 0,0,0
196 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_global_value.taskInfo.csv:
--------------------------------------------------------------------------------
1 | taskInfos1,taskInfos2,taskInfos3,taskInfos4
2 | 1,"Oxidative phosphorylation via NADH-coenzyme Q oxidoreductase (COMPLEX I)",ENERGY METABOLISM,OXYDATIVE PHOSPHORYLATION
3 | 2,"Oxidative phosphorylation via succinate-coenzyme Q oxidoreductase (COMPLEX II)",ENERGY METABOLISM,OXYDATIVE PHOSPHORYLATION
4 | 3,"Krebs cycle - oxidative decarboxylation of pyruvate",ENERGY METABOLISM,KREBS CYCLE
5 | 4,"Krebs cycle - NADH generation",ENERGY METABOLISM,KREBS CYCLE
6 | 5,"ATP regeneration from glucose (normoxic conditions) - glycolysis + krebs cycle",ENERGY METABOLISM,ATP GENERATION
7 | 6,"ATP generation from glucose (hypoxic conditions) - glycolysis",ENERGY METABOLISM,ATP GENERATION
8 | 7,"Reactive oxygen species detoxification (H2O2 to H2O)",ENERGY METABOLISM,OXIDATIVE PHOSPHORYLATION & ROS DETOXIFICATION
9 | 8,"Presence of the thioredoxin system through the thioredoxin reductase activity",ENERGY METABOLISM,REDOX METABOLISM
10 | 9,"Inosine monophosphate synthesis (IMP)",NUCLEOTIDE METABOLISM,IMP SYNTHESIS / PURINE METABOLISM
11 | 10,"Cytidine triphosphate synthesis (CTP)",NUCLEOTIDE METABOLISM,UMP SYNTHESIS
12 | 11,"Guanosine triphosphate synthesis (GTP)",NUCLEOTIDE METABOLISM,IMP SYNTHESIS / PURINE METABOLISM
13 | 12,"Uridine triphosphate synthesis (UTP)",NUCLEOTIDE METABOLISM,UMP SYNTHESIS
14 | 13,"Deoxyadenosine triphosphate synthesis (dATP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
15 | 14,"Deoxycytidine triphosphate synthesis (dCTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
16 | 15,"Deoxyguanosine triphosphate synthesis (dGTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
17 | 16,"Deoxyuridine triphosphate synthesis (dUTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
18 | 17,"Deoxythymidine triphosphate synthesis (dTTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
19 | 18,"AMP salvage from adenine",NUCLEOTIDE METABOLISM,SALVAGE
20 | 19,"IMP salvage from hypoxanthine",NUCLEOTIDE METABOLISM,SALVAGE
21 | 20,"GMP salvage from guanine",NUCLEOTIDE METABOLISM,SALVAGE
22 | 21,"3'-Phospho-5'-adenylyl sulfate synthesis",NUCLEOTIDE METABOLISM,COFACTOR
23 | 22,"Degradation of adenine to urate",NUCLEOTIDE METABOLISM,PURINE CATABOLISM
24 | 23,"Degradation of guanine to urate",NUCLEOTIDE METABOLISM,PURINE CATABOLISM
25 | 24,"Degradation of cytosine",NUCLEOTIDE METABOLISM,PYRIMIDINE CATABOLISM
26 | 25,"Degradation of uracil",NUCLEOTIDE METABOLISM,PYRIMIDINE CATABOLISM
27 | 26,"Gluconeogenesis from pyruvate",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
28 | 27,"Gluconeogenesis from Lactate",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
29 | 28,"Gluconeogenesis from Glycerol",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
30 | 29,"Gluconeogenesis from Alanine",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
31 | 30,"Gluconeogenesis from Glutamine",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
32 | 31,"Ethanol to acetaldehyde",CARBOHYDRATES METABOLISM,GLYCOLYSIS/GLUCONEOGENESIS
33 | 32,"Glucose to lactate conversion",CARBOHYDRATES METABOLISM,PYRUVATE METABOLISM
34 | 33,"Malate to pyruvate conversion",CARBOHYDRATES METABOLISM,PYRUVATE METABOLISM
35 | 34,"Synthesis of fructose-6-phosphate from erythrose-4-phosphate (HMP shunt)",CARBOHYDRATES METABOLISM,PENTOSE PHOSPHATE PATHWAY
36 | 35,"Synthesis of ribose-5-phosphate",CARBOHYDRATES METABOLISM,PENTOSE PHOSPHATE PATHWAY
37 | 36,"Synthesis of lactose",CARBOHYDRATES METABOLISM,GALACTOSE METABOLISM
38 | 37,"Glycogen biosynthesis",CARBOHYDRATES METABOLISM,GLYCOGEN METABOLISM
39 | 38,"Glycogen degradation",CARBOHYDRATES METABOLISM,GLYCOGEN METABOLISM
40 | 39,"Fructose degradation (to glucose-3-phosphate)",CARBOHYDRATES METABOLISM,FRUCTOSE METABOLISM
41 | 40,"Fructose to glucose conversion (via fructose-6-phosphate)",CARBOHYDRATES METABOLISM,FRUCTOSE METABOLISM
42 | 41,"UDP-glucose synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
43 | 42,"UDP-galactose synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
44 | 43,"UDP-glucuronate synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
45 | 44,"GDP-L-fucose synthesis",CARBOHYDRATES METABOLISM,FUCOSE METABOLISM
46 | 45,"Mannose degradation (to fructose-6-phosphate)",CARBOHYDRATES METABOLISM,MANNOSE METABOLISM
47 | 46,"GDP-mannose synthesis",CARBOHYDRATES METABOLISM,MANNOSE METABOLISM
48 | 47,"UDP-N-acetyl D-galactosamine synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
49 | 48,"CMP-N-acetylneuraminate synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
50 | 49,"N-Acetylglucosamine synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
51 | 50,"Glucuronate synthesis (via inositol)",CARBOHYDRATES METABOLISM,PENTOSE AND GLUCURONATE INTERCONVERSION
52 | 51,"Glucuronate synthesis (via udp-glucose)",CARBOHYDRATES METABOLISM,PENTOSE AND GLUCURONATE INTERCONVERSION
53 | 52,"Synthesis of acetone",CARBOHYDRATES METABOLISM,KETOGENESIS
54 | 53,"(R)-3-Hydroxybutanoate synthesis",CARBOHYDRATES METABOLISM,KETOGENESIS
55 | 54,"Synthesis of inositol",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
56 | 55,"Inositol as input for glucuronate-xylulose pathway",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
57 | 56,"Synthesis of phosphatidylinositol from inositol",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
58 | 57,"Conversion of 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate to 1D-myo-inositol 1,4,5-trisphosphate",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
59 | 58,"Starch degradation",CARBOHYDRATES METABOLISM,STARCH DEGRADATION
60 | 59,"Link between glyoxylate metabolism and pentose phosphate pathway (Xylulose to glycolate)",CARBOHYDRATES METABOLISM,GLYOXYLATE METABOLISM
61 | 60,"Synthesis of methylglyoxal",CARBOHYDRATES METABOLISM,METHYLGLYOXAL METABOLISM
62 | 61,"Alanine synthesis",AMINO ACIDS METABOLISM,ALANINE METABOLISM
63 | 62,"Alanine degradation",AMINO ACIDS METABOLISM,ALANINE METABOLISM
64 | 63,"Synthesis of alanine from glutamine",AMINO ACIDS METABOLISM,ALANINE METABOLISM
65 | 64,"Arginine synthesis",AMINO ACIDS METABOLISM,ARGININE METABOLISM
66 | 65,"Arginine degradation",AMINO ACIDS METABOLISM,ARGININE METABOLISM
67 | 66,"Synthesis of arginine from glutamine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
68 | 67,"Synthesis of nitric oxide from arginine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
69 | 68,"Synthesis of aspartate from glutamine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
70 | 69,"Synthesis of creatine from arginine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
71 | 70,"Asparagine synthesis",AMINO ACIDS METABOLISM,ASPARAGINE METABOLISM
72 | 71,"Asparagine degradation",AMINO ACIDS METABOLISM,ASPARAGINE METABOLISM
73 | 72,"Aspartate synthesis",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
74 | 73,"Aspartate degradation",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
75 | 74,"Conversion of aspartate to arginine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
76 | 75,"Conversion of aspartate to beta-alanine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
77 | 76,"Conversion of asparate to asparagine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
78 | 77,"beta-Alanine synthesis",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
79 | 78,"beta-Alanine degradation",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
80 | 79,"Conversion of carnosine to beta-alanine",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
81 | 80,"Beta-alanine to 3-oxopropanoate",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
82 | 81,"Cysteine synthesis (need serine and methionine)",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
83 | 82,"Cysteine degradation",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
84 | 83,"Synthesis of cysteine from cystine",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
85 | 84,"Synthesis of taurine from cysteine",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
86 | 85,"Glutamate synthesis",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
87 | 86,"Glutamate degradation",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
88 | 87,"Conversion of glutamate to glutamine",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
89 | 88,"Conversion of glutamate to proline",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
90 | 89,"Conversion of GABA into succinate",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
91 | 90,"Glutamine synthesis",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
92 | 91,"Glutamine degradation",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
93 | 92,"Glutaminolysis (glutamine to lactate)",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
94 | 93,"Glutathionate synthesis",AMINO ACIDS METABOLISM,GLUTATHIONE METABOLISM
95 | 94,"Glycine synthesis",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
96 | 95,"Glycine degradation",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
97 | 96,"Conversion of glycine to pyruvate",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
98 | 97,"Histidine degradation",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
99 | 98,"Conversion of histidine to glutamate",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
100 | 99,"Conversion of histidine to histamine",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
101 | 100,"Homocysteine synthesis (need methionine)",AMINO ACIDS METABOLISM,HOMOCYSTEINE METABOLISM
102 | 101,"Homocysteine degradation",AMINO ACIDS METABOLISM,HOMOCYSTEINE METABOLISM
103 | 102,"Isoleucine degradation",AMINO ACIDS METABOLISM,ISOLEUCINE METABOLISM
104 | 103,"Leucine degradation",AMINO ACIDS METABOLISM,LEUCINE METABOLISM
105 | 104,"Conversion of leucine to acetyl-coA",AMINO ACIDS METABOLISM,LEUCINE METABOLISM
106 | 105,"Lysine degradation",AMINO ACIDS METABOLISM,LYSINE METABOLISM
107 | 106,"Conversion of lysine to L-Saccharopine",AMINO ACIDS METABOLISM,LYSINE METABOLISM
108 | 107,"Conversion of lysine to L-2-Aminoadipate",AMINO ACIDS METABOLISM,LYSINE METABOLISM
109 | 108,"Methionine degradation",AMINO ACIDS METABOLISM,METHIONINE METABOLISM
110 | 109,"S-adenosyl-L-methionine synthesis",AMINO ACIDS METABOLISM,METHIONINE METABOLISM
111 | 110,"Ornithine degradation",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
112 | 111,"Synthesis of ornithine from glutamine",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
113 | 112,"Synthesis of spermidine from ornithine",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
114 | 113,"Serine synthesis",AMINO ACIDS METABOLISM,SERINE METABOLISM
115 | 114,"Serine degradation",AMINO ACIDS METABOLISM,SERINE METABOLISM
116 | 115,"Phenylalanine degradation",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
117 | 116,"Phenylalanine to phenylacetaldehyde",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
118 | 117,"Phenylalanine to phenylacetyl-L-glutaminate",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
119 | 118,"Phenylalanine to tyrosine",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
120 | 119,"Proline synthesis",AMINO ACIDS METABOLISM,PROLINE METABOLISM
121 | 120,"Proline degradation",AMINO ACIDS METABOLISM,PROLINE METABOLISM
122 | 121,"Synthesis of proline from glutamine",AMINO ACIDS METABOLISM,PROLINE METABOLISM
123 | 122,"Threonine degradation",AMINO ACIDS METABOLISM,THREONINE METABOLISM
124 | 123,"Tryptophan degradation",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
125 | 124,"Synthesis of anthranilate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
126 | 125,"Synthesis of kynate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
127 | 126,"Synthesis of L-kynurenine from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
128 | 127,"Synthesis of N-formylanthranilate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
129 | 128,"Synthesis of quinolinate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
130 | 129,"Synthesis of serotonin from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
131 | 130,"Tyrosine synthesis (need phenylalanine)",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
132 | 131,"Tyrosine degradation",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
133 | 132,"Tyrosine to adrenaline",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
134 | 133,"Tyrosine to dopamine",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
135 | 134,"Tyrosine to acetoacetate and fumarate",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
136 | 135,"Valine degradation",AMINO ACIDS METABOLISM,VALINE METABOLISM
137 | 136,"Valine to succinyl-coA",AMINO ACIDS METABOLISM,VALINE METABOLISM
138 | 137,"Hydroxymethylglutaryl-CoA synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
139 | 138,"Cholesterol synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
140 | 139,"Acetoacetate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
141 | 140,"Mevalonate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
142 | 141,"Farnesyl-pyrophosphate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
143 | 142,"Glycerol-3-phosphate synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
144 | 143,"Phosphatidyl-choline synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
145 | 144,"Phosphatidyl-ethanolamine synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
146 | 145,"Phosphatidyl-serine synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
147 | 146,"Phosphatidyl-inositol synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
148 | 147,"Cardiolipin synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
149 | 148,"Triacylglycerol synthesis",LIPIDS METABOLISM,TRIACYLGLYCEROL METABOLISM
150 | 149,"Sphingomyelin synthesis",LIPIDS METABOLISM,SPHINGOLIPID METABOLISM
151 | 150,"Ceramide synthesis",LIPIDS METABOLISM,SPHINGOLIPID METABOLISM
152 | 151,"Palmitate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
153 | 152,"Palmitate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
154 | 153,"Palmitolate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
155 | 154,"Palmitolate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
156 | 155,"cis-vaccenic acid synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
157 | 156,"cis-vaccenic acid degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
158 | 157,"Elaidate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
159 | 158,"Elaidate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
160 | 159,"Linolenate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
161 | 160,"Linoleate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
162 | 161,"gamma-Linolenate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
163 | 162,"gamma-Linolenate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
164 | 163,"Arachidonate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
165 | 164,"Arachidonate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
166 | 165,"Synthesis of malonyl-coa",LIPIDS METABOLISM,FATTY ACID METABOLISM
167 | 166,"Synthesis of palmitoyl-CoA",LIPIDS METABOLISM,FATTY ACID METABOLISM
168 | 167,"Taurochenodeoxycholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
169 | 168,"Glycochenodeoxycholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
170 | 169,"tauro-cholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
171 | 170,"glyco-cholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
172 | 171,"Synthesis of thromboxane from arachidonate",LIPIDS METABOLISM,EICOSANOID METABOLISM
173 | 172,"Synthesis of galactosyl glucosyl ceramide (link with ganglioside metabolism)",LIPIDS METABOLISM,GANGLIOSIDE METABOLISM
174 | 173,"Synthesis of glucocerebroside",LIPIDS METABOLISM,GANGLIOSIDE METABOLISM
175 | 174,"Synthesis of globoside (link with globoside metabolism)",LIPIDS METABOLISM,GLOBOSIDE METABOLISM
176 | 175,"NAD synthesis from nicotinamide",VITAMIN & COFACTOR METABOLISM,COENZYME NAD BIOSYNTHESIS
177 | 176,"FAD synthesis",VITAMIN & COFACTOR METABOLISM,COENZYME FAD BIOSYNTHESIS
178 | 177,"Synthesis of coenzyme A",VITAMIN & COFACTOR METABOLISM,COENZYME A BIOSYNTHESIS
179 | 178,"Synthesis of ubiquinone from tyrosine",VITAMIN & COFACTOR METABOLISM,UBIQUINONE 10 BIOSYNTHESIS
180 | 179,"Tetrahydrofolate synthesis",VITAMIN & COFACTOR METABOLISM,FOLATE METABOLISM
181 | 180,"Pyridoxal-phosphate synthesis",VITAMIN & COFACTOR METABOLISM,VITAMIN B6 METABOLISM
182 | 181,"Synthesis of bilirubin",VITAMIN & COFACTOR METABOLISM,HEME METABOLISM
183 | 182,"Heme synthesis",VITAMIN & COFACTOR METABOLISM,HEME METABOLISM
184 | 183,"Phosphatidyl-inositol to glucosaminyl-acylphosphatidylinositol",VITAMIN & COFACTOR METABOLISM,GPI ANCHOR BIOSYNTHESIS
185 | 184,"Glucosaminyl-acylphosphatidylinositoll to deacylated-glycophosphatidylinositol (GPI)-anchored protein",GLYCAN METABOLISM,GPI ANCHOR BIOSYNTHESIS
186 | 185,"Degradation of n2m2nmasn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
187 | 186,"Biosynthesis of m4mpdol_U",GLYCAN METABOLISM,N-GLYCAN METABOLISM
188 | 187,"Biosynthesis of g3m8masn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
189 | 188,"Degradation of s2l2fn2m2masn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
190 | 189,"Biosynthesis of core2 (beta-D-Galactosyl-1,3-(N-acetyl-beta-D-glucosaminyl-1,6)-N-acetyl-D-galactosaminyl-R)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
191 | 190,"Biosynthesis of core4 (N-Acetyl-beta-D-glucosaminyl-1,6-(N-acetyl-beta-D-glucosaminyl-1,3)-N-acetyl-D-galactosaminyl-R)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
192 | 191,"Biosynthesis of Tn_antigen (Glycoprotein N-acetyl-D-galactosamine)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
193 | 192,"Keratan sulfate biosynthesis from O-glycan (core 2-linked)",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
194 | 193,"Keratan sulfate biosynthesis from O-glycan (core 4-linked)",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
195 | 194,"Keratan sulfate degradation",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
196 | 195,"Keratan sulfate biosynthesis from N-glycan",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
197 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_local_mean.mat:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataRecon22_local_mean.mat
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/test/suite/dataRecon22_local_mean.score.csv:
--------------------------------------------------------------------------------
1 | 1.8746,0.0048218,0.0068464
2 | 3.233,3.5859,2.477
3 | 1.66,1.624,0.83026
4 | 1.9774,2.0948,1.5775
5 | 1.9198,2.1721,1.8811
6 | 2.6318,3.197,3.1107
7 | 1.2363,0.78654,0.4516
8 | 1.0204,1.1062,0.68701
9 | 2.0124,2.3476,2.5841
10 | 1.7669,1.8446,2.0902
11 | 1.9781,2.2111,2.4181
12 | 1.7778,1.8282,2.0985
13 | 1.8531,2.13,2.3286
14 | 1.6696,1.7332,1.9881
15 | 2.0682,2.3098,2.5375
16 | 1.8871,1.9355,2.2392
17 | 1.5833,1.614,2.1437
18 | 1.4435,1.7627,1.965
19 | 1.1461,2.0572,1.9036
20 | 1.1461,2.0572,1.9036
21 | 0.85525,1.3857,1.0937
22 | 0.5011,0.49689,1.0166
23 | 0.25313,2.8158,1.9344
24 | 2.4241,1.952,2.884
25 | 2.4241,1.952,2.884
26 | 2.4914,2.3842,2.4616
27 | 2.5985,2.5253,2.5897
28 | 2.4004,2.2931,2.2774
29 | 2.5285,2.2824,2.3883
30 | 2.0133,2.5605,1.8188
31 | 1.1552,0.48936,0.84786
32 | 2.4704,2.9589,2.8725
33 | 2.484,3.5245,2.1178
34 | 2.2779,1.7568,2.3002
35 | 2.3303,2.1147,2.5448
36 | 0.21013,3.1039,3.6251
37 | 3.6676,2.2842,1.6707
38 | 2.3279,1.3055,1.4823
39 | 2.6637,4.0486,3.5871
40 | 1.8364,1.7088,1.8455
41 | 2.6702,1.4083,1.7668
42 | 2.4517,3.3549,2.9256
43 | 3.0172,1.8215,2.1299
44 | 3.0842,3.8238,3.9756
45 | 1.7338,2.4143,2.2725
46 | 1.893,2.2276,2.6476
47 | 3.3321,2.7791,2.81
48 | 2.3734,2.1189,2.0086
49 | 3.3945,2.7632,2.8147
50 | 2.3586,2.03,3.1915
51 | 3.0172,1.8215,2.1299
52 | 2.2239,2.9726,2.5231
53 | 2.3109,2.9818,2.6301
54 | 1.7895,2.1668,2.8226
55 | 3.267,2.5178,2.4925
56 | 3.8446,3.9016,2.5353
57 | 2.3105,1.6818,1.9713
58 | 0.9828,0.26054,1.1467
59 | 0.87986,0.88662,0.82824
60 | 1.7702,1.9564,1.9338
61 | 2.6481,2.4483,2.2121
62 | 2.0881,2.1693,1.9288
63 | 1.9226,1.6089,1.6555
64 | 2.4908,2.2296,2.0086
65 | 2.1571,2.1039,2.0423
66 | 2.9237,2.2326,2.3013
67 | 2.1204,1.0965,1.8717
68 | 3.0984,2.7298,2.2592
69 | 3.1514,4.4526,2.5608
70 | 2.3572,2.4021,2.2741
71 | 1.962,2.1346,1.8904
72 | 2.4404,2.4087,2.1288
73 | 2.0032,2.2255,1.9095
74 | 3.9021,3.1571,3.1123
75 | 1.3698,1.5959,0.23853
76 | 2.5712,2.9637,4.6187
77 | 2.2523,2.2832,1.8564
78 | 2.0234,2.1395,1.906
79 | 0.81711,1.9058,1.1299
80 | 0.32811,2.0246,0.57603
81 | 1.8417,2.3809,2.4436
82 | 2.0199,1.7029,1.4958
83 | -1,-1,-1
84 | 6.1647,1.9775,0.40125
85 | 2.6179,2.5723,2.2769
86 | 1.9475,2.0568,1.9105
87 | 5.4557,1.5041,6.0274
88 | 1.5671,1.1004,1.5409
89 | 2.0871,1.436,1.856
90 | 2.8362,2.4902,2.5654
91 | 1.9586,2.1746,1.9458
92 | 1.9449,2.4769,1.8086
93 | 3.1323,3.7746,3.1153
94 | 2.4717,2.817,2.4221
95 | 1.6788,1.587,1.3145
96 | 1.889,0.7521,1.0841
97 | 2.0597,2.1874,2.1445
98 | 1.5382,2.6802,3.1741
99 | 3.1024,0.83685,5.5307
100 | 2.3863,2.6724,3.3624
101 | 0.75231,1.7979,0.60604
102 | 1.9836,2.0694,1.8357
103 | 2.0146,2.1574,1.8622
104 | 1.6743,2.5892,1.1757
105 | 1.8772,1.9495,1.7412
106 | 1.5264,1.2224,0.58309
107 | 1.236,1.4687,1.2187
108 | 1.784,1.884,1.8758
109 | 2.9931,1.8168,1.4382
110 | 1.864,2.0948,1.8549
111 | 1.7312,3.0095,2.1476
112 | 2.8432,2.3516,3.0467
113 | 2.53,3.0235,2.556
114 | 1.7121,1.5122,1.5079
115 | 2.1012,2.2166,1.9992
116 | 0.36181,0.58736,0.20245
117 | 0.38945,0.50339,0.20847
118 | 0.28529,3.3846,0.01697
119 | 2.4635,2.3172,2.0947
120 | 1.9592,2.1493,1.9779
121 | 1.7616,2.2167,1.7955
122 | 1.972,2.2005,1.8709
123 | 1.8964,2.0651,1.9524
124 | 0.31074,0.83204,6.7844
125 | 1.2438,1.2599,3.235
126 | 1.1463,1.0366,4.3476
127 | 0.44374,0.7035,4.3548
128 | 1.7927,1.4779,3.9537
129 | 0.69191,0.70287,1.781
130 | 1.1379,2.8543,0.28679
131 | 2.1567,2.1999,2.0906
132 | 0.2003,3.8871,0.43591
133 | 0.014228,0.91353,0.048139
134 | 2.6622,1.524,2.4046
135 | 1.9509,2.2068,1.9619
136 | 1.6986,2.4446,1.6101
137 | 2.2902,2.7099,2.6313
138 | 1.4113,2.2682,1.5012
139 | 2.2239,2.9726,2.5231
140 | 0.9131,2.4557,1.5895
141 | 1.3357,1.8124,1.4202
142 | 3.44,0.073937,0.027181
143 | 1.7747,1.6961,1.4053
144 | 1.7828,1.7101,1.384
145 | 1.7604,1.6823,1.3688
146 | 1.7207,1.5992,1.2553
147 | 1.7253,1.6148,1.2774
148 | 1.7646,1.5041,1.1799
149 | 1.8388,1.8032,1.5563
150 | 1.7847,1.7155,1.4506
151 | 1.5341,1.897,1.6657
152 | 0.65821,0.85381,0.57403
153 | 1.2948,1.4486,1.2081
154 | 0.75334,0.85461,0.62645
155 | 1.5107,1.8443,1.5435
156 | 1.1695,1.4174,1.0144
157 | 1.5107,1.8443,1.5435
158 | 0.65539,0.84658,0.54895
159 | 0.70318,0.92045,0.60751
160 | 1.1928,1.4912,0.99674
161 | 0.49864,0.9952,0.46954
162 | 1.2099,1.4497,0.99314
163 | 1.4735,1.8164,1.4632
164 | 0.74275,0.85233,0.60991
165 | 2.2992,2.8022,2.4253
166 | 2.0934,2.1725,1.9938
167 | 0.69636,0.63034,0.43077
168 | 0.97314,1.0214,0.76539
169 | 0.49649,0.56259,0.22805
170 | 0.49649,0.56259,0.22805
171 | 1.6672,1.397,2.8385
172 | 1.78,1.6084,1.3351
173 | 1.5463,1.379,0.96961
174 | 1.5477,1.3835,0.98455
175 | 2.7939,1.3297,2.8877
176 | 2.9991,3.6488,3.6333
177 | 1.0313,1.3697,1.1628
178 | 2.1191,3.0601,2.14
179 | 1.0691,0.38796,1.0324
180 | 1.6528,0.82856,0.84494
181 | 4.7216,3.1375,3.4641
182 | 3.1984,3.2099,2.7515
183 | 2.8678,3.279,3.3485
184 | 0.72055,0.71335,0.70889
185 | 1.6911,1.8484,2.4503
186 | -1,-1,-1
187 | -1,-1,-1
188 | 1.1889,1.3518,1.7466
189 | 3.6902,3.4274,4.7013
190 | 2.2478,1.939,5.5821
191 | 6.2587,5.8171,6.435
192 | 0.33333,0.6701,0.28937
193 | 0.33333,0.6701,0.28937
194 | 0.1773,0.21231,0.24055
195 | 0.27072,0.39493,0.28079
196 |
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/test/suite/dataRecon22_local_mean.score_binary.csv:
--------------------------------------------------------------------------------
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62 | 1,1,0
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66 | 1,0,0
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80 | 0,1,0
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83 | -1,-1,-1
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86 | 1,1,0
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89 | 0,1,0
90 | 1,0,1
91 | 1,1,0
92 | 1,1,0
93 | 0,1,0
94 | 1,1,1
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96 | 1,0,0
97 | 1,1,0
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100 | 1,1,1
101 | 0,1,0
102 | 1,1,0
103 | 1,1,0
104 | 0,1,0
105 | 1,1,0
106 | 1,1,0
107 | 0,1,0
108 | 1,1,0
109 | 1,1,0
110 | 1,1,0
111 | 0,1,1
112 | 1,0,1
113 | 1,1,1
114 | 1,0,0
115 | 1,1,0
116 | 0,1,1
117 | 1,1,1
118 | 0,1,0
119 | 1,0,0
120 | 1,0,0
121 | 1,0,0
122 | 1,1,0
123 | 1,1,0
124 | 0,0,1
125 | 0,0,1
126 | 0,0,1
127 | 0,0,1
128 | 0,0,1
129 | 0,1,1
130 | 0,1,0
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132 | 0,1,0
133 | 0,1,0
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139 | 0,1,1
140 | 0,1,0
141 | 0,1,0
142 | 1,0,0
143 | 1,0,0
144 | 1,0,0
145 | 1,0,0
146 | 1,1,0
147 | 1,1,0
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149 | 1,0,0
150 | 1,0,0
151 | 1,0,0
152 | 1,1,0
153 | 1,0,0
154 | 1,1,0
155 | 1,0,0
156 | 1,1,0
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159 | 1,1,0
160 | 1,1,0
161 | 1,1,1
162 | 1,1,0
163 | 1,1,1
164 | 1,1,0
165 | 1,1,1
166 | 1,0,0
167 | 1,1,0
168 | 1,1,0
169 | 1,1,0
170 | 1,1,0
171 | 0,0,1
172 | 1,0,0
173 | 1,0,0
174 | 1,0,0
175 | 1,0,1
176 | 0,1,1
177 | 0,1,1
178 | 0,1,0
179 | 1,0,1
180 | 1,0,0
181 | 1,0,0
182 | 1,1,1
183 | 0,0,0
184 | 0,0,0
185 | 0,0,1
186 | -1,-1,-1
187 | -1,-1,-1
188 | 0,0,1
189 | 1,0,1
190 | 0,0,1
191 | 1,1,1
192 | 1,1,1
193 | 1,1,1
194 | 0,1,0
195 | 1,1,1
196 |
--------------------------------------------------------------------------------
/test/suite/dataRecon22_local_mean.taskInfo.csv:
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1 | taskInfos1,taskInfos2,taskInfos3,taskInfos4
2 | 1,"Oxidative phosphorylation via NADH-coenzyme Q oxidoreductase (COMPLEX I)",ENERGY METABOLISM,OXYDATIVE PHOSPHORYLATION
3 | 2,"Oxidative phosphorylation via succinate-coenzyme Q oxidoreductase (COMPLEX II)",ENERGY METABOLISM,OXYDATIVE PHOSPHORYLATION
4 | 3,"Krebs cycle - oxidative decarboxylation of pyruvate",ENERGY METABOLISM,KREBS CYCLE
5 | 4,"Krebs cycle - NADH generation",ENERGY METABOLISM,KREBS CYCLE
6 | 5,"ATP regeneration from glucose (normoxic conditions) - glycolysis + krebs cycle",ENERGY METABOLISM,ATP GENERATION
7 | 6,"ATP generation from glucose (hypoxic conditions) - glycolysis",ENERGY METABOLISM,ATP GENERATION
8 | 7,"Reactive oxygen species detoxification (H2O2 to H2O)",ENERGY METABOLISM,OXIDATIVE PHOSPHORYLATION & ROS DETOXIFICATION
9 | 8,"Presence of the thioredoxin system through the thioredoxin reductase activity",ENERGY METABOLISM,REDOX METABOLISM
10 | 9,"Inosine monophosphate synthesis (IMP)",NUCLEOTIDE METABOLISM,IMP SYNTHESIS / PURINE METABOLISM
11 | 10,"Cytidine triphosphate synthesis (CTP)",NUCLEOTIDE METABOLISM,UMP SYNTHESIS
12 | 11,"Guanosine triphosphate synthesis (GTP)",NUCLEOTIDE METABOLISM,IMP SYNTHESIS / PURINE METABOLISM
13 | 12,"Uridine triphosphate synthesis (UTP)",NUCLEOTIDE METABOLISM,UMP SYNTHESIS
14 | 13,"Deoxyadenosine triphosphate synthesis (dATP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
15 | 14,"Deoxycytidine triphosphate synthesis (dCTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
16 | 15,"Deoxyguanosine triphosphate synthesis (dGTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
17 | 16,"Deoxyuridine triphosphate synthesis (dUTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
18 | 17,"Deoxythymidine triphosphate synthesis (dTTP)",NUCLEOTIDE METABOLISM,DEOXYNUCLEOTIDES SYNTHESIS
19 | 18,"AMP salvage from adenine",NUCLEOTIDE METABOLISM,SALVAGE
20 | 19,"IMP salvage from hypoxanthine",NUCLEOTIDE METABOLISM,SALVAGE
21 | 20,"GMP salvage from guanine",NUCLEOTIDE METABOLISM,SALVAGE
22 | 21,"3'-Phospho-5'-adenylyl sulfate synthesis",NUCLEOTIDE METABOLISM,COFACTOR
23 | 22,"Degradation of adenine to urate",NUCLEOTIDE METABOLISM,PURINE CATABOLISM
24 | 23,"Degradation of guanine to urate",NUCLEOTIDE METABOLISM,PURINE CATABOLISM
25 | 24,"Degradation of cytosine",NUCLEOTIDE METABOLISM,PYRIMIDINE CATABOLISM
26 | 25,"Degradation of uracil",NUCLEOTIDE METABOLISM,PYRIMIDINE CATABOLISM
27 | 26,"Gluconeogenesis from pyruvate",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
28 | 27,"Gluconeogenesis from Lactate",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
29 | 28,"Gluconeogenesis from Glycerol",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
30 | 29,"Gluconeogenesis from Alanine",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
31 | 30,"Gluconeogenesis from Glutamine",CARBOHYDRATES METABOLISM,GLUCONEOGENESIS
32 | 31,"Ethanol to acetaldehyde",CARBOHYDRATES METABOLISM,GLYCOLYSIS/GLUCONEOGENESIS
33 | 32,"Glucose to lactate conversion",CARBOHYDRATES METABOLISM,PYRUVATE METABOLISM
34 | 33,"Malate to pyruvate conversion",CARBOHYDRATES METABOLISM,PYRUVATE METABOLISM
35 | 34,"Synthesis of fructose-6-phosphate from erythrose-4-phosphate (HMP shunt)",CARBOHYDRATES METABOLISM,PENTOSE PHOSPHATE PATHWAY
36 | 35,"Synthesis of ribose-5-phosphate",CARBOHYDRATES METABOLISM,PENTOSE PHOSPHATE PATHWAY
37 | 36,"Synthesis of lactose",CARBOHYDRATES METABOLISM,GALACTOSE METABOLISM
38 | 37,"Glycogen biosynthesis",CARBOHYDRATES METABOLISM,GLYCOGEN METABOLISM
39 | 38,"Glycogen degradation",CARBOHYDRATES METABOLISM,GLYCOGEN METABOLISM
40 | 39,"Fructose degradation (to glucose-3-phosphate)",CARBOHYDRATES METABOLISM,FRUCTOSE METABOLISM
41 | 40,"Fructose to glucose conversion (via fructose-6-phosphate)",CARBOHYDRATES METABOLISM,FRUCTOSE METABOLISM
42 | 41,"UDP-glucose synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
43 | 42,"UDP-galactose synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
44 | 43,"UDP-glucuronate synthesis",CARBOHYDRATES METABOLISM,NUCLEOTIDE SUGAR
45 | 44,"GDP-L-fucose synthesis",CARBOHYDRATES METABOLISM,FUCOSE METABOLISM
46 | 45,"Mannose degradation (to fructose-6-phosphate)",CARBOHYDRATES METABOLISM,MANNOSE METABOLISM
47 | 46,"GDP-mannose synthesis",CARBOHYDRATES METABOLISM,MANNOSE METABOLISM
48 | 47,"UDP-N-acetyl D-galactosamine synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
49 | 48,"CMP-N-acetylneuraminate synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
50 | 49,"N-Acetylglucosamine synthesis",CARBOHYDRATES METABOLISM,AMINO SUGARS METABOLISM
51 | 50,"Glucuronate synthesis (via inositol)",CARBOHYDRATES METABOLISM,PENTOSE AND GLUCURONATE INTERCONVERSION
52 | 51,"Glucuronate synthesis (via udp-glucose)",CARBOHYDRATES METABOLISM,PENTOSE AND GLUCURONATE INTERCONVERSION
53 | 52,"Synthesis of acetone",CARBOHYDRATES METABOLISM,KETOGENESIS
54 | 53,"(R)-3-Hydroxybutanoate synthesis",CARBOHYDRATES METABOLISM,KETOGENESIS
55 | 54,"Synthesis of inositol",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
56 | 55,"Inositol as input for glucuronate-xylulose pathway",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
57 | 56,"Synthesis of phosphatidylinositol from inositol",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
58 | 57,"Conversion of 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate to 1D-myo-inositol 1,4,5-trisphosphate",CARBOHYDRATES METABOLISM,PHOSPHATE INOSITOL METABOLISM
59 | 58,"Starch degradation",CARBOHYDRATES METABOLISM,STARCH DEGRADATION
60 | 59,"Link between glyoxylate metabolism and pentose phosphate pathway (Xylulose to glycolate)",CARBOHYDRATES METABOLISM,GLYOXYLATE METABOLISM
61 | 60,"Synthesis of methylglyoxal",CARBOHYDRATES METABOLISM,METHYLGLYOXAL METABOLISM
62 | 61,"Alanine synthesis",AMINO ACIDS METABOLISM,ALANINE METABOLISM
63 | 62,"Alanine degradation",AMINO ACIDS METABOLISM,ALANINE METABOLISM
64 | 63,"Synthesis of alanine from glutamine",AMINO ACIDS METABOLISM,ALANINE METABOLISM
65 | 64,"Arginine synthesis",AMINO ACIDS METABOLISM,ARGININE METABOLISM
66 | 65,"Arginine degradation",AMINO ACIDS METABOLISM,ARGININE METABOLISM
67 | 66,"Synthesis of arginine from glutamine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
68 | 67,"Synthesis of nitric oxide from arginine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
69 | 68,"Synthesis of aspartate from glutamine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
70 | 69,"Synthesis of creatine from arginine",AMINO ACIDS METABOLISM,ARGININE METABOLISM
71 | 70,"Asparagine synthesis",AMINO ACIDS METABOLISM,ASPARAGINE METABOLISM
72 | 71,"Asparagine degradation",AMINO ACIDS METABOLISM,ASPARAGINE METABOLISM
73 | 72,"Aspartate synthesis",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
74 | 73,"Aspartate degradation",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
75 | 74,"Conversion of aspartate to arginine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
76 | 75,"Conversion of aspartate to beta-alanine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
77 | 76,"Conversion of asparate to asparagine",AMINO ACIDS METABOLISM,ASPARTATE METABOLISM
78 | 77,"beta-Alanine synthesis",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
79 | 78,"beta-Alanine degradation",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
80 | 79,"Conversion of carnosine to beta-alanine",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
81 | 80,"Beta-alanine to 3-oxopropanoate",AMINO ACIDS METABOLISM,BETA-ALANINE METABOLISM
82 | 81,"Cysteine synthesis (need serine and methionine)",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
83 | 82,"Cysteine degradation",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
84 | 83,"Synthesis of cysteine from cystine",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
85 | 84,"Synthesis of taurine from cysteine",AMINO ACIDS METABOLISM,CYSTEINE METABOLISM
86 | 85,"Glutamate synthesis",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
87 | 86,"Glutamate degradation",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
88 | 87,"Conversion of glutamate to glutamine",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
89 | 88,"Conversion of glutamate to proline",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
90 | 89,"Conversion of GABA into succinate",AMINO ACIDS METABOLISM,GLUTAMATE METABOLISM
91 | 90,"Glutamine synthesis",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
92 | 91,"Glutamine degradation",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
93 | 92,"Glutaminolysis (glutamine to lactate)",AMINO ACIDS METABOLISM,GLUTAMINE METABOLISM
94 | 93,"Glutathionate synthesis",AMINO ACIDS METABOLISM,GLUTATHIONE METABOLISM
95 | 94,"Glycine synthesis",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
96 | 95,"Glycine degradation",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
97 | 96,"Conversion of glycine to pyruvate",AMINO ACIDS METABOLISM,GLYCINE METABOLISM
98 | 97,"Histidine degradation",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
99 | 98,"Conversion of histidine to glutamate",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
100 | 99,"Conversion of histidine to histamine",AMINO ACIDS METABOLISM,HISTIDINE METABOLISM
101 | 100,"Homocysteine synthesis (need methionine)",AMINO ACIDS METABOLISM,HOMOCYSTEINE METABOLISM
102 | 101,"Homocysteine degradation",AMINO ACIDS METABOLISM,HOMOCYSTEINE METABOLISM
103 | 102,"Isoleucine degradation",AMINO ACIDS METABOLISM,ISOLEUCINE METABOLISM
104 | 103,"Leucine degradation",AMINO ACIDS METABOLISM,LEUCINE METABOLISM
105 | 104,"Conversion of leucine to acetyl-coA",AMINO ACIDS METABOLISM,LEUCINE METABOLISM
106 | 105,"Lysine degradation",AMINO ACIDS METABOLISM,LYSINE METABOLISM
107 | 106,"Conversion of lysine to L-Saccharopine",AMINO ACIDS METABOLISM,LYSINE METABOLISM
108 | 107,"Conversion of lysine to L-2-Aminoadipate",AMINO ACIDS METABOLISM,LYSINE METABOLISM
109 | 108,"Methionine degradation",AMINO ACIDS METABOLISM,METHIONINE METABOLISM
110 | 109,"S-adenosyl-L-methionine synthesis",AMINO ACIDS METABOLISM,METHIONINE METABOLISM
111 | 110,"Ornithine degradation",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
112 | 111,"Synthesis of ornithine from glutamine",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
113 | 112,"Synthesis of spermidine from ornithine",AMINO ACIDS METABOLISM,ORNITHINE METABOLISM
114 | 113,"Serine synthesis",AMINO ACIDS METABOLISM,SERINE METABOLISM
115 | 114,"Serine degradation",AMINO ACIDS METABOLISM,SERINE METABOLISM
116 | 115,"Phenylalanine degradation",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
117 | 116,"Phenylalanine to phenylacetaldehyde",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
118 | 117,"Phenylalanine to phenylacetyl-L-glutaminate",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
119 | 118,"Phenylalanine to tyrosine",AMINO ACIDS METABOLISM,PHENYLALANINE METABOLISM
120 | 119,"Proline synthesis",AMINO ACIDS METABOLISM,PROLINE METABOLISM
121 | 120,"Proline degradation",AMINO ACIDS METABOLISM,PROLINE METABOLISM
122 | 121,"Synthesis of proline from glutamine",AMINO ACIDS METABOLISM,PROLINE METABOLISM
123 | 122,"Threonine degradation",AMINO ACIDS METABOLISM,THREONINE METABOLISM
124 | 123,"Tryptophan degradation",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
125 | 124,"Synthesis of anthranilate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
126 | 125,"Synthesis of kynate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
127 | 126,"Synthesis of L-kynurenine from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
128 | 127,"Synthesis of N-formylanthranilate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
129 | 128,"Synthesis of quinolinate from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
130 | 129,"Synthesis of serotonin from tryptophan",AMINO ACIDS METABOLISM,TRYPTOPHAN METABOLISM
131 | 130,"Tyrosine synthesis (need phenylalanine)",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
132 | 131,"Tyrosine degradation",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
133 | 132,"Tyrosine to adrenaline",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
134 | 133,"Tyrosine to dopamine",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
135 | 134,"Tyrosine to acetoacetate and fumarate",AMINO ACIDS METABOLISM,TYROSINE METABOLISM
136 | 135,"Valine degradation",AMINO ACIDS METABOLISM,VALINE METABOLISM
137 | 136,"Valine to succinyl-coA",AMINO ACIDS METABOLISM,VALINE METABOLISM
138 | 137,"Hydroxymethylglutaryl-CoA synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
139 | 138,"Cholesterol synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
140 | 139,"Acetoacetate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
141 | 140,"Mevalonate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
142 | 141,"Farnesyl-pyrophosphate synthesis",LIPIDS METABOLISM,CHOLESTEROL METABOLISM
143 | 142,"Glycerol-3-phosphate synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
144 | 143,"Phosphatidyl-choline synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
145 | 144,"Phosphatidyl-ethanolamine synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
146 | 145,"Phosphatidyl-serine synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
147 | 146,"Phosphatidyl-inositol synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
148 | 147,"Cardiolipin synthesis",LIPIDS METABOLISM,GLYCEROPHOSPHOLIPID METABOLISM
149 | 148,"Triacylglycerol synthesis",LIPIDS METABOLISM,TRIACYLGLYCEROL METABOLISM
150 | 149,"Sphingomyelin synthesis",LIPIDS METABOLISM,SPHINGOLIPID METABOLISM
151 | 150,"Ceramide synthesis",LIPIDS METABOLISM,SPHINGOLIPID METABOLISM
152 | 151,"Palmitate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
153 | 152,"Palmitate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
154 | 153,"Palmitolate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
155 | 154,"Palmitolate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
156 | 155,"cis-vaccenic acid synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
157 | 156,"cis-vaccenic acid degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
158 | 157,"Elaidate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
159 | 158,"Elaidate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
160 | 159,"Linolenate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
161 | 160,"Linoleate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
162 | 161,"gamma-Linolenate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
163 | 162,"gamma-Linolenate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
164 | 163,"Arachidonate synthesis",LIPIDS METABOLISM,FATTY ACID METABOLISM
165 | 164,"Arachidonate degradation",LIPIDS METABOLISM,FATTY ACID METABOLISM
166 | 165,"Synthesis of malonyl-coa",LIPIDS METABOLISM,FATTY ACID METABOLISM
167 | 166,"Synthesis of palmitoyl-CoA",LIPIDS METABOLISM,FATTY ACID METABOLISM
168 | 167,"Taurochenodeoxycholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
169 | 168,"Glycochenodeoxycholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
170 | 169,"tauro-cholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
171 | 170,"glyco-cholate synthesis",LIPIDS METABOLISM,BILE ACID SYNTHESIS
172 | 171,"Synthesis of thromboxane from arachidonate",LIPIDS METABOLISM,EICOSANOID METABOLISM
173 | 172,"Synthesis of galactosyl glucosyl ceramide (link with ganglioside metabolism)",LIPIDS METABOLISM,GANGLIOSIDE METABOLISM
174 | 173,"Synthesis of glucocerebroside",LIPIDS METABOLISM,GANGLIOSIDE METABOLISM
175 | 174,"Synthesis of globoside (link with globoside metabolism)",LIPIDS METABOLISM,GLOBOSIDE METABOLISM
176 | 175,"NAD synthesis from nicotinamide",VITAMIN & COFACTOR METABOLISM,COENZYME NAD BIOSYNTHESIS
177 | 176,"FAD synthesis",VITAMIN & COFACTOR METABOLISM,COENZYME FAD BIOSYNTHESIS
178 | 177,"Synthesis of coenzyme A",VITAMIN & COFACTOR METABOLISM,COENZYME A BIOSYNTHESIS
179 | 178,"Synthesis of ubiquinone from tyrosine",VITAMIN & COFACTOR METABOLISM,UBIQUINONE 10 BIOSYNTHESIS
180 | 179,"Tetrahydrofolate synthesis",VITAMIN & COFACTOR METABOLISM,FOLATE METABOLISM
181 | 180,"Pyridoxal-phosphate synthesis",VITAMIN & COFACTOR METABOLISM,VITAMIN B6 METABOLISM
182 | 181,"Synthesis of bilirubin",VITAMIN & COFACTOR METABOLISM,HEME METABOLISM
183 | 182,"Heme synthesis",VITAMIN & COFACTOR METABOLISM,HEME METABOLISM
184 | 183,"Phosphatidyl-inositol to glucosaminyl-acylphosphatidylinositol",VITAMIN & COFACTOR METABOLISM,GPI ANCHOR BIOSYNTHESIS
185 | 184,"Glucosaminyl-acylphosphatidylinositoll to deacylated-glycophosphatidylinositol (GPI)-anchored protein",GLYCAN METABOLISM,GPI ANCHOR BIOSYNTHESIS
186 | 185,"Degradation of n2m2nmasn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
187 | 186,"Biosynthesis of m4mpdol_U",GLYCAN METABOLISM,N-GLYCAN METABOLISM
188 | 187,"Biosynthesis of g3m8masn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
189 | 188,"Degradation of s2l2fn2m2masn",GLYCAN METABOLISM,N-GLYCAN METABOLISM
190 | 189,"Biosynthesis of core2 (beta-D-Galactosyl-1,3-(N-acetyl-beta-D-glucosaminyl-1,6)-N-acetyl-D-galactosaminyl-R)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
191 | 190,"Biosynthesis of core4 (N-Acetyl-beta-D-glucosaminyl-1,6-(N-acetyl-beta-D-glucosaminyl-1,3)-N-acetyl-D-galactosaminyl-R)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
192 | 191,"Biosynthesis of Tn_antigen (Glycoprotein N-acetyl-D-galactosamine)",GLYCAN METABOLISM,O-GLYCAN METABOLISM
193 | 192,"Keratan sulfate biosynthesis from O-glycan (core 2-linked)",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
194 | 193,"Keratan sulfate biosynthesis from O-glycan (core 4-linked)",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
195 | 194,"Keratan sulfate degradation",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
196 | 195,"Keratan sulfate biosynthesis from N-glycan",GLYCAN METABOLISM,KERATAN SULFATE METABOLISM
197 |
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/test/suite/dataRecon22_local_minmaxmean_percentile.mat:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataRecon22_local_minmaxmean_percentile.mat
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/test/suite/dataRecon22_local_minmaxmean_percentile.score.csv:
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1 | 2.259,0.00091904,0.0013052
2 | 5.5001,6.08,4.1739
3 | 1.2377,1.624,0.8793
4 | 3.0918,3.1573,2.4148
5 | 3.7972,3.956,3.822
6 | 8.0164,8.5346,8.856
7 | 2.6567,1.9678,1.3312
8 | 1.8016,2.3271,1.6681
9 | 2.1935,2.4633,2.6072
10 | 1.9139,1.9196,2.0723
11 | 2.2841,2.5266,2.6475
12 | 1.9353,1.9086,2.0793
13 | 1.9889,2.2169,2.3459
14 | 1.8074,1.8035,1.9713
15 | 2.3911,2.6428,2.7796
16 | 2.0567,2.022,2.2186
17 | 1.7058,1.6766,2.1288
18 | 2.2737,2.699,2.9599
19 | 1.1461,2.0572,1.9036
20 | 1.1461,2.0572,1.9036
21 | 0.87024,2.0202,1.0937
22 | 0.72839,0.73881,1.2653
23 | 0.17748,2.1544,1.4875
24 | 2.376,1.6329,2.6045
25 | 2.376,1.6329,2.6045
26 | 5.3802,5.2564,5.4926
27 | 5.5178,5.3946,5.6195
28 | 4.5695,3.8248,3.821
29 | 5.211,4.9495,5.2028
30 | 2.7406,3.7361,2.4951
31 | 4.6293,2.2586,0.53221
32 | 7.247,7.6991,7.9642
33 | 2.484,3.5245,2.1178
34 | 4.5402,3.1366,3.2909
35 | 2.7606,2.3313,2.6459
36 | 0.21013,2.3823,3.1268
37 | 3.5026,2.7276,1.896
38 | 2.7799,1.3055,1.4823
39 | 7.0991,9.405,8.6819
40 | 3.2672,3.1574,2.5653
41 | 5.0174,3.025,2.8421
42 | 2.4702,3.3812,2.9475
43 | 4.7776,3.034,2.9364
44 | 2.7698,3.5449,3.9756
45 | 2.1809,2.5817,1.9944
46 | 2.1911,2.3392,2.4622
47 | 4.5491,3.6533,3.5353
48 | 3.3181,2.8308,2.5722
49 | 4.5362,3.5984,3.4493
50 | 1.6625,1.7391,2.0827
51 | 4.7776,3.034,2.9364
52 | 5.3107,6.3934,5.8816
53 | 5.1602,6.1394,5.7303
54 | 2.0876,2.2784,2.6372
55 | 2.5723,2.6469,1.7578
56 | 5.1168,5.1848,3.5083
57 | 1.3984,1.6818,1.9713
58 | 0.48151,0.2456,0.98373
59 | 1.5924,1.8149,1.5657
60 | 3.9429,4.0519,3.4234
61 | 5.7985,5.579,5.5317
62 | 3.1407,3.1484,2.8997
63 | 1.9226,1.6089,1.6555
64 | 4.3295,4.343,4.0549
65 | 3.2967,3.2406,3.1666
66 | 3.4468,3.1281,2.7778
67 | 2.1204,1.0965,1.8717
68 | 3.0848,2.7298,2.2406
69 | 3.1514,4.4526,2.5608
70 | 5.0288,4.8029,4.7321
71 | 2.9239,2.9827,2.8262
72 | 5.5863,5.5416,5.454
73 | 3.1535,3.3194,2.9901
74 | 5.7639,4.842,4.2924
75 | 0.26927,0.82363,0.088718
76 | 2.5712,2.9637,4.6187
77 | 4.7304,4.8187,4.41
78 | 3.0453,3.1038,2.8531
79 | 1.2605,1.2739,1.6598
80 | 0.32811,2.0246,0.57603
81 | 1.684,2.2784,2.2564
82 | 2.1269,1.9455,1.7497
83 | -1,-1,-1
84 | 6.5497,2.1586,0.44461
85 | 6.0308,5.964,5.8731
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90 | 1,1,1
91 | 1,0,0
92 | 1,1,1
93 | 0,0,0
94 | 1,1,1
95 | 1,0,0
96 | 1,0,0
97 | 1,0,0
98 | 0,0,0
99 | 0,1,0
100 | 0,0,0
101 | 0,0,0
102 | 1,0,0
103 | 1,1,0
104 | 0,1,0
105 | 1,0,0
106 | 0,0,0
107 | 0,0,0
108 | 1,0,0
109 | 0,1,1
110 | 1,1,0
111 | 0,1,0
112 | 0,0,1
113 | 1,1,1
114 | 1,0,0
115 | 1,0,0
116 | 0,1,0
117 | 1,1,0
118 | 0,0,0
119 | 1,1,1
120 | 1,0,0
121 | 0,0,0
122 | 1,1,0
123 | 1,0,0
124 | 0,0,1
125 | 0,0,0
126 | 0,0,1
127 | 0,0,1
128 | 0,0,0
129 | 0,0,0
130 | 0,0,0
131 | 1,0,0
132 | 0,1,0
133 | 0,0,0
134 | 0,0,0
135 | 1,0,0
136 | 0,1,0
137 | 1,1,1
138 | 0,1,0
139 | 1,1,1
140 | 0,1,0
141 | 0,0,0
142 | 1,0,0
143 | 1,1,0
144 | 1,1,0
145 | 1,1,0
146 | 1,1,0
147 | 1,1,0
148 | 1,0,0
149 | 1,0,0
150 | 1,0,0
151 | 1,1,1
152 | 1,1,0
153 | 1,1,0
154 | 1,0,0
155 | 1,1,1
156 | 1,1,0
157 | 1,1,1
158 | 1,1,0
159 | 1,1,0
160 | 1,1,0
161 | 1,1,1
162 | 1,1,0
163 | 1,1,1
164 | 1,1,0
165 | 1,1,1
166 | 1,0,0
167 | 0,0,0
168 | 0,0,0
169 | 0,0,0
170 | 0,0,0
171 | 0,0,1
172 | 1,0,0
173 | 1,0,0
174 | 1,0,0
175 | 0,0,1
176 | 0,0,0
177 | 0,0,0
178 | 0,0,0
179 | 0,0,0
180 | 1,0,0
181 | 1,0,0
182 | 0,0,0
183 | 0,0,0
184 | 0,0,0
185 | 0,0,0
186 | -1,-1,-1
187 | -1,-1,-1
188 | 0,0,0
189 | 0,0,0
190 | 0,0,0
191 | 1,1,1
192 | 0,0,0
193 | 0,0,0
194 | 0,1,0
195 | 0,0,0
196 |
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/test/suite/dataTest.mat:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataTest.mat
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/test/suite/dataTest.xlsx:
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https://raw.githubusercontent.com/LewisLabUCSD/CellFie/208727ebdeb6146cb5f00a1f863583596aa853cf/test/suite/dataTest.xlsx
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/test/suite/testCellFie.m:
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1 | % Generation of all the potential results
2 |
3 | %% dataRecon22_local_minmaxmean_value
4 | load('dataTest.mat')
5 | SampleNumber=3;
6 | ref='MT_recon_2_2_entrez.mat';
7 | param.ThreshType='local';
8 | param.percentile_or_value='value';
9 | param.LocalThresholdType='minmaxmean';
10 | param.value_low=25;
11 | param.value_high=75;
12 |
13 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
14 | save dataRecon22_local_minmaxmean_value score score_binary taskInfos detailScoring
15 | csvwrite('dataRecon22_local_minmaxmean_value.score.csv',score);
16 | csvwrite('dataRecon22_local_minmaxmean_value.score_binary.csv',score_binary);
17 | T = cell2table(taskInfos);
18 | writetable(T,'dataRecon22_local_minmaxmean_value.taskInfo.csv');
19 | Var={};
20 | for i=1:SampleNumber
21 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
22 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
23 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
24 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
25 | strcat('GeneExpressionValue_S',num2str(i))];
26 | end
27 | D = cell2table(detailScoring,'VariableNames',Var);
28 | writetable(D,'dataRecon22_local_minmaxmean_value.detailScoring.csv')
29 |
30 | %% dataRecon22_local_minmaxmean_percentile
31 | load('dataTest.mat')
32 | SampleNumber=3;
33 | ref='MT_recon_2_2_entrez.mat';
34 | param.ThreshType='local';
35 | param.percentile_or_value='percentile';
36 | param.LocalThresholdType='minmaxmean';
37 | param.percentile_low=25;
38 | param.percentile_high=75;
39 |
40 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
41 | save dataRecon22_local_minmaxmean_percentile score score_binary taskInfos detailScoring
42 | csvwrite('dataRecon22_local_minmaxmean_percentile.score.csv',score);
43 | csvwrite('dataRecon22_local_minmaxmean_percentile.score_binary.csv',score_binary);
44 | T = cell2table(taskInfos);
45 | writetable(T,'dataRecon22_local_minmaxmean_percentile.taskInfo.csv');
46 | Var={};
47 | for i=1:SampleNumber
48 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
49 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
50 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
51 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
52 | strcat('GeneExpressionValue_S',num2str(i))];
53 | end
54 | D = cell2table(detailScoring,'VariableNames',Var);
55 | writetable(D,'dataRecon22_local_minmaxmean_percentile.detailScoring.csv');
56 |
57 | %% dataRecon22_local_mean
58 | load('dataTest.mat')
59 | SampleNumber=3;
60 | ref='MT_recon_2_2_entrez.mat';
61 | param.ThreshType='local';
62 | param.LocalThresholdType='mean';
63 |
64 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
65 | save dataRecon22_local_mean score score_binary taskInfos detailScoring
66 | csvwrite('dataRecon22_local_mean.score.csv',score);
67 | csvwrite('dataRecon22_local_mean.score_binary.csv',score_binary);
68 | T = cell2table(taskInfos);
69 | writetable(T,'dataRecon22_local_mean.taskInfo.csv');
70 | Var={};
71 | for i=1:SampleNumber
72 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
73 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
74 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
75 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
76 | strcat('GeneExpressionValue_S',num2str(i))];
77 | end
78 | D = cell2table(detailScoring,'VariableNames',Var);
79 | writetable(D,'dataRecon22_local_mean.detailScoring.csv');
80 |
81 | %% dataRecon22_global_value
82 | load('dataTest.mat')
83 | SampleNumber=3;
84 | ref='MT_recon_2_2_entrez.mat';
85 | param.ThreshType='global';
86 | param.percentile_or_value='value';
87 | param.value=50;
88 |
89 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
90 | save dataRecon22_global_value score score_binary taskInfos detailScoring
91 | csvwrite('dataRecon22_global_value.score.csv',score);
92 | csvwrite('dataRecon22_global_value.score_binary.csv',score_binary);
93 | T = cell2table(taskInfos);
94 | writetable(T,'dataRecon22_global_value.taskInfo.csv');
95 | Var={};
96 | for i=1:SampleNumber
97 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
98 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
99 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
100 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
101 | strcat('GeneExpressionValue_S',num2str(i))];
102 | end
103 | D = cell2table(detailScoring,'VariableNames',Var);
104 | writetable(D,'dataRecon22_global_value.detailScoring.csv');
105 |
106 | %% dataRecon22_global_percentile
107 | load('dataTest.mat')
108 | SampleNumber=3;
109 | ref='MT_recon_2_2_entrez.mat';
110 | param.ThreshType='global';
111 | param.percentile_or_value='percentile';
112 | param.percentile=50;
113 |
114 | [score, score_binary ,taskInfos, detailScoring]=CellFie(data,SampleNumber,ref,param);
115 | save dataRecon22_global_percentile score score_binary taskInfos detailScoring
116 | csvwrite('dataRecon22_global_percentile.score.csv',score);
117 | csvwrite('dataRecon22_global_percentile.score_binary.csv',score_binary);
118 | T = cell2table(taskInfos);
119 | writetable(T,'dataRecon22_global_percentile.taskInfo.csv');
120 |
121 | Var={};
122 | for i=1:SampleNumber
123 | Var=[Var strcat('SampleID_S',num2str(i)) strcat('TaskID_S',num2str(i)) strcat('TaskScore_S',num2str(i))...
124 | strcat('BinaryTaskScore_S',num2str(i)) strcat('EssentialRxnsTask_S',num2str(i))...
125 | strcat('ExpressionScoreEssentialRxnsTask_S',num2str(i))...
126 | strcat('GeneAssociatedToEssentialRxnsTask_S',num2str(i))...
127 | strcat('GeneExpressionValue_S',num2str(i))];
128 | end
129 | D = cell2table(detailScoring,'VariableNames',Var);
130 | writetable(D,'dataRecon22_global_percentile.detailScoring.csv');
131 |
132 |
133 |
--------------------------------------------------------------------------------