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| Original file line number | Diff line number | Diff line change |
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| function [eGenes, INIT_output] = estimateEssentialGenes(model, dataFile, taskStruct, useGeneSymbol) | ||
| % generate tINIT models and estimate essential genes | ||
| % | ||
| % Input: | ||
| % | ||
| % model reference human or animal model | ||
| % | ||
| % dataFile (opt, default Hart2015_RNAseq.txt) | ||
| % | ||
| % taskStruct metabolic task structure (opt, default is Essential tasks) | ||
| % | ||
| % useGeneSymbol use gene symbols as ids and in grRules (opt, default TRUE) | ||
| % | ||
| % Output: | ||
| % | ||
| % eGenes results structure with the following fields: | ||
| % taskList list of metabolic tasks that were tested | ||
| % tissues list of tissues (model IDs) corresponding to each model | ||
| % geneList cell array of the list of genes from each model | ||
| % essentialGenes cell array with one entry per model, where each | ||
| % entry is a logical matrix with rows corresponding | ||
| % to genes (in geneList) and columns to tasks (in | ||
| % taskList). Entries in the matrix are true when a | ||
| % gene is essential for a task, and false otherwise. | ||
| % | ||
| % INIT_output structure containing tINIT models (or information | ||
| % necessary to regenerate tINIT models) for which gene | ||
| % essentiality will be evaluated. | ||
| % | ||
| % Note: This function may take long computation time. | ||
| % | ||
|
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| if nargin < 2 | ||
| % load Hart et al. RNA-Seq cell line data | ||
| dataFile = 'Hart2015_RNAseq.txt'; | ||
| end | ||
|
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| if nargin < 3 | ||
| taskStruct = parseTaskList('metabolicTasks_Essential.txt'); | ||
| end | ||
|
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| if nargin < 4 | ||
| useGeneSymbol = true; | ||
| end | ||
|
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| % replace gene IDs with gene symbols | ||
| if useGeneSymbol | ||
| idMapping = [model.genes, model.geneShortNames]; | ||
| [grRules,genes,rxnGeneMat] = replaceGrRules(model.grRules,idMapping); | ||
| model.grRules = grRules; | ||
| model.genes = genes; | ||
| model.rxnGeneMat = rxnGeneMat; | ||
| end | ||
|
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| % pre-process RNA-Seq data | ||
| disp('Step 1: preprocess and preliminary step') | ||
| tmp = readtable(dataFile); | ||
| arrayData.genes = tmp.gene; | ||
| arrayData.tissues = tmp.Properties.VariableNames(2:end)'; | ||
| arrayData.levels = table2array(tmp(:,2:end)); | ||
|
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|
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| % Run some preliminary steps | ||
| [~,deletedDeadEndRxns] = simplifyModel(model,true,false,true,true,true); | ||
| cModel = removeReactions(model,deletedDeadEndRxns,false,true); | ||
| [taskReport, essentialRxnMat] = checkTasks(cModel,[],true,false,true,taskStruct); | ||
|
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| % add pre-processing results to arrayData structure | ||
| arrayData.deletedDeadEndRxns = deletedDeadEndRxns; | ||
| arrayData.taskReport = taskReport; | ||
| arrayData.essentialRxnMat = essentialRxnMat; | ||
| arrayData.threshold = 1; | ||
|
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| % run tINIT | ||
| disp('Step 2: get tissue models') | ||
| model = addBoundaryMets(model); | ||
| params = {}; | ||
| INIT_output = {}; | ||
|
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| for i = 1:length(arrayData.tissues) | ||
| disp(['Tissue ', num2str(i), ' out of ', num2str(length(arrayData.tissues)),': ', arrayData.tissues{i}]) | ||
|
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| % First try to run tINIT with shorter time limit. If it fails, then | ||
| % try again with a longer time limit. | ||
| try | ||
| params.TimeLimit = 1000; | ||
| init_model = getINITModel2(model,arrayData.tissues{i},[],[],arrayData,[],true,[],true,true,taskStruct,params); | ||
| catch | ||
| params.TimeLimit = 5000; | ||
| init_model = getINITModel2(model,arrayData.tissues{i},[],[],arrayData,[],true,[],true,true,taskStruct,params); | ||
| end | ||
|
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| init_model.id = arrayData.tissues{i}; | ||
| INIT_output.id{i,1} = init_model.id; | ||
| INIT_output.model{i,1} = init_model; | ||
|
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| end | ||
|
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| disp('Step 3: get essential genes') | ||
| % get essential genes for each model and task | ||
| eGenes = getTaskEssentialGenes(INIT_output, model, taskStruct); | ||
| eGenes.refModel = model; | ||
|
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| end |
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