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fix: score HPA genes over the cell types they were measured in #680

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Merged
edkerk merged 1 commit into from
Jul 17, 2026
Merged

fix: score HPA genes over the cell types they were measured in #680

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13 changes: 10 additions & 3 deletions INIT/scoreComplexModel.m
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Original file line number Diff line number Diff line change
Expand Up @@ -265,12 +265,19 @@
EM = 'There are expression level categories that do not match to hpaLevelScores';
error('RAVEN:badInput', '%s', EM);
end
[K, L, M] = find(hpaData.gene2Level);
[K, ~, M] = find(hpaData.gene2Level);
scores = hpaLevelScores.scores(J);
% Reduce over the cell types each gene was actually measured in. Building a
% sparse GENESxCELLTYPES matrix instead would put a numeric 0 at every
% unmeasured (gene, cell type) pair, and 0 sits between 'Low' (10) and
% 'Not detected' (-8): a gene not detected in one of two cell types would
% score 0 rather than -8 under 'max', and would never be pruned. The
% arrayData branch above likewise divides by the number of measurements.
measured = reshape(scores(M), [], 1);
if strcmpi(multipleCellScoring,'max')
hScores = max(sparse(K,L,scores(M),numel(hpaData.genes),numel(hpaData.tissues)),[],2);
hScores = accumarray(K(:), measured, [numel(hpaData.genes) 1], @max, 0);
else
hScores = mean(sparse(K,L,scores(M),numel(hpaData.genes),numel(hpaData.tissues)),2);
hScores = accumarray(K(:), measured, [numel(hpaData.genes) 1], @mean, 0);
end

% Assign gene scores, prioritizing HPA (protein) data over arrayData (RNA)
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25 changes: 25 additions & 0 deletions testing/function_tests/tINIT.m
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,31 @@ function scoreComplexModelRuns(testCase)
testCase.verifyNumElements(rxnScores, numel(testCase.model.rxns));
end

function scoreComplexModelHpaUnmeasuredCellTypeIsNotZero(testCase)
% A gene not detected in one cell type of a tissue and not
% measured in the other must keep its 'Not detected' score. An
% unmeasured cell type is not a measurement of zero, and zero
% ranks above 'Not detected' (-8), so scoring it as such would
% also keep the gene from ever being pruned.
m = testCase.model;
hpaData.genes = m.genes(1);
hpaData.tissues = {'liver';'liver'};
hpaData.celltypes = {'hepatocyte';'kupffer'};
hpaData.levels = {'Not detected'};
hpaData.gene2Level = sparse(1,2);
hpaData.gene2Level(1,1) = 1; % not detected in hepatocyte
% not measured in kupffer

evalc(['[~,~,hpaScores] = scoreComplexModel(m, hpaData, [], ' ...
'''liver'', ''multipleCellScoring'', ''max'');']);
testCase.verifyEqual(hpaScores(1), -8, 'AbsTol', 1e-9);

% The average is likewise taken over the measurements that exist
evalc(['[~,~,hpaScores] = scoreComplexModel(m, hpaData, [], ' ...
'''liver'', ''multipleCellScoring'', ''average'');']);
testCase.verifyEqual(hpaScores(1), -8, 'AbsTol', 1e-9);
end

function scoreComplexModelExactScores(testCase)
% scoreComplexModel must reproduce the known reaction scores for
% the reference model prepared by prepINITModel.
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