I have a large table with over 13,000 rows, each row represents an experiment and containins a set of genes. I would like to get generate a list of all the genes and the number of experiments that contain that gene. For example:
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Hello,
tableA:
Experiment, Gene1, Gene2, Gene3, Gene4;
'1', 'A', 'C', 'D', '';
'2', 'A', 'C', 'G', '';
'3', 'C', 'B', 'D', 'D';
What I would like is a count of number of experiments that each gene occurs in ignoring duplicates within the same experiment. For Example A = 2, B = 1, C = 3, D = 2
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Guillaume
2017-10-5
Here is how I'd do it:
%demo input
tableA = cell2table({...
'1', 'A', 'C', 'D', '';
'2', 'A', 'C', 'G', '';
'3', 'C', 'B', 'D', 'D';}, ...
'VariableNames', ...
{'Experiment', 'Gene1', 'Gene2', 'Gene3', 'Gene4'})
%processing
genes = unique(tableA{:, 2:5}).'; %get list of genes
genes(1) = []; %get rid of ''
expcount = sum(cell2mat(rowfun(@(g) ismember(genes, g), tableA, 'SeparateInputs', false, 'InputVariables', 2:5, 'OutputFormat', 'cell')));
result = array2table(expcount, 'VariableNames', compose('gene_%s', string(genes))) %for pretty display
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Guillaume
2017-10-5
Exactly, g is the the name of the input to the anonymous function. It receives a row of the table, and check the list of genes against it. That gives you an array of 0 and 1 telling which genes are present in the row. Summing all the rows gives you the result you want.
Stephen
2017-10-5
编辑:Stephen
2017-10-5
Given your example and format, you will probably want to make use of a for loop and the "unique" function:
Gene = struct('numExp',[],...) %Add other properties of experiments
AllGenes = struct('A',Gene ,'B',Gene ,'C',Gene ,...etc) % Each gene gets a structure field
for ptr = 1:13000
currGenes = unique(tableA(2:end));
for ptr2 = 1:length(currGenes)
incGene = currGenes{ptr2};
AllGenes.(incGene).numExp = AllGenes.(incGene).numExp + 1;
end
end
Peter Perkins
2017-10-13
+1 for Guillaume's use of rowfun.
Here's another possibility that might be wiorth looking at if you have very large number of experiments and a smaller number of genes:
>> tableA = cell2table({...
'1', 'A', 'C', 'D', '';
'2', 'A', 'C', 'G', '';
'3', 'C', 'B', 'D', 'D';}, ...
'VariableNames', ...
{'Experiment', 'Gene1', 'Gene2', 'Gene3', 'Gene4'});
Turn things into categorical because categorical is useful:
>> tableA.Experiment = categorical(tableA.Experiment);
>> genes = {'A' 'B' 'C' 'D' 'G'};
>> tableA.Gene1 = categorical(tableA.Gene1,genes);
>> tableA.Gene2 = categorical(tableA.Gene2);
>> tableA.Gene3 = categorical(tableA.Gene3);
>> tableA.Gene4 = categorical(tableA.Gene4);
Create one array, then look across rows for each gene, looping over genes instead of rows. This could create a 1-row table, or a scalar struct, or a double vector (with no names). I chose the struct, just because a table with only one row is kind of overkill:
>> allGenes = [tableA.Gene1 tableA.Gene2 tableA.Gene3 tableA.Gene4]
allGenes =
3×4 categorical array
A C D <undefined>
A C G <undefined>
C B D D
>> for i = 1:length(genes)
counts.(genes{i}) = sum(any(allGenes==genes{i},2));
end
>> counts
counts =
struct with fields:
A: 2
B: 1
C: 3
D: 2
G: 1
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