collecting data for chi squared test

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Christos Papagrigoriou
Christos Papagrigoriou on 29 Apr 2022
Answered: Voss on 30 Apr 2022
Hello,
I have this data. I import it into matlab and and I need to fill that table out. Can you please give me a hint on how to fill it in a time efficient manner.
  2 Comments
Rik
Rik on 29 Apr 2022
If you post the non-efficient code, we can help you optimize it.
Christos Papagrigoriou
Christos Papagrigoriou on 30 Apr 2022
Hello,
table1 = (strcmp(NSCLCR01RadiogenomicDATALABEL.KrasMutation, 'Mutant') & strcmp(NSCLCR01RadiogenomicDATALABEL.SmokingStatus, 'Current'), :);
It turns back a hollow table 0x17

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Accepted Answer

Voss
Voss on 30 Apr 2022
Ran in:
C = readcell('NSCLCR01Radiogenomic_DATA_LABEL.xlsx');
C(:,[4 6 8 10 18:end]) = [];
headers = C(1,:);
C(1,:) = [];
t = cell2table(C,'VariableNames',headers)
t = 210×13 table
Case ID Patient affiliation Age at Histological Diagnosis Gender Smoking status Histology EGFR mutation status KRAS mutation status ALK translocation status Adjuvant Treatment Chemotherapy Radiation Recurrence ___________ ___________________ _____________________________ __________ ______________ __________________ ____________________ ____________________ ________________________ __________________ ____________ _________ __________ {'AMC-001'} {'Stanford'} 34 {'Male' } {'Nonsmoker'} {'Adenocarcinoma'} {'Wildtype' } {'Mutant' } {'Wildtype' } {'No' } {'No' } {'No'} {'yes'} {'AMC-002'} {'Stanford'} 33 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Wildtype' } {'Wildtype' } {'Not collected'} {'No' } {'No' } {'No'} {'no' } {'AMC-003'} {'Stanford'} 69 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Mutant' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-004'} {'Stanford'} 80 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Wildtype' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-005'} {'Stanford'} 76 {'Male' } {'Former' } {'Adenocarcinoma'} {'Mutant' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'yes'} {'AMC-006'} {'Stanford'} 80 {'Female'} {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Not collected'} {'Not collected'} {'No' } {'No' } {'No'} {'no' } {'AMC-007'} {'Stanford'} 56 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Wildtype' } {'Wildtype' } {'Not collected'} {'No' } {'No' } {'No'} {'no' } {'AMC-008'} {'Stanford'} 65 {'Female'} {'Former' } {'Adenocarcinoma'} {'Not collected'} {'Mutant' } {'Not collected'} {'No' } {'No' } {'No'} {'no' } {'AMC-009'} {'Stanford'} 61 {'Male' } {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Mutant' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-010'} {'Stanford'} 42 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Mutant' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-011'} {'Stanford'} 66 {'Female'} {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Mutant' } {'Wildtype' } {'Yes'} {'Yes'} {'No'} {'yes'} {'AMC-012'} {'Stanford'} 70 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Mutant' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'yes'} {'AMC-013'} {'Stanford'} 67 {'Female'} {'Nonsmoker'} {'Adenocarcinoma'} {'Mutant' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-014'} {'Stanford'} 78 {'Female'} {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' } {'AMC-015'} {'Stanford'} 58 {'Male' } {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Mutant' } {'Not collected'} {'No' } {'No' } {'No'} {'no' } {'AMC-016'} {'Stanford'} 65 {'Male' } {'Former' } {'Adenocarcinoma'} {'Wildtype' } {'Wildtype' } {'Wildtype' } {'No' } {'No' } {'No'} {'no' }
t_mutant_smokers = t(strcmp(t{:,'KRAS mutation status'},'Mutant') & strcmp(t{:,'Smoking status'},'Current'),:)
t_mutant_smokers = 8×13 table
Case ID Patient affiliation Age at Histological Diagnosis Gender Smoking status Histology EGFR mutation status KRAS mutation status ALK translocation status Adjuvant Treatment Chemotherapy Radiation Recurrence ___________ ___________________ _____________________________ __________ ______________ __________________ ____________________ ____________________ ________________________ __________________ ____________ _________ __________ {'R01-003'} {'VA' } 65 {'Male' } {'Current'} {'Adenocarcinoma'} {'Mutant' } {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' } {'R01-024'} {'VA' } 52 {'Male' } {'Current'} {'Adenocarcinoma'} {'Unknown' } {'Mutant'} {'Unknown' } {'No' } {'No' } {'No' } {'yes'} {'R01-053'} {'VA' } 62 {'Male' } {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' } {'R01-054'} {'VA' } 56 {'Male' } {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'Yes'} {'Yes'} {'Yes'} {'yes'} {'R01-095'} {'VA' } 74 {'Male' } {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' } {'R01-097'} {'VA' } 70 {'Male' } {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' } {'R01-116'} {'VA' } 54 {'Male' } {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' } {'R01-137'} {'Stanford'} 50 {'Female'} {'Current'} {'Adenocarcinoma'} {'Wildtype'} {'Mutant'} {'Wildtype'} {'No' } {'No' } {'No' } {'no' }

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