Scripts in this folder is used for the data analysis of LSTM-SOM results.
Used to add clinical data in the LSTM-SOM results.
The cluster results were added inmut_info_XX.csv
in addclicinfo.py, Fisrt the '.csv' files of LSTM-SOM results will be combined, and then devided according to their ICD O3 coding, cancers in some location were merged according to cancer treatment guidelines and clinical practice. Reference: cfg.CFG.clicfeat_dict
Replace the LSTM-SOM class results (i.e. '1210') by new class label (i.e. 'Class_1').
Before analysis of clinical features, another file of patient information shold be prepared, as the formation of ./SAMPLEDATA/analysis_patient_20200617/thymus.csv . The example of mutation data is also shown in this folder (./SAMPLEDATA/analysis_data_20200617/thymus.csv)
Used to analyze relationship between MBs classification (datapoint) and clinical features (ICD coding).
The clinical feature need analysis is assigned in variable 'calcitem' (in main process).
Used to analyze relationship between MBs proportion and clinical features.
Used to analyze relationship between relation between MB classification (datapoint). and clinical features (age, weight, ajcc stage, T stage, N stage, and M stage), the clinical feature need analysis is assigned in variable 'drawfile', and output figure with name 'fig_name' (in main process).
Used to draw scatter picture of pairwise survival analysis. Different is reflected by color.
Used to draw scatter picture for MB count and frequency in patients of different cancer (site, pathology).
Used to decide how many classes is most suitable for K-means cluster. K-means is performed according to the proportion of MBs. A 'elbow' method is used to find out the number of classes.
Used to analyze the clinical features of patients in different classes cluster by K-means. The clinical features need analysis are in variable 'CLAC_DICT', if the value is 'avr', it means a continuous variable; if the value is 'chi', it means a discrete variable.
Survival analysis for patients according to classes cluster by K-means.
Draw histogram for base proportion beside mutation site.
Draw heatmap for genes with high frequency in different classes of MBs.