An integrated learning approach for cancer subtype classification of breast cancer using the TCGA public high-dimensional dataset.
- idconvert.zip #Breast cancer data from the Sangerbox 3.0 platform. Remember to unzip the dataset when using it.
- breastPam50Classification.R #PAM50 roughly layered.
- comparison #This catalogue is a comparative experiment of the sparesk method.
- dataset #The catalogue is a data set.
- imageResults #This directory contains the processing results.
- model #This directory contains the AE autoencoder training parameters.
- processData #The catalogue loads data and processes it.
- sClass #The catalogue contains supervised models, reviews of machine learning methods and selected TOP features learned.
- usClass #This directory contains the AE Autoencoder model.
- flag.py #Parameter settings
- main_jupyter.ipynb #A priori space to guide the extraction of TOP features.
- main.py #Discovery of cancer subtypes.
- MainSteps.py #main.py method wrapper.
- Python
- R
-
Data format: filename.csv file.
-
X: Numerical matrix. Data other than the first two columns.Each row is a sample and each column is a gene.
Y: Numerical vector. Column PAM50, i.e. column 0. The i-th element indicates the class to which the i-th sample belongs.
-
DATA_DIR #Dataset Storage Directory DATA_FILE #Dataset breast_1211_23900.csv、breast_2133_20000.csv standardization_way # Standardized approach isOneHot #is onthot,1 is,0 not is RESULT_DIR # Visualization of results storage paths '''Clustering''' DATA_CLUSTER_DIR # top feature data subset storage directory DATA_CLUSTER_FILE # top feature data subset samples DATA_CLUSTER_FILE_y # top feature data subset labels n_clusters #Number of clusters '''AE''' IN_OUT_SIZES # Number of input and output neurons Encoder_hidden_size # Encoder hidden layer hidden_size # Number of neurons in the core layer Decoder_hidden_size # Decoder hidden layer AE_epochs # Number of model training rounds AE_batch_size # batchSize
-
First, unpack the breast_1211_23900 dataset located in dataset.
Then, run main_jupyter.ipynb, and finally run main.py.