Clustering Data

       We collected CD4 T cell expression data related to BRCA, and after classification using clustering algorithms, the classification results are shown below. We will subsequently train our model based on these results.

Clustering Results

       The histogram above shows the clustering results of BRCA in the database. According to this result, we divided BRCA-related T cells into 6 categories, and the specific data can be seen in the table below.

       In the following image, we demonstrate the clustering plot of T cells in BRCA. Different colored dots represent cells of different categories.

        After clustering genes in BRCA, their properties of p_val, avg_log2FC, pct_1, pct_2, and p_val_adj are analyzed as follows:

        p_val: Positive values indicate that the gene is more highly expressed in the first group
        avg_log2FC: Log fold-change of the average expression between the two groups
        pct_1: The percentage of cells where the gene is detected in the first group
        pct_2: The percentage of cells where the gene is detected in the second group
        p_val_adj: Adjusted p-value, based on Bonferroni correction using all genes in the dataset
BRCA_DataTables
 cluster cell_type gene p_val avg_log2FC pct_1 pct_2 p_val_adj