The Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. 
Gene Expression Omnibus (GEO)
Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. 
machine learning algorithms
TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. 
consensus clustering
These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. 
GSVA
Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. 
single cell RNA-seq (scRNA-seq)
Furthermore, we used CMap to predict five potential drugs for TB-related diseases.



CMap