To investigate CAR-T regulatory pathways activated by different treatments, we ran **SCENIC** which is a computational method to infer Gene Regulatory Networks (GRN) from scRNA-seq data.</br>
To investigate CAR-T regulatory pathways activated by different treatments, we ran **SCENIC** which is a computational method to infer Gene Regulatory Networks (GRN) from scRNA-seq data.</br>
In details, starting from the UMI count matrix, the potential targets of the same transcription factor (TF) (gene regulatory networks) are identified based on co-expression using the GRNBoost algorithm and then potential direct-binding targets (regulons) are selected based on DNA-motif analysis (i.e., TF motif analysis). Finally, the network activity of regulons is computed in each individual cell and combined to obtain the average regulon activity by treatment group.
In details, starting from the UMI count matrix, the potential targets of the same transcription factor (TF) (gene regulatory networks) are identified based on co-expression using the GRNBoost algorithm and then potential direct-binding targets (regulons) are selected based on DNA-motif analysis (i.e., TF motif analysis). Finally, the network activity of regulons is computed in each individual cell and combined to obtain the average regulon activity by treatment group.
- Full_CART_metadata.xlsx: file containing the meta-data of the CART Seurat object.
- Full_CART_HM_regulonActivity.xlsx: file containing the regulon activity shown in the heatmap.
