immunopipe-ThomasW-2020
Reanalysis of the data from Wu, Thomas D., et al. 2020 using immunopipe.
Data preparation
The data was downloaded from GSE139555.
The metadata is also downloaded and used to build a reference Seurat object. One could also use it to map the data to it, instead of using the unsupervised clustering.
See prepare-data.sh for details.
Configuration
Note
This is not a replication of the original paper, primarily due to the irreproducibility of the clustering results. This is a reanalysis of the data using immunopipe, showing the potential of the pipeline similar analyses listed in the paper.
The configuration can be found at Immunopipe.config.toml. Some settings may be different from the original paper. The analysis was done using Seurat v5. The integration of scRNA-seq data from individual samples were integrated by the IntegrateLayers, instead of FindIntegrationAnchors and IntegrateData workflow in the original paper.
When separating the T cells from the other cells, CD3G, CD3D, CD14 and CD68, together with CD3E, which is the only indicator gene used in the original paper, were used to identify the T cells. Rather than a manual process, immunopipe uses k-means clustering to identify the T cells, using the expression of the above genes and TCR clonotype percentages as features.
The T cell clusters were not annotated with the cell types listed in the paper, as we couldn't replicate the exact clustering results from the original paper.
Results/Reports
You can find the results in the Immunopipe-output directory.
The report can be found at https://imp-thomasw-2020.pwwang.com/REPORTS.