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License: MIT License
All the code for the Blood/Tumor project.
License: MIT License
Blood_Tumor_Code is the GitHub repository that holds the code for the paper published at the Journal of Experimental Medicine: "Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment." Find the paper here! https://doi.org/10.1084/jem.20200920 The published datasets can be found at the following GEO repository: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41870 This directory has four subdirectories: - DataProcessing Used for converting 10X output (filtered_feature_bc_matrix) to finished Seurat/Scanpy objects for analysis. - Gene_TCR_Analysis Runs various analyses using GEX and TCR for both mouse and human. Uses primarily Scanpy objects. - AUC_ROC_MarkerAnalyses Runs various analyses using GEX, TCR, and COMET data on both mouse and human data. Uses primarily Seurat objects. - MachineLearning Machine Learning analysis for Figure 2 of the paper.
Greetings!
I wasn't able to find an email to inquiry about these doubts so I decided to open an issue.
I'm attempting to reproduce the paper figures using the scripts and notebooks available at this repository, but when trying to plot the UMAP for the Mouse integrated samples (M1, M2 and M3) using the MakeFigures_Mouse
notebook the number of clusters is larger than the 7 expected clusters. I've attempted to vary the resolution parameter in the IntegrateMouseData.R
script but I'm always getting similar results.
Another problem I'm facing is when trying to plot the UMAP for specific genes in Blood samples, I get a missing key error for the genes. I was expecting it'd run all smoothly having the specified inputs and versions of the packages installed but I keep coming across these errors. What could be the cause?
Best regards,
Jonatan
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