Data Analysis Process of "Single-cell and spatial transcriptomics reveal mechanisms of radioresistance and immune escape in recurrent nasopharyngeal carcinoma".
For citation or to learn more, please visit our article: Single-cell and spatial transcriptomics reveal mechanisms of radioresistance and immune escape in recurrent nasopharyngeal carcinoma
For bulk RNA-seq access, please visit: https://ngdc.cncb.ac.cn/gsa-human/s/aBR4OloA.
For scRNA-seq and spatial data access, please visit: https://ngdc.cncb.ac.cn/gsa-human/s/6R3na3yw.
Below is the specific content introduction of the code file：
RNA-seq:
01: Deconv for bulk RNA-seq
02: Survival analysis
scRNA-seq:
01: Load scRNA-seq data data and preprocess
02: Integation by harmony and clustering/annotation for scRNA-seq data
03: NMF anaylsis for single malignant cells
04: DEG analysis
05: Crosstalk (ligand and receptor analysis) using liana
06.1: Trajectory analysis using Monocle3 for CAF (Cancer-associated fibroblast)
06.2: Trajectory analysis using Monocle3 for CD8T
07: CytoTRACE (Cellular (Cyto) Trajectory Reconstruction Analysis using gene Counts and Expression) analysis for CAF
Spatial transcriptomics:
01: Progeny (Pathway RespOnsive GENes for activity inference) for spatial data
02: Deconvolution for ST data by using SpaCET (Spatial Cellular Estimator for Tumors)
03: Colocalization score
04: Stemness scores for Malignant near/not near CAFs
05: Spatial interactions using COMMOT
06: Neighbor enrichment using Suqidpy
07: Distance from myeloid to malignant on ST data