Comparison and definition of clusters
To distinguish tumor cells, we used inferring copy number variation
(CNV)10,11 to compare our PPB data with normal
fibroblasts whose expression profiles were obtained from the open access
repository of the Gene expression omnibus (Gse
128169)12. To further analyze tumor heterogeneity, the
obtained clusters were identified by cluster-specific marker genes. Gene
Ontology enrichment analysis (GO analysis) was performed with the
ClusterProfiler package
(https://bioconductor.org/packages/clusterProfiler) for functional
comparison of similar clusters. Moreover, secondary sparse-nonnegative
matrix factorization (NMF) analysis was applied to PPB to improve
identification of both broad and rare cell clusters13.
Pseudotime trajectory
analysis with monocle
Using Monocle 2 (http://cole-trapnell-lab.github.io/monocle-release)
with differential genes of each clusters, we reshaped the process of
cellular changes over time by constructing trajectories between tumor
cells. After exclusion of non-tumor cells, the remaining cells were
reduced dimensionality using the DDRTree method, sequenced in
pseudotime, and finally visualized.