Enrichment analysis of common highly expressed AP-1 related naïve and memory B cell subtypes was performed using the Metascape resource, which identified enriched biological process (BP) terms and constructed the functional network. Other overexpression enrichment analysis and gene set enrichment analysis based on BP terms, KEGG pathways and Hallmark were using ClusterProfiler R package32 (link) (V.4.4.4), where BP and Hallmark gene sets were download from MsigDB33 (link) and KEGG pathways were acquired by KEGGREST R package (V.1.36.3). The genes used to measure AP-1 complex activity included FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, and JUND. Gene set module scores in single cells were calculated using the AddModuleScore function in the Seurat package. Differential abundance of B cell lineage subtypes between NewlyDx and normal samples testing based on k-nearest neighbor graphs was implemented by miloR R package34 (link) (V.1.4.0). The kernel density enrichment of sample types and antibody isotypes distribution was assessed along the differentiation trajectory. Taking sample types as an example, we estimated the probability density distribution of AML and normal isotypes along pseudotime using Gaussian kernel functions. Subsequently, the ratio of each pseudotime point was calculated. For the analysis of ligand-receptor interactions between B cells and AML cells, we specifically examined genes that were expressed in more than 10% of cells within each cell subtype, based on experimentally confirmed interactions. Manually collected confirmed ligand-receptor interactions are presented in online supplemental table 5 . To compare ligand-receptor gene expression levels in bulk datasets, we calculated the average expression value for each gene within each sample and then determined the overall average of all ligand-receptor genes. Smoothed Cox proportional hazard analysis were conducted using the phenoTest R package (V.1.44.0) and Kaplan-Meier Curves were visualized by survminer R package (V.0.4.9).
Statistical analyses and data visualization were implemented by R programming language (V.4.2.1). Two-sided Wilcoxon rank-sum and signed rank test and Kruskal-Wallis rank sum test were adopted to test the difference from two or more than two comparable groups, respectively. Correlation analyses were used the Spearman rank correlation test.
Statistical analyses and data visualization were implemented by R programming language (V.4.2.1). Two-sided Wilcoxon rank-sum and signed rank test and Kruskal-Wallis rank sum test were adopted to test the difference from two or more than two comparable groups, respectively. Correlation analyses were used the Spearman rank correlation test.
