Research Paper Volume 13, Issue 21 pp 24219—24235

Construction and external validation of a 5-gene random forest model to diagnose non-obstructive azoospermia based on the single-cell RNA sequencing of testicular tissue

The identification of cell markers via scRNA-seq analysis. (A) The quality control chart. (B, C) The association of detected gene counts with the percent of mitochondrial genes (B) and sequencing depth (C). (D) The Top 10 genes with the most differentially expressed among various cell samples. (E) The PCA analysis. (F) The P-values of each PC. (G) The cell samples were divided into 5 clusters. (H) The cell type annotation. (I) The heat map indicating the expression level of the cell markers in different cell clusters. scRNA-seq, single-cell RNA sequencing; PCA, principal component analysis; PC, principal component.

Figure 2. The identification of cell markers via scRNA-seq analysis. (A) The quality control chart. (B, C) The association of detected gene counts with the percent of mitochondrial genes (B) and sequencing depth (C). (D) The Top 10 genes with the most differentially expressed among various cell samples. (E) The PCA analysis. (F) The P-values of each PC. (G) The cell samples were divided into 5 clusters. (H) The cell type annotation. (I) The heat map indicating the expression level of the cell markers in different cell clusters. scRNA-seq, single-cell RNA sequencing; PCA, principal component analysis; PC, principal component.