The sncRNA-based signatures robustly distinguish subjects with subfertile sperm from healthy controls (IMAGE)
Caption
(A) The discovery cohort of the study. (B) Strategies and workflow of screening sncRNA characteristics for developing the prediction model between subfertile sperm (AZS or TZS) and healthy control (NZS) samples. (C) The ranking results of the random forest classifiers for distinguishing subfertile sperm (AZS and TZS) cases from healthy controls, ordered by feature importance. (D) The receiver operating characteristic (ROC) curve for sncRNA signatures used to distinguish subfertile sperm (AZS and TZS) cases from healthy controls, along with the corresponding area under the ROC curve (AUC) score. (E) Representative linear correlations between selected sncRNA sequences and sperm motility or sperm morphology metrics. (F, I) The ranking results of the random forest classifiers for distinguishing AZS (F) or TZS (I) cases from healthy controls, ordered by feature importance. SncRNA signatures, sperm morphology indicators, sperm motility indicators, and individual sncRNAs were marked as red, orange, purple, and blue points, respectively. (G, J) ROC curve for selected sncRNA signatures used to distinguish AZS (G) or TZS (J) cases from healthy controls, along with the corresponding AUC scores. (H, K) Representative linear correlations between selected sncRNA sequences and sperm motility (H) or sperm morphology metrics (K).
Credit
Ruofan Huang, Yiting Yang, Wenlin Jiang, Zheng Cao, Junchao Shi, Xiao-Ou Zhang, Yunfang Zhang
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