ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics (Adv. Sci. 15/2023)

Abstract

Single‐Cell Proteomics Single‐cell proteomics provides an unprecedented view of cellular heterogeneity at the single‐cell resolution, enabling a better understanding of biological mechanisms. In article number 2207061, Ying Zhang, Huaicheng Sun, Feng Zhu, and co‐workers develop ANPELA, a significantly enhanced quantification tool that facilitates the analysis of high‐dimensional single‐cell proteome expression data and the identification of cell populations with distinct static or pseudo‐temporal phenotypes. This work sheds light on the importance of accurate quantification and proper data processing in advancing single‐cell proteomics research.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

“…Different strategies can be used to correct the signal of metabolites and remove the variations among different experimental batches based on QC samples, ISs, or QC metabolites. Moreover, the data-driven normalization method is necessary for removing unwanted variations and retaining useful information . These normalization methods consist of sample-based methods, metabolite-based methods, and sample- and metabolite-based methods.…”

“…Moreover, the data-driven normalization method is necessary for removing unwanted variations and retaining useful information. 87 These normalization methods consist of sample-based methods, metabolitebased methods, and sample-and metabolite-based methods. Strategies combining sample-and metabolite-based methods were suggested in previous studies as superior to other methods, as they can not only reduce systematic bias among samples but also make all features more comparable.…”