Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments
원문 초록
Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
DCR: Quantifying Data Contamination in LLMs Evaluation
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.