Protocol for Executing and Benchmarking Eight Computational Doublet-Detection Methods in Single-Cell RNA Sequencing Data Analysis
The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational methods have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet-detection methods. DoubletCollection also provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection methods in the fast-growing scRNA-seq field.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingSimilar Papers 제목 키워드 기반
Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data
The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been developed to remove doublets from scRNA-seq …
A Case for Dataset Specific Profiling
Data-driven science is an emerging paradigm where scientific discoveries depend on the execution of computational AI models against rich, discipline-specific datasets. With modern machine learning frameworks, anyone can …
BenchmarkingModel SelectionA Transparent Fairness Evaluation Protocol for Open-Source Language Model Benchmarking on the Blockchain
Large language models (LLMs) are increasingly deployed in realworld applications, yet concerns about their fairness persist especially in highstakes domains like criminal justice, education, healthcare, and finance. This…
A Kernel Classification Framework for Metric Learning
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we gene…
ClassificationGeneral ClassificationMetric LearningTripletTetrad: Actively Secure 4PC for Secure Training and Inference
Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four parties with at most one active corruption …
BenchmarkingFairnessPrivacy Preserving