Search Results for author:
Found 7 papers, 3 papers with code
Review of computational methods for estimating cell potency from single-cell RNA-seq data, with a detailed analysis of discrepancies between method description and code implementation
In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots.
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability.
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
While practitioners commonly combine ambiguous outcome labels for all data points (instances) in an ad hoc way to improve the accuracy of multi-class classification, there lacks a principled approach to guide the label combination for all data points by any optimality criterion.
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.
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
In contrast, the Neyman-Pearson paradigm can train classifiers to achieve a high-probability control of the population type I error, but it relies on sample splitting that reduces the effective training sample size.
Issues arising from benchmarking single-cell RNA sequencing imputation methods
We find that the semi-synthetic data have very different properties from those of real scRNA-seq data and that the cell clusters used for benchmarking are inconsistent with the cell types labeled by biologists.
Applications Genomics Quantitative Methods
Matched bipartite block model with covariates
Community detection or clustering is a fundamental task in the analysis of network data.
