clustifyr

clustifyr assigns cell-type identities to clusters in single-cell RNA sequencing (scRNA-seq) data by comparing cluster expression profiles to reference datasets and applying correlation-based and gene-list enrichment analyses, and is implemented as an R package.

Key Features:

• Versatile Reference Data Compatibility: Accepts bulk RNA-seq profiles, sorted microarray expression data, single-cell gene signatures, and lists of marker genes as reference inputs.
• Data Integration: Incorporates external gene expression profiles and marker lists into the analysis workflow for cluster annotation.
• Correlation-Based Matching: Matches scRNA-seq cluster expression profiles to reference datasets using correlation metrics with support for benchmarking parameter choices.
• Gene List Enrichment Methods: Implements multiple gene list enrichment techniques to evaluate the presence and significance of marker genes within clusters.
• Exploratory Data Analysis Functions: Provides functions to examine cluster-level expression and marker distributions to inform annotation decisions.

Scientific Applications:

• Identifying Novel Cell Types: Enables detection of putative novel cell types by comparing cluster profiles against diverse reference datasets.
• Validating Experimental Results: Allows cross-validation of cluster annotations against established gene expression profiles and marker lists.
• Facilitating Multi-Omics Integration: Supports incorporation of varied expression data types to aid integration of scRNA-seq with other omics datasets.

Methodology:

Data integration: incorporating external gene expression profiles and marker lists into analyses. Correlation analysis: matching scRNA-seq clusters with reference datasets using correlation metrics and benchmarking of parameters. Enrichment analysis: applying gene list enrichment methods to assess and refine cell type assignments based on marker genes.