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The cell type identification is among the most important tasks in single-cell RNA-sequencing (scRNA-seq) analysis. Many in silico methods have been developed and can be roughly categorized as either supervised or unsupervised. In this study, A team led by researchers at Emory University investigated the performances of 8 supervised and 10 unsupervised cell type identification methods using 14 public scRNA-seq datasets of different tissues, sequencing protocols and species. The researchers investigated the impacts of a number of factors, including total amount of cells, number of cell types, sequencing depth, batch effects, reference bias, cell population imbalance, unknown/novel cell type, and computational efficiency and scalability. Instead of merely comparing individual methods, they focused on factors’ impacts on the general category of supervised and unsupervised methods.

The workflow of the scRNAIdent  pipeline

scRNAIdent provides a modularized R pipeline tool for automating the evaluation and comparison of cell typing methods in scRNA-seq analysis.

The researchers found that in most scenarios, the supervised methods outperformed the unsupervised methods, except for the identification of unknown cell types. This is particularly true when the supervised methods use a reference dataset with high informational sufficiency, low complexity and high similarity to the query dataset. However, such outperformance could be undermined by some undesired dataset properties investigated in this study, which lead to uninformative and biased reference datasets. In these scenarios, unsupervised methods could be comparable to supervised methods. This study not only explained the cell typing methods’ behaviors under different experimental settings but also provided a general guideline for the choice of method according to the scientific goal and dataset properties. Finally, the evaluation workflow is implemented as a modularized R pipeline that allows future evaluation of new methods.

Availability: All the source codes are available at https://github.com/xsun28/scRNAIdent.

Next-generation sequencing (NGS) can identify mutations in the human genome that cause disease and has been widely adopted in clinical diagnosis. However, the human genome contains many polymorphic, low-complexity, and repetitive regions that are difficult to sequence and analyze. Despite their difficulty, these regions include many clinically important sequences that can inform the treatment of human diseases and improve the diagnostic yield of NGS.

To evaluate the accuracy by which these difficult regions are analyzed with NGS, a team led by researchers at the Garvan Institute of Medical Research built an in silico decoy chromosome, along with corresponding synthetic DNA reference controls, that encode difficult and clinically important human genome regions, including repeats, microsatellites, HLA genes, and immune receptors. These controls provide a known ground-truth reference against which to measure the performance of diverse sequencing technologies, reagents, and bioinformatic tools. Using this approach, the researchers provide a comprehensive evaluation of short- and long-read sequencing instruments, library preparation methods, and software tools and identify the errors and systematic bias that confound our resolution of these remaining difficult regions.

In silico chromosome design and experimental workflow

a The in silico decoy chromosome is designed to incorporate difficult and clinically important features of the human genome. The chromosome is divided into (i) small variants (including SNPs and indels) and simple repeats, (ii) structural variants (including large insertions, deletions, duplications, inversions, and translocations), (iii) HLA genes, and (iv) immune receptor genes. b The schematic diagram illustrates the use of synthetic DNA controls (sequins) and the in silico chromosome during the NGS workflow (upper panel). The range of experimental variables evaluated within this study, including difficult genetic features, library preparation methods, sequencing instruments, and bioinformatic tools, are indicated (lower panel)

This study provides an analytical validation of diagnosis using NGS in difficult regions of the human genome and highlights the challenges that remain to resolve these difficult regions.