cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data

Abstract

Summary

The 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression of genes in individual cells due to the massive data volume. Here, we present cellCounts, a new tool for efficient and accurate quantification of Chromium data. cellCounts employs the seed-and-vote strategy to align reads to a reference genome, collapses reads to Unique Molecular Identifiers (UMIs) and then assigns UMIs to genes based on the featureCounts program. Using both simulation and real datasets for evaluation, cellCounts was found to compare favourably to cellRanger and STARsolo. cellCounts is implemented in R, making it easily integrated with other R programs for analysing Chromium data.

Availability and implementation

cellCounts was implemented as a function in R package Rsubread that can be downloaded from http://bioconductor.org/packages/release/bioc/html/Rsubread.html. Data and analysis code used in this study can be freely accessed via La Trobe University’s Institutional Repository at https://doi.org/10.26181/21588276.

1 Introduction

The advent of single-cell RNA sequencing (scRNA-seq) technologies has fundamentally transformed biomedical research landscape. scRNA-seq data need to be quantified before downstream analyses can be performed, such as cell clustering, cell type identification, and differential gene expression analysis. Quantifying scRNA-seq data are more complex than quantifying bulk RNA-seq data because in addition to determining the origin of sequence reads, quantification of scRNA-seq data also requires identification of cells and Unique Molecular Identifiers (UMIs). Furthermore, scRNA-seq quantification is more computing intensive due to much larger number of reads generated for a sample. The 10x Genomics Chromium scRNA-seq technology is currently the dominant scRNA-seq platform.

There are two main strategies developed for quantifying data generated by this platform. The first strategy aligns reads to a reference genome and then assigns reads and UMIs to genes in each cell. Examples of methods adopting this strategy include CellRanger (Zheng et al. 2017) and STARsolo (Kaminow et al. 2022). The second strategy uses an alignment-free approach to find transcripts compatible with read/UMI sequences and then assign UMIs to transcripts based on a probabilistic model. Representative methods using this strategy include kallisto|bustools (Melsted et al. 2021) and alevin-fry (He et al. 2022). In this study, we focus on the alignment-based strategy for quantifying Chromium data.

We present a new Chromium quantification tool called cellCounts. cellCounts was developed based on the seed-and-vote read mapping paradigm (Liao et al. 2013, Liao et al. 2019) and featureCounts read assignment algorithm (Liao et al. 2014, Liao et al. 2019). cellCounts was implemented as an R function as part of the Bioconductor/R package Rsubread, allowing Chromium quantification to be carried out within the user-friendly R environment. cellCounts can work seamlessly with many popular Chromium data analysis R packages to create an R pipeline for a complete analysis of a Chromium dataset. With cellCounts, scRNA-seq quantification can be carried out with a single function call (after an index was generated for a reference genome which is a one-off operation). Using both simulation data and real datasets, we demonstrate that the speed and accuracy of cellCounts are comparable to, or better than, those of cellRanger and STARsolo, popular programs developed for quantifying scRNA-seq data.

2 Methods

2.1 cellCounts design

cellCounts starts with processing raw reads generated by a sequencer and finishes with outputting UMI counts for each gene in each cell. cellCounts is able to take both BCL and FASTQ format reads as input. When input format is BCL, cellCounts directly processes reads from the raw data files instead of converting them into FASTQ reads before processing. This avoids the costs of format conversion and disk operations involved in saving and retrieving FASTQ reads. This direct processing of BCL reads could save >20% of cellCounts’s running time. It also simplifies the analysis as users do not need to convert BCL reads to FASTQ reads which is required by other tools such as CellRanger.

cellCounts adapted the seed-and-vote aligner Subread for mapping Chromium reads. cellCounts performs more sensitive read mapping than Subread, by using more seeds (15 seeds) to discover candidate mapping locations and by applying a more relaxed voting threshold for calling mapping locations (only requiring a minimum of one vote). This highly sensitive read mapping enables more UMIs to be detected from the scRNA-seq data. cellCounts considers both numbers of matched bases and mismatched bases in each alignment to find the best mapping location. This ensures a high mapping accuracy is achieved. If a read maps to more than one location, the location overlapping a known gene is preferred.

Mapped reads will be assigned to genes in each cell using the featureCounts algorithm. Within each gene, assigned reads that share the same UMI tag (allowing one base mismatch) will be reduced to one UMI. If the same UMI appears in more than one gene, it will be assigned to the gene that has the highest number of reads carrying this UMI tag. cellCounts supports barcode correction and whitelisting. It utilizes a barcode whitelist, which is a list of barcode sequences downloaded from 10x Genomics website that are used by Chromium assay kit, to detect valid barcodes in a Chromium dataset. When matching cell barcodes observed from the data against the barcode whitelist, cellCounts allows for one base mismatch to account for sequencing errors.

After obtaining UMI counts for each gene in each cell, cellCounts will call valid cells using the same algorithm as implemented in CellRanger (version 3.0). Briefly, this algorithm first uses a bootstrap sampling procedure to determine a total-UMI-count cut-off for calling high-confidence cell barcodes. It then builds an expression profile for ambient RNAs based on expression levels of genes in those barcodes that have a very low total UMI count. Lastly, CellRanger will rescue the cell barcodes that fail to meet the total-UMI-count cut-off but have a gene expression profile significantly different from the ambient RNA profile. cellCounts reports both high-confidence cells and rescued cells in the UMI count matrix it generates.

2.2 Simulation

A simulation dataset was generated for this evaluation, based on the CellRanger analysis results of a Chromium dataset including human peripheral blood mononuclear cells (PBMCs) of a healthy female donor. This PBMC dataset was generated by 10x Genomics. The library was prepared using the Chromium Next GEM v3.1 chemistry and sequencing was performed using an Illumina NovaSeq 6000. We downloaded this dataset from 10x Genomics website. We used the quantification statistics observed from the CellRanger quantification results to generate the simulation data. Expression of genes in 10, 000 cells was simulated. Number of genes per cell was subject to a Gamma distribution (k = 4.5, θ = 550), with an average of 1,930 expressed genes included in each cell. UMIs per gene and reads per UMI were subject to Gamma distribution with parameter settings of k = 1, θ = 6 and k = 2, θ = 2, respectively. Approximately 50k reads were generated for each cell on average. Each simulation read includes a technical part and a biological part. The technical part contains a sample index sequence, a cell barcode sequence and a UMI sequence. We downloaded technical sequences from 10x Genomics website and used them for this simulation. The biological part of a simulation read includes a genomic sequence extracted from the 3' end of a gene. This mimics the 3' bias of Chromium Single Cell 3′ GEM library. This genomic sequence is located 350 bases from the very end of the gene on average with a standard deviation of 30 bases. Genomic variants and sequencing errors were introduced to reads using the same approach as described in Liao et al. (2019). Briefly, sequencing quality data were obtained from a real Illumina sequencing dataset. Quality scores included in the data were provided to both technical and biological parts of simulation reads. Substitution errors were then introduced to read bases based on quality scores assigned to the bases. More errors were introduced to the 3' end of simulation reads due to sequencing bias of Illumina technology. Biological variants were only introduced to the biological part of simulation reads. These variants included single nucleotide polymorphisms and short indels, which were introduced at the rates of 0.0009 and 0.0001, respectively.

2.3 Software execution

CellRanger (version 6.0.1), STARsolo (version 2.7.10a) and cellCounts (version 2.14.2) were executed on a supercomputer with 48 Intel Xeon 3.30 GHz CPU cores and 768 GB of memory. Each program was specified to run with 10 CPU threads. Note that the STARsolo program utilizes two internal threads to decompress read files and distribute reads to other threads, resulting in a total of 12 threads used for quantification. As both CellRanger and cellCounts output location-sorted BAM files by default, we also instructed STARsolo to output location-sorted BAM files unless otherwise stated. The pre-built CellRanger reference package provided by 10x Genomics was used for running CellRanger. Reference sequences and gene annotation included in the reference package were also provided to STARsolo and cellCounts for their data quantification.

3 Results

3.1 Speed

We first assessed the speed of cellCounts, CellRanger and STARsolo. Two in-house mouse datasets, including 245 and 379 million reads, respectively, and a public mouse melanoma dataset including 505 million reads (Gene Expression Omnibus database accession GSM4505965), were used in this evaluation. The in-house datasets were generated using Chromium Next GEM v3.1 chemistry. The smaller in-house dataset includes CD45⁺ cells sorted from stomach, small intestine, caecum and colon. The larger in-house dataset includes TCRβ⁺CD8α⁺ and TCRγδ⁺CD8α⁺ cells sorted from colon. The public dataset includes tumour-infiltrating leukocytes sorted from a melanoma sample using Chromium Next GEM v2 chemistry.

Figure 1a shows that both cellCounts and STARsolo are significantly faster than CellRanger. cellCounts is 1.7–3.3 times faster than CellRanger. cellCounts demonstrates comparable speed to STARsolo, while exhibiting better scalability over large data size, as illustrated by the red line in Fig. 1a. Similar comparison results were observed when the output of BAM files is not needed.

Figure 1.

Comparison of speed and accuracy for quantifying 10x Chromium scRNA-seq data. (a) Running time of cellCounts, STARsolo and CellRanger on three real datasets. Number of reads included in each dataset is indicated under each column. These datasets all have a BCL format. Numeric values shown at the bottom of bars for STARsolo and CellRanger indicate the amount of time spent on converting BCL-format reads to FASTQ-format reads. cellCounts’s running time does not include this time because cellCounts directly processes BCL-format reads. (b) Root mean square error (RMSE) of gene expression calculated for cellCounts, STARsolo and CellRanger based on ground truth. For each method, UMI counts were converted to log2-cpm values for each gene in each cell and then used for calculating RMSE. An offset of 0.5 was added to UMI counts to avoid log transformation of zero counts. ‘Simulation’ is a simulation dataset. ‘NCI’ and ‘LLU’ are two real datasets generated from sequencing of two human cell lines mixed at known ratios.

3.2 Accuracy

We then compared the quantification accuracy of the three quantification tools. We first generated a simulation dataset for this evaluation, based on the CellRanger analysis results of a PBMC dataset generated by using 10x Chromium Next GEM v3.1 chemistry. Quantification statistics obtained from CellRanger analysis results were used to set parameters for Gamma distributions used to draw number of genes per cell, number of UMIs per gene and number of reads per UMI. Simulation reads were extracted from 3' end of each gene to mimic the 3' bias of Chromium Single Cell 3′ GEM library. Genomic variants and sequencing errors were also introduced to read sequences. Details of generation of simulation data can be found in Section 2.2.

A UMI count matrix including known expression levels (UMI counts) generated for each gene in each cell in the simulation, was used as the ground truth for assessing quantification accuracy of CellRanger, STARsolo and cellCounts. The ‘Simulation’ column in Fig. 1b shows that cellCounts is more accurate (smaller RMSE) than CellRanger and STARsolo. cellCounts successfully recalled all the cells generated in the simulation, whereas CellRanger and STARsolo failed to call ∼2% of the cells. Cell calling is performed based on gene expression profiles in the cells. The better cell calling result from cellCounts should be due to its read mapping and UMI assignment algorithm, which is the main difference between cellCounts and the other two tools.

We then used two real 10x Chromium datasets, generated by a recent multicentre scRNA-seq benchmarking study (Chen et al. 2021), to compare the quantification accuracy of the three methods. Each dataset contained data generated from a breast cancer cell line sample (sample ‘A’), a normal B lymphocyte cell line sample (sample ‘B’) and a mixture of the two samples. The mix ratio is 95%A:5%B for dataset ‘NCI’ and 90%A:10%B for dataset ‘LLU’. The RMSE quantification error was calculated by comparing expression levels of genes in the mixture sample and sum of expression levels of genes in sample ‘A’ and ‘B’, multiplied by their respective mix ratio. In other words, we examined which quantification method yielded better concordance of gene expression between the in silico mixture sample and the actual mixture sample. Using known mix ratios as truth to assess gene expression accuracy has been demonstrated to be a successful strategy for evaluating the performance of RNA-seq quantification methods (SEQC/MAQC-III Consortium 2014). With this strategy, we found that cellCounts generated more concordant quantification results than CellRanger and STARsolo (‘NCI’ and ‘LLU’ columns in Fig. 1b).

In summary, we have developed a new tool called cellCounts, specifically designed for quantifying 10x Chromium scRNA-seq data in this study. The performance of cellCounts has been evaluated using both simulated and real data, demonstrating its effectiveness. It allows users to perform quantification within the user-friendly R environment and facilitates the creation of an R pipeline for analysing Chromium data, starting from raw reads and leading to final results.

Funding

This work was supported by the Victorian Government Department of Health acting through the Victorian Cancer Agency, Victorian Government Operational Infrastructure Support Program, Australian National Health and Medical Research Council [2011558 and 1185513], Victorian Cancer Agency mid-career and early career fellowships to L.A.M. and D.R., Cure Cancer Australia, Priority Driven Young Investigator Grant [1123388].

Conflict of interest

None declared.