bigSCale: an analytical framework for big-scale single-cell data

Abstract

Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.[Supplemental material is available for this article.]Single-cell RNA sequencing (scRNA-seq) is at the forefront of techniques to chart molecular properties of individual cells. Recent microfluidic-based methods are scalable to tens of thousands of cells, enabling an unbiased sampling and in-depth characterization without prior knowledge Macosko et al. 2015;Zheng et al. 2017). Consequently, studies are less confined by the number of cells and aim to produce comprehensive cellular atlases of entire tissues, organs, and organisms (Regev et al. 2017). Increasing cell numbers, however, generate extremely large data sets, which extend processing time and challenge computing resources. Current scRNA-seq analysis tools are not designed to analyze data sets larger than thousands of cells and often lack sensitivity and specificity to identify marker genes for cell populations or experimental conditions.To address the challenges of large scRNA-seq data sets, we developed bigSCale, an analytical framework for the sensitive detection of population markers and differentially expressed genes, being scalable to analyze mil...

Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing

“…The expression estimates of 112,593 isoforms are provided by the Conquer project (Soneson and Robinson, 2018). • Neuronal progenitor cells (NPCs) also form two groups, one from the patient and the other from a healthy donor (Iacono et al, 2018), 360 cells in each group. The expression estimates of 41,020 genes are provided by the bigSCale project (Iacono et al, 2018).…”

“…The third real data set (NPCs) is a subset of GSE102934 data from the NCBI Gene Expression Omnibus (Iacono et al, 2018). This data set has 720 NPCs derived from induced pluripotent stem (iPS) cells, half of which are from a Williams-Beuren patient and the other half are from a healthy donor.…”

Understanding tumor ecosystems by single-cell sequencing: promises and limitations

“…For large programs, e.g., the Human Cell Atlas project [ 148 ], the volume of data demands more robust computer hardware and software. Although a few down-sampling or convolution-based methods have been proposed to manage large-scale scRNA-seq data for clustering and differential expression analysis [ 149 – 151 ], efficient and effective algorithms are of pressing need to circumvent these difficulties.…”

Network modeling of single-cell omics data: challenges, opportunities, and progresses

“…The second category of methods focuses on modeling GRNs within-cell populations without considering cell trajectories or dynamics. These methods include coexpression and TF-based [88,94,95], coexpression and TF-independent [89,90,109], and information theory [91] (Table 1 and Figure 1B). This is in line with the basic concepts underlying GRN modeling of gene-gene interactions for a tissue, except here single-cell data are modeled for specific cell populations.…”

“…The former calculates pairwise Spearman rank correlations between all sets of genes across cells within a cell type to infer cell-type GRNs in hematopoiesis, and significant pairwise associations were identified using the odds ratio of linearly transformed expression data. Iacono et al [109] used a Pearson correlation-based method which first transforms the expression values using bigSCale to derive a z-score for each gene using a probabilistic model to account for noise and variability inherent to single-cell data. Pairwise correlations of z-scores are used to construct GRNs.…”