Single-cell RNA sequencing significantly deepened our insights into complex tissues and latest techniques are capable processing ten-thousands of cells simultaneously. Increasing cell numbers, however, generate extremely large datasets, extending processing time and challenging computing resources. Current scRNAseq analysis tools are not designed to analyze datasets larger than thousands of cells and often lack sensitivity to identify marker genes. With bigSCale, we provide an analytical framework being scalable to analyze millions of cells, addressing challenges of future large datasets. To handle the noise and sparsity of scRNAseq 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 datasets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using a biological model of aberrant gene expression in patient derived neuronal progenitor cells and simulated datasets, which underlined its speed and accuracy in differential expression analysis. To test its applicability for large datasets, we applied bigSCale to analyze 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 positive Cajal-Retzius neurons, for which we determined a previously not recognized heterogeneity associated to distinct differentiation stages, spatial organization and cellular function. Together, bigSCale presents a perfect solution to address future challenges of large single-cell datasets.

bigSCale: An Analytical Framework for Big-Scale Single-Cell Data