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

Single-cell RNA sequencing significantly deepened our insights into complex tissues and latest techniques are capable to analyze ten-thousands of cells simultaneously. With bigSCale, we provide an analytical framework being scalable to analyze millions of cells, addressing challenges of future large data sets. Unlike other methods, bigSCale does not constrain data to fit an a priori-defined distribution and instead uses an accurate numerical model of noise. We evaluate the performance of bigSCale using a biological model of aberrant gene expression and simulated data sets, which underlined its speed and accuracy. We further apply bigSCale to analyze 1.3 million cells from the mouse developing forebrain. The directed down-sampling strategy 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 data sets. Overall design: Single cell transcriptomes from 1847 neuronal progenitors differentiated from iPSC of one healthy donor, two patients with Williams-Beuren syndrome and two patients with 7q11.23 microduplication syndrome.