Extended Data Fig. 2: Computational modeling of microfluidic chip loading and of barcode collisions.

a, Droplet overloading boosts the percentage of droplets filled with nuclei for the scATAC v1.1 Next GEM microfluidic chip. b, Droplet overloading on the scATAC v1.1 Next GEM chip increases the average number of nuclei per droplet in a controlled fashion, while maintaining the desired Poisson-like loading distribution. c, Expected collision rates on the Next GEM chip as a function of the loaded number of cells or nuclei per channel for standard droplet-based scRNA-seq and for scifi-RNA-seq with different numbers of round1 barcodes. The cell/nuclei fill rate was modeled as a zero-inflated Poisson distribution. d-f, Modeling of the microfluidic device loading using alternative distributions (Negative Binomial, Poisson, Zero Inflated Negative Binomial, Zero Inflated Poisson). The number of loaded nuclei is plotted against the number of nuclei per droplet on a linear scale (panel d), logarithmic scale (panel e) and as point estimates (panel f). g, Statistical properties of the distribution of nuclei per droplet across experiments. The relationship between mean and variance that is expected for a Poisson distribution is indicated by gray lines. h, Computational modeling of droplet loading as a zero-inflated Poisson function. i, Posterior probability distributions of lambda and psi sampled using a Markov Chain Monte Carlo (MCMC) analysis. j, Independent estimation of the cell doublet rates using Monte Carlo simulations. Error bars in panels d, e, h, and j indicate three standard deviations around the mean.