Motivation New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. Results We introduce a highly scalable graph-based clustering algorithm PARC—Phenotyping by Accelerated Refined Community-partitioning—for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. Availability and implementation https://github.com/ShobiStassen/PARC. Supplementary information Supplementary data are available at Bioinformatics online.

Cellular biophysical properties are the effective label‐free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell‐based assays that involve large‐scale single‐cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single‐cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label‐free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi‐ATOM, that enables label‐free single‐cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s—a capability that is otherwise inaccessible in current QPI. With the aim to translate multi‐ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high‐dimensional single‐cell biophysical phenotypic profiles at ultra‐large‐scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label‐free statistical power to classify multiple human leukemic cell types at high accuracy (~92–97%). This system could substantiate the significance of high‐throughput QPI flow cytometry in enabling next frontier in large‐scale image‐derived single‐cell analysis applied in biological discovery and cost‐effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry

The association of the intrinsic optical and biophysical properties of cells to the homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly...

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