A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Pei-Hsun Wu, Denis Wirtz, Jude M. Phillip, Kyu-Sang Han, Wei-Chiang Chen

Published: 2021-01-10 DOI: 10.1038/s41596-020-00432-x

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Data 1

Example input files for the protocol procedures, including fluorescence images of phalloidin-stained mouse embryonic fibroblast, their segmented images, CellProfiler segmentation workflow pipeline file, lists of segmented images for building and applying the model in CSV format, and example output files generated during the analysis.

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

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References

Supplementary information

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A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Supplementary information

Supplementary Information

Reporting Summary

Supplementary Data 1

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