Reconstructing cell cycle and disease progression using deep learning

We present Scanpy, a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. The Python-based implementation efficiently deals with datasets of more than one million cells and enables easy interfacing of advanced machine learning packages. Code is available from https://github.com/theislab/scanpy.

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“…• Analyzing deep learning results for single-cell images (Eulenberg et al, 2016): scanpy_usage/170529_images.…”

Section: Supplemental Note 2: Scanpy's Analysis Featuresmentioning

confidence: 99%

Scanpy for analysis of large-scale single-cell gene expression data

Wolf

Angerer

2017

Preprint

Self Cite

996

1,425

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“…Imaging flow cytometry provides accurate and high‐throughput characterization of single cells and cell populations with the resolution of microscopy and is promising for a wide range of applications in immunology, cancer, neuroscience, hematology, and the related fields 1–5 . However, such an approach still raises several technological challenges such as the ability to acquire and analyze high‐content image data from many single cells at high speed in real time.…”

mentioning

confidence: 99%

Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics

Adachi¹,

Kawamura²,

Nakagawa³

et al. 2020

Cytometry Pt A

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Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals of cells, thereby relieving a computational bottleneck for real-time data analysis in high-throughput imaging cytometry. In our previous work, we demonstrated that this image-free approach could distinguish cells from two cell lines prepared with the same fluorescence staining method. However, the demonstration using different cell lines could not exclude the possibility that classification was based on non-morphological factors such as the speed of cells in flow, which could be encoded in the compressed signals. In this study, we show that GC can classify cells from the same cell line but with different fluorescence distributions in space, supporting the strength of our image-free approach for accurate morphological cell analysis.

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“…Convolutional neural networks could accurately detect phototoxicity [62] and cell-cycle states [63] from images. An interesting architecture predicts lineage choice from brightfield timecourse imaging of differentiating primary hematopoietic progenitors by combining convolution for individual micrographs with recurrent connections between timepoints [64].…”

Section: Cell and Image Phenotypingmentioning

confidence: 99%

Computational biology: deep learning

Jones

Alasoo

Fishman

et al. 2017

Emerging Topics in Life Sciences

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Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

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Reconstructing cell cycle and disease progression using deep learning

Cited by 70 publications

References 25 publications

Scanpy for analysis of large-scale single-cell gene expression data

Scanpy for analysis of large-scale single-cell gene expression data

Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics

Computational biology: deep learning

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