Exploring Single-Cell Data with Deep Multitasking Neural Networks

AbstractBiomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.A well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

Download Full-text

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

ACM Transactions on Graphics ◽

10.1145/3450284 ◽

2021 ◽

Vol 40 (3) ◽

pp. 1-13

Author(s):

Lumin Yang ◽

Jiajie Zhuang ◽

Hongbo Fu ◽

Xiangzhi Wei ◽

Kun Zhou ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Large Scale ◽

State Of The Art ◽

Semantic Segmentation ◽

Structure Information ◽

Graph Neural Networks ◽

Node Labels ◽

Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

Download Full-text

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Download Full-text

High-throughput single cell data analysis – A tutorial

Analytica Chimica Acta ◽

10.1016/j.aca.2021.338872 ◽

2021 ◽

pp. 338872

Author(s):

Gerjen H. Tinnevelt ◽

Kristiaan Wouters ◽

Geert J. Postma ◽

Rita Folcarelli ◽

Jeroen J. Jansen

Keyword(s):

Data Analysis ◽

Single Cell ◽

High Throughput ◽

Cell Data

Download Full-text

Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network

Nature Communications ◽

10.1038/s41467-020-20365-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Changming Wu ◽

Heshan Yu ◽

Seokhyeong Lee ◽

Ruoming Peng ◽

Ichiro Takeuchi ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Phase Change ◽

Convolutional Neural Network ◽

Large Scale ◽

Phase Change Materials ◽

Refractive Index Change ◽

Optical Computing ◽

Machine Learning Algorithms ◽

Matrix Vector Multiplication

AbstractNeuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.

Download Full-text

EpiScanpy: integrated single-cell epigenomic analysis

10.1101/648097 ◽

2019 ◽

Cited By ~ 4

Author(s):

Anna Danese ◽

Maria L. Richter ◽

David S. Fischer ◽

Fabian J. Theis ◽

Maria Colomé-Tatché

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Large Scale ◽

Feature Space ◽

Rna Seq ◽

Computational Framework ◽

Learning Techniques ◽

Multiple Feature ◽

The Many ◽

Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

Download Full-text

Distinguishing different modes of growth using single-cell data

eLife ◽

10.7554/elife.72565 ◽

2021 ◽

Vol 10 ◽

Author(s):

Prathitha Kar ◽

Sriram Tiruvadi-Krishnan ◽

Jaana Männik ◽

Jaan Männik ◽

Ariel Amir

Keyword(s):

Data Analysis ◽

Single Cell ◽

Statistical Methods ◽

Synthetic Data ◽

Cellular Growth ◽

Biological Mechanisms ◽

E Coli ◽

High Throughput Data ◽

Consistent Method ◽

Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Download Full-text

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

Briefings in Bioinformatics ◽

10.1093/bib/bbab368 ◽

2021 ◽

Author(s):

Xiangtao Li ◽

Shaochuan Li ◽

Lei Huang ◽

Shixiong Zhang ◽

Ka-chun Wong

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Single Cell ◽

Deep Neural Networks ◽

Expression Profiles ◽

Marker Gene ◽

Gene Expression Profiles ◽

Underlying Mechanisms ◽

Cell Data ◽

Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

Download Full-text

Extensive deep neural networks for transferring small scale learning to large scale systems

Chemical Science ◽

10.1039/c8sc04578j ◽

2019 ◽

Vol 10 (15) ◽

pp. 4129-4140 ◽

Cited By ~ 9

Author(s):

Kyle Mills ◽

Kevin Ryczko ◽

Iryna Luchak ◽

Adam Domurad ◽

Chris Beeler ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Large Scale ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Small Scale ◽

Large Scale Systems ◽

Energy Entropy ◽

Large Systems ◽

Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

Download Full-text

Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

BioMed Research International ◽

10.1155/2015/958302 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Andreas Friedrich ◽

Erhan Kenar ◽

Oliver Kohlbacher ◽

Sven Nahnsen

Keyword(s):

Big Data ◽

Experimental Design ◽

High Throughput ◽

Large Scale ◽

Integrated Design ◽

Added Value ◽

Biomedical Data ◽

Data Generation ◽

Web Based ◽

Data Annotation

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

Download Full-text

Replicator degrees of freedom allow publication of misleading failures to replicate

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910951116 ◽

2019 ◽

Vol 116 (51) ◽

pp. 25535-25545 ◽

Cited By ~ 6

Author(s):

Christopher J. Bryan ◽

David S. Yeager ◽

Joseph M. O’Brien

Keyword(s):

Data Analysis ◽

Experimental Design ◽

Degrees Of Freedom ◽

Large Scale ◽

Current Practice ◽

False Negative ◽

Ongoing Debate ◽

Key Dimensions ◽

Using Data

In recent years, the field of psychology has begun to conduct replication tests on a large scale. Here, we show that “replicator degrees of freedom” make it far too easy to obtain and publish false-negative replication results, even while appearing to adhere to strict methodological standards. Specifically, using data from an ongoing debate, we show that commonly exercised flexibility at the experimental design and data analysis stages of replication testing can make it appear that a finding was not replicated when, in fact, it was. The debate that we focus on is representative, on key dimensions, of a large number of other replication tests in psychology that have been published in recent years, suggesting that the lessons of this analysis may be far reaching. The problems with current practice in replication science that we uncover here are particularly worrisome because they are not adequately addressed by the field’s standard remedies, including preregistration. Implications for how the field could develop more effective methodological standards for replication are discussed.

Download Full-text