scholarly journals DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Cédric Arisdakessian ◽  
Olivier Poirion ◽  
Breck Yunits ◽  
Xun Zhu ◽  
Lana X. Garmire
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Single Cells ◽  
Rna Seq ◽  
Squared Error ◽  
Study Gene Expression ◽  
Network Method ◽  
The Mean

Abstract Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute.

scholarly journals DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-Seq data

2018 ◽  
Author(s):  
Cedric Arisdakessian ◽  
Olivier Poirion ◽  
Breck Yunits ◽  
Xun Zhu ◽  
Lana X. Garmire
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Deep Neural Network ◽  
Missing Values ◽  
Mean Squared Error ◽  
Single Cells ◽  
Computing Time ◽  
Imputation Methods ◽  
Study Gene Expression ◽  
Dataset Size

BackgroundSingle-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. However, a significant problem of current scRNA-seq data is the large fractions of missing values or “dropouts” in gene counts. Incorrect handling of dropouts may affect downstream bioinformatics analysis. As the number of scRNA-seq datasets grows drastically, it is crucial to have accurate and efficient imputation methods to handle these dropouts.MethodsWe present DeepImpute, a deep neural network based imputation algorithm. The architecture of DeepImpute efficiently uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation.ResultsOverall DeepImpute yields better accuracy than other publicly available scRNA-Seq imputation methods on experimental data, as measured by mean squared error or Pearson’s correlation coefficient. Moreover, its efficient implementation provides significantly higher performance over the other methods as dataset size increases. Additionally, as a machine learning method, DeepImpute allows to use a subset of data to train the model and save even more computing time, without much sacrifice on the prediction accuracy.ConclusionsDeepImpute is an accurate, fast and scalable imputation tool that is suited to handle the ever increasing volume of scRNA-seq data. The package is freely available at https://github.com/lanagarmire/DeepImpute

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

2021 ◽  
Author(s):  
Jiaxing Chen ◽  
Chinwang Cheong ◽  
Liang Lan ◽  
Xin Zhou ◽  
Jiming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Regulatory Networks ◽  
Deep Neural Network ◽  
Neighborhood Context ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

scholarly journals Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xin Wang ◽  
Jane Frederick ◽  
Hongbin Wang ◽  
Sheng Hui ◽  
Vadim Backman ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Transcriptional Repression ◽  
Drug Response ◽  
Transcriptional Activity ◽  
Gene Expression Regulation ◽  
Single Cells ◽  
Rna Seq ◽  
Study Gene Expression

Abstract The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here, we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that cellular mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

scholarly journals Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia

2018 ◽  
Vol 2 (2) ◽  
pp. 169
Author(s):  
Alan Boy Sandy Damanik ◽  
Agung Bimantoro
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Economic Indicator ◽  
Squared Error ◽  
The World ◽  
Artificial Neural Network Method ◽  
World Economic ◽  
Architectural Models ◽  
The Mean

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.

scholarly journals Perceptual Image Super Resolution Using Deep Learning and Super Resolution Convolution Neural Networks (SRCNN)

2020 ◽  
Author(s):  
Nagaraj P ◽  
Muthamilsudar K ◽  
Naga Nehanth S ◽  
Mohammed Shahid R ◽  
Sujith Kumar V
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Convolution Neural Network ◽  
Resolution Image ◽  
Squared Error ◽  
Image Super Resolution ◽  
The Mean ◽  
State Of Art

The main objective of Perceptual Image Super Resolution is to obtain a high resoluted image from a normal low resolution image. The task is very simple that we just want to make a Low firmness appearance into a extraordinary resolution image. To perform this task we have various methods like Classical Approach in which we try to maximize the mean squared error, evaluate by PSNR(Peak-Signal-to-Noise-Ratio). The first method used to perform this operation was SRCNN (Super Resolution Convolution Neural Network) and these days many of them use DRCN and VDSR which are slightly upgraded methods. Another technique used for the purpose of upscaling to get a high resoluted image from normal little resolution image is the state of art by PSNR. This method was a quite simple one in which we take a low determination image as input and place in a convolution neural network(CNN) and produce a high resolution image as the output. In this technique the edges will be clearly defined, but the whole image will be blurred. This method is unable to produce good-looking textures.

scholarly journals A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data

2020 ◽  
Author(s):  
Hui Li ◽  
Cory R. Brouwer ◽  
Weijun Luo
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Clustering Analysis ◽  
Deep Neural Network ◽  
Differential Expression Analysis ◽  
Noise Removal ◽  
Data Recovery ◽  
Single Type ◽  
Rna Seq ◽  
Wide Range

AbstractSingle cell RNA sequencing (scRNA-Seq) has been widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts and need thorough cleaning. The existing denoising and imputation methods largely focus on a single type of noise (i.e. dropouts) and have strong distribution assumptions which greatly limit their performance and application. We designed and developed the AutoClass model, integrating two deep neural network components, an autoencoder and a classifier, as to maximize both noise removal and signal retention. AutoClass is free of distribution assumptions, hence can effectively clean a wide range of noises and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass.

Fault Diagnosis for the Oil System of Aviation Piston Engine Based on BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 1185-1188
Author(s):  
Yan Feng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Mean Squared Error ◽  
Piston Engine ◽  
Squared Error ◽  
Training Samples ◽  
The Mean ◽  
The Given

Introduced the composition and the principle of operation of the oil system of aviation piston engine. Analysed common faults of the oil system including high oil pressure indication,low oil pressure indication, high oil temperature indication and excessive oil consumption.Failure causes for above faults were analysed separately.Symbols were stood for failure modes and failure causes. Constructed the BP neural network.Symbols of failure modes were inputs of the BP neural network,and symbols of failure causes were outputs of the BP neural network.Builded a mapping relationship between failure modes and failure causes by training samples studying.Four training samples were selected based on common faults and fault effects.A given mode was as a input of the network,and by adjusting connection weights and the threshold of every neuron,an ideal result could be gotten.Then other mode was as a input of the network which carried on studying until the epochs was 369,and the mean squared error fast converged and the value of mean squared error was.The failure causes for the given failure mode can be confirmed by this BP neural network.By engineering verification, the BP neural network is applicable to fault diagnosis for oil system of aviation piston engine.

scholarly journals Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response

2021 ◽  
Author(s):  
Xin Wang ◽  
Jane Frederick ◽  
Hongbin Wang ◽  
Sheng Hui ◽  
Vadim Backman ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Drug Response ◽  
Transcriptional Activity ◽  
Gene Expression Regulation ◽  
Single Cells ◽  
Cancer Drug ◽  
Rna Seq ◽  
Study Gene Expression ◽  
Anti Cancer

ABSTRACTThe transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anti-cancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that celluar mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification, and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

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