scholarly journals Graphic Model-based Gene Regulatory Network Reconstruction using RNA Sequencing Count Data

2019 ◽  
Vol 8 ◽  
pp. 1078-1085
Author(s):  
Liliana Lopez-Kleine ◽  
Cristian Andres Gonzalez-Prieto
Keyword(s):  
Rna Sequencing ◽  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Regulatory Networks ◽  
Graphical Model ◽  
Negative Binomial ◽  
Sequencing Data ◽  
Model Based ◽  
Gene Regulatory

Interactions between genes, such as regulations are best represented by gene regulatory networks (GRN). These are often constructed based on gene expression data. Few methods for the construction of GRN exist for RNA sequencing count data. One of the most used methods for microarray data is based on graphical Gaussian networks. Considering that count data have different distributions, a method assuming RNA sequencing counts distribute Poisson has been proposed recently. Nevertheless, it has been argued that the most likely distribution of RNA sequencing counts is not Poisson due to overdispersion. Therefore, the negative binomial distribution is much more likely. For this distribution, no model-based method for the construction of GRN has been proposed until now. Here, we present a graphical, model-based method for the construction of GRN assuming a negative binomial distribution of the RNA sequencing count data. The R code is available under request. We used the method proposed both on simulated RNA sequencing count data and on real data. The graph is showed, and its descriptive measurements were assessed. They were found some interesting biological conclusions. We confirm that using negative binomial distribution for fitting the model is suitable because RNA sequencing data present overdispersion.

scholarly journals Fitting the truncated negative binomial distribution to count data

2016 ◽  
Vol 23 (3) ◽  
pp. 359-385 ◽  
Author(s):  
Claude Manté ◽  
Saikou Oumar Kidé ◽  
Anne-Francoise Yao-Lafourcade ◽  
Bastien Mérigot
Keyword(s):  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Negative Binomial

scholarly journals A flexible multivariate model for high-dimensional correlated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexander D. Knudson ◽  
Tomasz J. Kozubowski ◽  
Anna K. Panorska ◽  
A. Grant Schissler
Keyword(s):  
Stochastic Model ◽  
Rna Sequencing ◽  
Count Data ◽  
Negative Binomial ◽  
Copula Function ◽  
Multivariate Model ◽  
High Dimensional ◽  
Sequencing Data ◽  
Multivariate Distributions ◽  
Dimensional Simulation

AbstractWe propose a flexible multivariate stochastic model for over-dispersed count data. Our methodology is built upon mixed Poisson random vectors (Y1,…,Yd), where the {Yi} are conditionally independent Poisson random variables. The stochastic rates of the {Yi} are multivariate distributions with arbitrary non-negative margins linked by a copula function. We present basic properties of these mixed Poisson multivariate distributions and provide several examples. A particular case with geometric and negative binomial marginal distributions is studied in detail. We illustrate an application of our model by conducting a high-dimensional simulation motivated by RNA-sequencing data.

scholarly journals Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars

2015 ◽  
Vol 57 (1) ◽  
pp. 1-19
Author(s):  
Norbert Mielenz ◽  
Joachim Spilke ◽  
Eberhard von Borell
Keyword(s):  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Mixed Models ◽  
Aggressive Behaviour ◽  
Negative Binomial ◽  
Linear Mixed Models ◽  
Model Parameters ◽  
Generalised Linear Mixed Models ◽  
Animal Effects

Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.

scholarly journals Single-Cell RNA Sequencing Analysis of the Heterogeneity in Gene Regulatory Networks in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui-Qi Wang ◽  
Wei Zhao ◽  
Hai-Kui Yang ◽  
Jia-Mei Dong ◽  
Wei-Jie Lin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Communication Analysis ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Colorectal cancer (CRC) manifests as gastrointestinal tumors with high intratumoral heterogeneity. Recent studies have demonstrated that CRC may consist of tumor cells with different consensus molecular subtypes (CMS). The advancements in single-cell RNA sequencing have facilitated the development of gene regulatory networks to decode key regulators for specific cell types. Herein, we comprehensively analyzed the CMS of CRC patients by using single-cell RNA-sequencing data. CMS for all malignant cells were assigned using CMScaller. Gene set variation analysis showed pathway activity differences consistent with those reported in previous studies. Cell–cell communication analysis confirmed that CMS1 was more closely related to immune cells, and that monocytes and macrophages play dominant roles in the CRC tumor microenvironment. On the basis of the constructed gene regulation networks (GRNs) for each subtype, we identified that the critical transcription factor ERG is universally activated and upregulated in all CMS in comparison with normal cells, and that it performed diverse roles by regulating the expression of different downstream genes. In summary, molecular subtyping of single-cell RNA-sequencing data for colorectal cancer could elucidate the heterogeneity in gene regulatory networks and identify critical regulators of CRC.

scholarly journals tuxnet : a simple interface to process RNA sequencing data and infer gene regulatory networks

2019 ◽  
Vol 101 (3) ◽  
pp. 716-730 ◽  
Author(s):  
Ryan J. Spurney ◽  
Lisa Van den Broeck ◽  
Natalie M. Clark ◽  
Adam P. Fisher ◽  
Maria A. de Luis Balaguer ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Data ◽  
Simple Interface ◽  
Gene Regulatory

scholarly journals COMPARATIVE STUDY OF CATTLE TICK RESISTANCE USING GENERALIZED LINEAR MIXED MODELS

2019 ◽  
Vol 37 (1) ◽  
pp. 41
Author(s):  
Amanda Marchi MAIORANO ◽  
Thiago Santos MOTA ◽  
Ana Carolina VERDUGO ◽  
Ricardo Antonio da Silva FARIA ◽  
Beatriz Pressi Molina da SILVA ◽  
...  
Keyword(s):  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Mixed Models ◽  
Negative Binomial ◽  
Bos Taurus ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Tick Count ◽  
Tick Resistance

Comparison of tick resistance in Bos taurus indicus (Nelore) and Bos taurus taurus (Simmental and Caracu) subspecies was investigated utilizing generalized linear mixed models (GLMMs) with Poisson and Negative binomial distributions. Nelore animals (NE) are known to present greater resistance than t. taurus. Difference between tick resistance in Simmental (SI) and Caracu (CA) breeds has never been reported previously. Three artificial tick infestations were conducted to evaluate tick resistance in these breeds. The statistic point of the present study was to show alternative models for the evaluation of tick count data, the GLMMs. Analysis for tick resistance by GLMM with Negative binomial distribution has never been assessed previously. The analyses were performed by the use of the PROC GLIMMIX procedure of the SAS program. The results showed that GLMM with Negative binomial distribution is appropriated to evaluate tick count data with excess of zero observations avoiding overdispersion problems. Finally, considering multiple comparisons with the Bonferroni test, different pattern of tick infestation was observed for the studied breeds, suggesting that NE is the most resistant breed followed by CA.

scholarly journals Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

2019 ◽  
Author(s):  
Qiao Wen Tan ◽  
Marek Mutwil
Keyword(s):  
Transcription Factors ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Gene Function ◽  
Credit Card ◽  
Regulatory Networks ◽  
Artemisia Annua ◽  
Sequencing Data ◽  
Gene Regulatory ◽  
Artemisinin Biosynthesis

0.ABSTRACTPrediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as the major bottleneck in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for less than 50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics.HighlightsProcessing of large scale transcriptomic data with affordable single-board computersTranscription factors can be found in the same network as their targetsCo-expression of transcription factors and genes in secondary cell wall biosynthesisCo-expression of transcription factors and genes involved in artemisinin biosynthesis

Random Forest Factorization Reveals Latent Structure in Single Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Boris M. Brenerman ◽  
Benjamin D. Shapiro ◽  
Michael C. Schatz ◽  
Alexis Battle
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Data ◽  
Random Forest Regression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes.

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