scholarly journals Predictive modelling using pathway scores: robustness and significance of pathway collections

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcelo P. Segura-Lepe ◽  
Hector C. Keun ◽  
Timothy M. D. Ebbels
Keyword(s):  
Gene Expression ◽  
Prediction Models ◽  
Disease Status ◽  
Predictive Modelling ◽  
Data Set ◽  
Prediction Rules ◽  
Similar Accuracy ◽  
Robust To Noise ◽  
Pathway Databases ◽  
Gene Expression Levels

Abstract Background Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. Results Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. Conclusions Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

Normalization for Two-channel Microarray Data

2005 ◽  
Vol 44 (03) ◽  
pp. 418-422 ◽  
Author(s):  
C. Ittrich
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Single Channel ◽  
Single Channels ◽  
Data Set ◽  
Microarray Experiments ◽  
Normalization Methods ◽  
Sources Of Variation ◽  
Many Sources ◽  
Gene Expression Levels

Summary Objectives: In two-channel microarray experiments the measured gene expression levels are affected by many sources of systematic variation. Normalization refers to the process of removing such systematic sources of variation, to make measured intensities within and between slides comparable. Some commonly used normalization methods removing intensity-dependent dye bias and adjusting differences in variability between slides will be reviewed with the main focus on intensity-dependent normalization methods. Methods: This article describes different intensity-dependent within-slide normalization methods for the log ratios of red and green channel intensities but also refers to single channel normalization methods incorporating all single channels of the slides at once. Results: The described procedures provide a useful approach to remove systematic sources of variation like intensity-dependent dye bias and variability between slides in cDNA microarray experiments. This is illustrated by an experimental data set. Conclusions: Several reasonable normalization procedures for two-channel microarray data have recently been proposed. Deciding on which method would perform well for a concrete experiment is difficult. Designed spike-in experiments or dilution series with known differences for some selected genes would be helpful to assess the different methods, but may be impractical for most laboratories due to the high costs.

Optimizing the use of gene expression data to predict plant metabolic pathway memberships

2020 ◽  
Author(s):  
Peipei Wang ◽  
Bethany M. Moore ◽  
Sahra Uygun ◽  
Melissa D. Lehti-Shiu ◽  
Cornelius S. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Pathway ◽  
Prediction Models ◽  
Error Rates ◽  
Training Data ◽  
Plant Metabolites ◽  
Pathway Annotation ◽  
Pathway Expression ◽  
Gene Expression Levels ◽  
Better Than

AbstractPlant metabolites produced via diverse pathways are important for plant survival, human nutrition and medicine. However, the pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilizing >600 expression values and similarity data combinations from tomato, three strategies for predicting membership in 85 pathways were explored: naive prediction (identifying pathways with the most similarly expressed genes), unsupervised and supervised learning. Optimal predictions for different pathways require distinct data combinations that, in some cases, are indicative of biological processes relevant to pathway functions. Naive prediction produced higher error rates compared with machine learning methods. In 52 pathways, unsupervised learning performed better than a supervised approach, which may be due to the limited availability of training data. Furthermore, using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on gene expression levels. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximize the accuracy of metabolic pathway membership assignment. We anticipate that the prediction framework outlined here can be applied to other species and also be used to improve plant pathway annotation.

scholarly journals The Genetic Architecture of Gene Expression Levels in Wild Baboons

2014 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Nonhuman Primates ◽  
Rna Seq ◽  
Gene Expression Variation ◽  
Data Set ◽  
Expression Levels ◽  
Expression Variation ◽  
Wild Baboons ◽  
Gene Expression Levels

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

scholarly journals The genetic architecture of gene expression levels in wild baboons

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Eqtl Mapping ◽  
Rna Seq ◽  
Specific Expression ◽  
Data Set ◽  
Expression Levels ◽  
Trait Locus ◽  
Eqtl Data ◽  
Gene Expression Levels

Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

scholarly journals Ampliconic Genes on the Great Ape Y Chromosomes: Rapid Evolution of Copy Number but Conservation of Expression Levels

2020 ◽  
Vol 12 (6) ◽  
pp. 842-859
Author(s):  
Rahulsimham Vegesna ◽  
Marta Tomaszkiewicz ◽  
Oliver A Ryder ◽  
Rebeca Campos-Sánchez ◽  
Paul Medvedev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Y Chromosome ◽  
Copy Number ◽  
Gene Families ◽  
Great Apes ◽  
Published Data ◽  
Great Ape ◽  
Data Set ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Multicopy ampliconic gene families on the Y chromosome play an important role in spermatogenesis. Thus, studying their genetic variation in endangered great ape species is critical. We estimated the sizes (copy number) of nine Y ampliconic gene families in population samples of chimpanzee, bonobo, and orangutan with droplet digital polymerase chain reaction, combined these estimates with published data for human and gorilla, and produced genome-wide testis gene expression data for great apes. Analyzing this comprehensive data set within an evolutionary framework, we, first, found high inter- and intraspecific variation in gene family size, with larger families exhibiting higher variation as compared with smaller families, a pattern consistent with random genetic drift. Second, for four gene families, we observed significant interspecific size differences, sometimes even between sister species—chimpanzee and bonobo. Third, despite substantial variation in copy number, Y ampliconic gene families’ expression levels did not differ significantly among species, suggesting dosage regulation. Fourth, for three gene families, size was positively correlated with gene expression levels across species, suggesting that, given sufficient evolutionary time, copy number influences gene expression. Our results indicate high variability in size but conservation in gene expression levels in Y ampliconic gene families, significantly advancing our understanding of Y-chromosome evolution in great apes.

scholarly journals Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

2017 ◽  
Author(s):  
John D. Blischak ◽  
Ludovic Tailleux ◽  
Marsha Myrthil ◽  
Cécile Charlois ◽  
Emmanuel Bergot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Healthy Individuals ◽  
Data Set ◽  
Expression Levels ◽  
Infected Cells ◽  
Latent Tb ◽  
Gene Expression Levels

ABSTRACTTuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the world’s population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

scholarly journals Identification of deregulation mechanisms specific to cancer subtypes

2021 ◽  
Vol 19 (01) ◽  
pp. 2140003
Author(s):  
Magali Champion ◽  
Julien Chiquet ◽  
Pierre Neuvial ◽  
Mohamed Elati ◽  
François Radvanyi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Data Set ◽  
Cancer Subtypes ◽  
Cancer Data ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Gene Expression Levels

In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy is based on the comparison between tumoral and healthy cells. First, we use gene expression data from healthy cells to infer a reference gene regulatory network. Then, we compare it with gene expression levels in tumor samples to detect deregulated target genes. We finally measure the ability of each transcription factor to explain these deregulations. We apply our method on a public bladder cancer data set derived from The Cancer Genome Atlas project and confirm that it captures hallmarks of cancer subtypes. We also show that it enables the discovery of new potential biomarkers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Alternative splicing during mammalian organ development

2021 ◽  
Author(s):  
Pavel V. Mazin ◽  
Philipp Khaitovich ◽  
Margarida Cardoso-Moreira ◽  
Henrik Kaessmann
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Regulatory Networks ◽  
Organ Development ◽  
Functional Diversification ◽  
Mammalian Genomes ◽  
Species Comparisons ◽  
Time Specific ◽  
The Brain ◽  
Gene Expression Levels

AbstractAlternative splicing (AS) is pervasive in mammalian genomes, yet cross-species comparisons have been largely restricted to adult tissues and the functionality of most AS events remains unclear. We assessed AS patterns across pre- and postnatal development of seven organs in six mammals and a bird. Our analyses revealed that developmentally dynamic AS events, which are especially prevalent in the brain, are substantially more conserved than nondynamic ones. Cassette exons with increasing inclusion frequencies during development show the strongest signals of conserved and regulated AS. Newly emerged cassette exons are typically incorporated late in testis development, but those retained during evolution are predominantly brain specific. Our work suggests that an intricate interplay of programs controlling gene expression levels and AS is fundamental to organ development, especially for the brain and heart. In these regulatory networks, AS affords substantial functional diversification of genes through the generation of tissue- and time-specific isoforms from broadly expressed genes.

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