scholarly journals Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

2015 ◽  
Author(s):  
Andrew Anand Brown ◽  
Zhihao Ding ◽  
Ana Viñuela ◽  
Dan Glass ◽  
Leopold Parts ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Expression Levels ◽  
Biologically Relevant ◽  
Kegg Pathways ◽  
Analysis Methods ◽  
Gene Expression Levels

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38E-5). These phenotypes are more heritable (h^2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

Bayesian functional mixed-effects models with grouped smoothness for analyzing time-course gene expression data

2020 ◽  
Vol 15 ◽  
Author(s):  
Shangyuan Ye ◽  
Ye Liang ◽  
Bo Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Penalized Splines ◽  
Expression Data ◽  
Expression Levels ◽  
The Mean ◽  
Gene Expression Levels

Objective: As a result of the development of microarray technologies, gene expression levels of thousands of genes involved in a given biological process can be measured simultaneously, and it is important to study their temporal behavior to understand their mechanisms. Since the dependence between gene expression levels over time for a given gene is often too complicated to model parametrically, sparse functional data analysis has received an increasing amount of attention for analyzing such data. Methods: We propose a new functional mixed-effects model for analyzing time-course gene expression data. Specifically, the model groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed-effects model representation of penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models was developed, and Bayesian computation was implemented by using Markov chain Monte Carlo methods. Results: The performance of our new model was studied by two simulation studies and illustrated using a yeast cell cycle gene expression dataset. Simulation results suggest that our proposed methods can outperform the previously used methods in terms of the mean integrated squared error. The yeast gene expression data application suggests that the proposed model with two latent groups should be used on this dataset.

Inference of genetic networks using random forests: Assigning different weights for gene expression data

2019 ◽  
Vol 17 (04) ◽  
pp. 1950015 ◽  
Author(s):  
Shuhei Kimura ◽  
Masato Tokuhisa ◽  
Mariko Okada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Genetic Network ◽  
Expression Data ◽  
Informative Gene ◽  
Inference Method ◽  
Expression Levels ◽  
Time Series Experiment ◽  
Gene Expression Levels

In using gene expression levels for genetic network inference, we believe that two measurements that are similar to each other are less informative than two measurements that differ from each other. Given, for example, that gene expression levels measured at two adjacent time points in a time-series experiment are often similar to each other, we assume that each measurement in the time-series experiment will be less informative than each measurement in a steady-state experiment. Based on this idea, we propose a new inference method that relies heavily on informative gene expression data. Through numerical experiments, we prove that the quality of an inferred genetic network is slightly improved by heavily weighting informative gene expression data. In this study, we develop a new method by modifying the existing random-forest-based inference method to take advantage of its ability to analyze both time-series and static gene expression data. The idea we propose can be similarly applied to many of the other existing inference methods, as well.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

scholarly journals Uncovering Potential Therapeutic Targets in Colorectal Cancer by Deciphering Mutational Status and Expression of Druggable Oncogenes

Cancers ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 983 ◽  
Author(s):  
Otília Menyhart ◽  
Tatsuhiko Kakisaka ◽  
Lőrinc Sándor Pongor ◽  
Hiroyuki Uetake ◽  
Ajay Goel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gene Expression Data ◽  
Drug Targets ◽  
Therapeutic Targets ◽  
Independent Set ◽  
Driver Mutations ◽  
Expression Data ◽  
Expression Levels ◽  
Mutational Status

Background: Numerous driver mutations have been identified in colorectal cancer (CRC), but their relevance to the development of targeted therapies remains elusive. The secondary effects of pathogenic driver mutations on downstream signaling pathways offer a potential approach for the identification of therapeutic targets. We aimed to identify differentially expressed genes as potential drug targets linked to driver mutations. Methods: Somatic mutations and the gene expression data of 582 CRC patients were utilized, incorporating the mutational status of 39,916 and the expression levels of 20,500 genes. To uncover candidate targets, the expression levels of various genes in wild-type and mutant cases for the most frequent disruptive mutations were compared with a Mann–Whitney test. A survival analysis was performed in 2100 patients with transcriptomic gene expression data. Up-regulated genes associated with worse survival were filtered for potentially actionable targets. The most significant hits were validated in an independent set of 171 CRC patients. Results: Altogether, 426 disruptive mutation-associated upregulated genes were identified. Among these, 95 were linked to worse recurrence-free survival (RFS). Based on the druggability filter, 37 potentially actionable targets were revealed. We selected seven genes and validated their expression in 171 patient specimens. The best independently validated combinations were DUSP4 (p = 2.6 × 10−12) in ACVR2A mutated (7.7%) patients; BMP4 (p = 1.6 × 10−04) in SOX9 mutated (8.1%) patients; TRIB2 (p = 1.35 × 10−14) in ACVR2A mutated patients; VSIG4 (p = 2.6 × 10−05) in ANK3 mutated (7.6%) patients, and DUSP4 (p = 7.1 × 10−04) in AMER1 mutated (8.2%) patients. Conclusions: The results uncovered potentially druggable genes in colorectal cancer. The identified mutations could enable future patient stratification for targeted therapy.

ENTROPY-BASED CLUSTER VALIDATION AND ESTIMATION OF THE NUMBER OF CLUSTERS IN GENE EXPRESSION DATA

2012 ◽  
Vol 10 (05) ◽  
pp. 1250011
Author(s):  
NATALIA NOVOSELOVA ◽  
IGOR TOM
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
Biological Knowledge ◽  
Consensus Clustering ◽  
Expression Data ◽  
Cluster Validation ◽  
Number Of Clusters ◽  
Validity Measure

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selection procedure to determine the suitable number of clusters. The validity measure is based on the estimation of the "clearness" of the consensus matrix, which is the result of a resampling clustering scheme or consensus clustering. According to the proposed selection procedure the stable clustering result is determined with the reference to the validity measure for the null hypothesis encoding for the absence of clusters. The final number of clusters is selected by analyzing the distance between the validity plots for initial and permutated data sets. We applied the selection procedure to estimate the clustering results on several datasets. As a result the proposed procedure produced an accurate and robust estimate of the number of clusters, which are in agreement with the biological knowledge and gold standards of cluster quality.

scholarly journals Inferring time-lagged causality using the derivative of single-cell expression

2021 ◽  
Author(s):  
Huan-Huan Wei ◽  
Hui Lu ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Single Cell ◽  
Causal Relationship ◽  
Gene Expression Data ◽  
Expression Data ◽  
Causal Relationships ◽  
Expression Levels ◽  
Gene Pairs ◽  
Time Lagged

AbstractMany computational methods have been developed for inferring causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships, with many of these inferred results supported by the literature.Author summaryComputational causal inference is a promising way to survey causal relationships between genes efficiently. Though many causal inference methods have been applied to gene expression data, none considers the time-lagged causal relationship, which means that some genes may take some time to affect their target genes with several reactions. If relationships between genes are time-lagged, the existing methods’ assumptions will be violated. The relationships will be challenging to recognize. We demonstrate that this is indeed the case through simulation. Therefore, we develop a method for inferring time-lagged causal relationships of single-cell gene expression data. We assume that a time-lagged causal relationship should present a strong association between the cause and the effect’s changing. To calculate such correlation, we first estimate the derivative of gene expression using the information from unspliced transcripts. Then, we use conditional independent tests to search gene pairs satisfying our assumption. Our results suggest that we could accurately infer time-lagged causal gene pairs validated by published literature. This method may complement gene regulatory analysis and provide candidate gene pairs for further controlled experiments.

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Suyan Tian ◽  
Chi Wang ◽  
Bing Wang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Selection Process ◽  
Biological Knowledge ◽  
Expression Data ◽  
Selection Methods ◽  
Its Gene ◽  
Active Research

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.

scholarly journals Putative biomarkers for predicting tumor sample purity based on gene expression data

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Adrienna Bingham ◽  
Qi-Jing Li ◽  
Yuan Zhuang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Supervised Machine Learning ◽  
Tumor Type ◽  
Expression Data ◽  
Expression Levels ◽  
Gene Set ◽  
Tumor Purity ◽  
Tumor Types ◽  
Cancerous Cells

Abstract Background Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor. Methods We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. Results Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity. Conclusions Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.

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