Method For Expression Analysis Research Articles

RmRNAseq: differential expression analysis for repeated-measures RNA-seq data.

With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs. We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units. We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/tree/master/simulation.

Identification of Hub Genes Associated With Development of Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatics Analysis.

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment.

Identification of stable reference genes for qPCR studies in common wheat (Triticum aestivum L.) seedlings under short-term drought stress

BackgroundQuantitative PCR (qPCR) is one of the most common and accurate methods of gene expression analysis. However, the biggest challenge for this kind of examinations is normalization of the results, which requires the application of dependable internal controls. The selection of appropriate reference genes (RGs) is one of the most crucial points in qPCR data analysis and for correct assessment of gene expression. Because of the fact that many reports indicate that the expression profiles of typically used RGs can be unstable in certain experimental conditions, species or tissues, reference genes with stable expression levels should be selected individually for each experiment. In this study, we analysed a set of ten candidate RGs for wheat seedlings under short-term drought stress. Our tests included five ‘traditional’ RGs (GAPDH, ACT, UBI, TUB, and TEF1) and five novel genes developed by the RefGenes tool from the Genevestigator database.ResultsExpression stability was assessed using five different algorithms: geNorm, NormFinder, BestKeeper, RefFinder and the delta Ct method. In the final ranking, we identified three genes: CJ705892, ACT, and UBI, as the best candidates for housekeeping genes. However, our data indicated a slight variation between the different algorithms that were used. We revealed that the novel gene CJ705892, obtained by means of in silico analysis, showed the most stable expression in the experimental tissue and condition.ConclusionsOur results support the statement, that novel genes selected for certain experimental conditions have a more stable level of expression in comparison to routinely applied RGs, like genes encoding actin, tubulin or GAPDH. Selected CJ705892 gene can be used as a housekeeping gene in the expression analysis in wheat seedlings under short-term drought. The results of our study will be useful for subsequent analyses of gene expression in wheat tissues subjected to drought.

Action unit analysis enhanced facial expression recognition by deep neural network evolution

Facial expression is one of the most powerful and natural signals for human beings conveying their emotional states and intentions. Recently, facial expression recognition from facial cues based on FACS is investigated, where facial expressions can be described by a subset of AUs, and the facial expression categories could be easily extended. The goal of our work is the proposal of an action unit analysis enhanced facial expression recognition system based on evolutional deep learning approach. The main contributions of our work include the following three aspects: (1) the temporal dynamic based 3DLeNets is exploited for video analysis based AUs detection. And a general evolutionary framework is conducted for the deep neural networks optimization. (2) The correlations among AUs and the correlation between AUs and emotions are investigated, and the relationship probability model between AUs and emotions is derived by the concept of discriminative power coefficients. (3) An adaptive subsequence matching algorithm (ASMA) is adopted to measure the similarity between AUs sequences, so that to construct the inference scheme of mapping AUs to emotions. Experimental results proved that the AUs enhanced facial expression recognition system performs well comparing to existing facial expression analysis methods, and each AU has different contribution roles for different facial expressions. It is also found to be more practical than discrete facial expression recognition as most of the facial expressions can be described using a subset of AUs.

ALCAM (CD166) as a gene expression marker for human mesenchymal stromal cell characterisation.

BackgroundHuman mesenchymal stromal cells (MSCs) phenotypically share their positive expression of the International Society for Cell and Gene Therapy (ISCT) markers CD73, CD90 and CD105 with fibroblasts. Fibroblasts are often co-isolated as an unwanted by-product from biopsy and they can rapidly overgrow the MSCs in culture. Indeed, many other surface markers have been proposed, though no unique MSC specific marker has been identified yet. Quantitative PCR (qPCR) is a precise, efficient and rapid method for gene expression analysis. To identify a marker suitable for accurate MSC characterisation, qPCR was exploited.Methods and resultsTwo commercially obtained bone marrow (BM) derived MSCs and an hTERT immortalised BM-MSC line (MSC-TERT) have been cultured for different days and at different oxygen levels before RNA extraction. Together with RNA samples previous extracted from umbilical cord derived MSCs and MSC-TERT cells cultured in 2D or 3D, this heterogeneous sample set was quantitatively analysed for the expression levels of 18 candidate MSC marker genes. The expression levels in MSCs were compared with the expression levels in fibroblasts to verify the differentiation capability of these genes between MSCs and fibroblasts. None of the ISCT markers could differentiate between fibroblasts and MSCs. A total of six other genes (ALCAM, CLIC1, EDIL3, EPHA2, NECTIN2, and TMEM47) were identified as possible biomarkers for accurate identification of MSCs.ConclusionJustified by considerations on expression level, reliability and specificity, Activated-Leukocyte Cell Adhesion Molecule (ALCAM) was the best candidate for improving the biomarker set of MSC identification.

Single-cell sperm transcriptomes and variants from fathers of children with and without autism spectrum disorder

The human sperm is one of the smallest cells in the body, but also one of the most important, as it serves as the entire paternal genetic contribution to a child. Investigating RNA and mutations in sperm is especially relevant for diseases such as autism spectrum disorders (ASD), which have been correlated with advanced paternal age. Historically, studies have focused on the assessment of bulk sperm, wherein millions of individual sperm are present and only high-frequency variants can be detected. Using 10× Chromium single-cell sequencing technology, we assessed the transcriptome from >65,000 single spermatozoa across six sperm donors (scSperm-RNA-seq), including two who fathered multiple children with ASD and four fathers of neurotypical children. Using RNA-seq methods for differential expression and variant analysis, we found clusters of sperm mutations in each donor that are indicative of the sperm being produced by different stem cell pools. Finally, we have shown that genetic variations can be found in single sperm.

ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic profiling of individual cells under different biological conditions. scRNA-seq data have two unique challenges that can affect the sensitivity and specificity of single-cell differential expression analysis: a large proportion of expressed genes with zero or low read counts ('dropout' events) and multimodal data distributions. We have developed a zero-inflation-adjusted quantile (ZIAQ) algorithm, which is the first method to account for both dropout rates and complex scRNA-seq data distributions in the same model. ZIAQ demonstrates superior performance over several existing methods on simulated scRNA-seq datasets by finding more differentially expressed genes. When ZIAQ was applied to the comparison of neoplastic and non-neoplastic cells from a human glioblastoma dataset, the ranking of biologically relevant genes and pathways showed clear improvement over existing methods. ZIAQ is implemented in the R language and available at https://github.com/gefeizhang/ZIAQ. Supplementary data are available at Bioinformatics online.

ECFS-DEA: an ensemble classifier-based feature selection for differential expression analysis on expression profiles

BackgroundVarious methods for differential expression analysis have been widely used to identify features which best distinguish between different categories of samples. Multiple hypothesis testing may leave out explanatory features, each of which may be composed of individually insignificant variables. Multivariate hypothesis testing holds a non-mainstream position, considering the large computation overhead of large-scale matrix operation. Random forest provides a classification strategy for calculation of variable importance. However, it may be unsuitable for different distributions of samples.ResultsBased on the thought of using an ensemble classifier, we develop a feature selection tool for differential expression analysis on expression profiles (i.e., ECFS-DEA for short). Considering the differences in sample distribution, a graphical user interface is designed to allow the selection of different base classifiers. Inspired by random forest, a common measure which is applicable to any base classifier is proposed for calculation of variable importance. After an interactive selection of a feature on sorted individual variables, a projection heatmap is presented using k-means clustering. ROC curve is also provided, both of which can intuitively demonstrate the effectiveness of the selected feature.ConclusionsFeature selection through ensemble classifiers helps to select important variables and thus is applicable for different sample distributions. Experiments on simulation and realistic data demonstrate the effectiveness of ECFS-DEA for differential expression analysis on expression profiles. The software is available at http://bio-nefu.com/resource/ecfs-dea.

The Method of Rapid Analysis to Determine the Version of the Modern Use of Historic Manor Complexes

The article is focused on the problem of preserving the ownerless historic manor complexes (HMC) and their restoration on the basis of commercial use in conditions of public-private partnership. Basing on the theoretical R & D done in the recent years and on their study of the experience gained in the best practices of capitalizing the architectural heritage, the authors identified possible options for the use of estate complexes and formalized a minimum list of the key HMC indicators affecting the further choices. Proceeding from the prescriptive literature, they determined the values and the importance degree of key indicators for each use option. Further on, using the method of multivariate analysis of variance, the authors adjusted the process to juxtapose the values of the key indicators of the estates with the specified conditions for each option and automated that process by means of software support. The result of the work appears in form of the analysis matrix, which automatically performs the multifactor analysis of the estate complex and helps to select the most rational use of the given HMC. The proposed method of express analysis makes it possible to determine the most effective options for the modern use of each particular estate, depending on its indices. This will reduce the risks of potential investors, attract the inflow of investments into the restoration and thus contribute to the revitalization of the estates by integrating them into the Key words: manor, historic manor complex, architectural monument, restoration, commercial use, use cases, express analysis.

Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies

BackgroundHigh-throughput RNA sequencing (RNA-seq) has evolved as an important analytical tool in molecular biology. Although the utility and importance of this technique have grown, uncertainties regarding the proper analysis of RNA-seq data remain. Of primary concern, there is no consensus regarding which normalization and statistical methods are the most appropriate for analyzing this data. The lack of standardized analytical methods leads to uncertainties in data interpretation and study reproducibility, especially with studies reporting high false discovery rates. In this study, we compared a recently developed normalization method, UQ-pgQ2, with three of the most frequently used alternatives including RLE (relative log estimate), TMM (Trimmed-mean M values) and UQ (upper quartile normalization) in the analysis of RNA-seq data. We evaluated the performance of these methods for gene-level differential expression analysis by considering the factors, including: 1) normalization combined with the choice of a Wald test from DESeq2 and an exact test/QL (Quasi-likelihood) F-Test from edgeR; 2) sample sizes in two balanced two-group comparisons; and 3) sequencing read depths.ResultsUsing the MAQC RNA-seq datasets with small sample replicates, we found that UQ-pgQ2 normalization combined with an exact test can achieve better performance in term of power and specificity in differential gene expression analysis. However, using an intra-group analysis of false positives from real and simulated data, we found that a Wald test performs better than an exact test when the number of sample replicates is large and that a QL F-test performs the best given sample sizes of 5, 10 and 15 for any normalization. The RLE, TMM and UQ methods performed similarly given a desired sample size.ConclusionWe found the UQ-pgQ2 method combined with an exact test/QL F-test is the best choice in order to control false positives when the sample size is small. When the sample size is large, UQ-pgQ2 with a QL F-test is a better choice for the type I error control in an intra-group analysis. We observed read depths have a minimal impact for differential gene expression analysis based on the simulated data.

Detection of Arbuscular Mycorrhizal Fungal Gene Expression by In Situ Hybridization.

The complexity of the obligate symbiotic interaction of arbuscular mycorrhizal (AM) fungi and their host roots requires sophisticated molecular methods. In particular, to capture the dynamic of the interaction, cell-specific methods for gene expression analysis are required. In situ hybridization is a technique that allows to determine the location of transcript accumulation within tissues, being of special interest for these fungi that cannot be genetically modified. The method requires proper fixation and embedding methods as well as specific probes for the hybridization allowing detection of specific transcripts. In this chapter, we present a method to prepare roots, which have established a symbiosis with an arbuscular mycorrhizal fungus for the detection of fungal transcripts. This includes chemical fixation, subsequent embedding in a suitable medium, sectioning and pretreatment of sections, the hybridization procedure itself, as well as the immunological detection of RNA-RNA hybrids.

Bacterial Differential Expression Analysis Methods.

RNA-Seq examines global gene expression to provide insights into cellular processes, and it can be particularly informative when comparing contrasting physiological states or strains. Although relatively routine in many laboratories, there are many steps involved in performing a transcriptomics experiment to ensure representative and high-quality results are generated for analysis. In this chapter, we present the application of widely used bioinformatic methodologies to assess, trim, and filter RNA-seq reads for quality using FastQC and Trim Galore, respectively. High-quality reads are mapped using Bowtie2 and differentially expressed genes across different groups were estimated using the DEseq2 R-Bioconductor package. In addition, we describe the various steps to perform the sample-wise data quality assessment by generating exploratory plots through the DESeq2 package. Simple steps to calculate the significant differentially expressed genes, up- and down-regulated genes, and exporting the data and images are also included. A Venn diagram is a useful method to compare the differentially expressed genes across various comparisons and steps to generate the Venn diagram from DESeq2 results are provided. Finally, the output from DESeq2 is compared to published results from EdgeR. The Clostridium autoethanogenum data are published and publicly available.

Identifying critical genes in esophageal squamous cell carcinoma using an ensemble approach

Abstract Esophageal Squamous Cell Carcinoma (ESCC) is considered as a deadly disease especially in the North-East, India. A series of differentially expressed genes (DEGs) are suspected to be involved in the progression of ESCC. To search the DEGs a good number of tools are available. To remove the biasness of resulting DEGs given by all such tools, a consensus function is necessary on which user can rely on the output generated by differential expression analysis methods applied on multiple sources of data. In this study, we have considered two microarrays (of 34 and 106 samples, respectively) and one RNA-seq data (of 29 samples) to conduct an unbiased integrative analysis towards the identification of critical genes for ESCC. Initially, independent downstream analysis on each type of data using six differentially expressed gene identification tools followed by an integrative analysis supported by an effective consensus function is conducted to identify an unbiased set of differently expressed genes. The identified gene set includes common genes obtained (for P-value cut-off

Prospects for sociogenomics in avian cooperative breeding and parental care.

For the last 40 years, the study of cooperative breeding (CB) in birds has proceeded primarily in the context of discovering the ecological, geographical, and behavioral drivers of helping. The advent of molecular tools in the early 1990s assisted in clarifying the relatedness of helpers to those helped, in some cases, confirming predictions of kin selection theory. Methods for genome-wide analysis of sequence variation, gene expression, and epigenetics promise to add new dimensions to our understanding of avian CB, primarily in the area of molecular and developmental correlates of delayed breeding and dispersal, as well as the ontogeny of achieving parental status in nature. Here, we outline key ways in which modern -omics approaches, in particular genome sequencing, transcriptomics, and epigenetic profiling such as ATAC-seq, can be used to add a new level of analysis of avian CB. Building on recent and ongoing studies of avian social behavior and sociogenomics, we review how high-throughput sequencing of a focal species or clade can provide a robust foundation for downstream, context-dependent destructive and non-destructive sampling of specific tissues or physiological states in the field for analysis of gene expression and epigenetics. -Omics approaches have the potential to inform not only studies of the diversification of CB over evolutionary time, but real-time analyses of behavioral interactions in the field or lab. Sociogenomics of birds represents a new branch in the network of methods used to study CB, and can help clarify ways in which the different levels of analysis of CB ultimately interact in novel and unexpected ways.

Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy

BackgroundThe early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups.MethodsThis paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between normal, normal adjacent to tumor (NAT) and tumor samples. For the potential marker genes, the expression level boundaries among different groups are defined with the information entropy method.ResultsCompared with two conventional methods t-test and fold change, the top average ranked genes selected by our method can achieve better performance under all metrics in the 10-fold cross-validation. Then GO and KEGG enrichment analysis are conducted to explore the biological function of the top 100 ranked genes. At last, we choose the top 10 average ranked genes as lung cancer markers and their expression boundaries are calculated and reported.ConclusionThe proposed approach is effective to identify gene markers for lung cancer diagnosis. It is not only more accurate than conventional DEA methods but also provides a reliable method to identify the gene expression level boundaries.

ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data

BackgroundData from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as “imputation”, avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis.ResultsWe propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method.ConclusionsThe new method for the differential expression analysis of proteomic data is available as an easy to use Python package.

Метод генерації гіперкомплексних числових систем для моделювання цифрових реверсивних фільтрів 4-го порядку

A digital filter is a digital system used to filter a discrete signal. It can be implemented either by software or by specialized equipment. The use of hypercomplex numbers for building digital filter structures can provide significant benefits. Digital filters with hypercomplex parameters have higher speed and better performance in terms of total parametric sensitivity. Although in previous works software filter of 3 th order structure synthesis algorithms were proposed, in this paper only software filter of 4 th order structure synthesis algorithms are considered. Studies have shown that the expression analysis method for the transfer function denominator norms of the hypercomplex filter allows you to effectively select the hypercomplex number system, which has all the necessary properties. It is shown that only quadriplex number system K and hypercomplex number system obtained by auto doubling of a double-number system W are suitable for the structures synthesis of fourth-order hypercomplex digital filters. These hypercomplex number systems allow to get a complete set of filter transfer function shift operator steps. On the other hand, they have isomorphic weakly filled hypercomplex number systems. Transition to such systems can significantly reduce the number of real operations during the filter operation time. In the work, all transformations and calculations in the hypercomplex domain were performed using the software module for hypercomplex calculations in the Maple computer algebra framework. This fact confirms the efficiency of this software module. Tabl.: 2. Refs: 17 titles.

QPCR-based methods for expression analysis of miRNAs.

Several approaches for miRNA expression analysis have been developed in recent years. In this article, we provide an updated and comprehensive review of available qPCR-based methods for miRNA expression analysis and discuss their advantages and disadvantages. Existing techniques involve the use of stem-loop reverse transcriptase-PCR, polyadenylation of RNAs, ligation of adapters or RT with complex primers, using universal or miRNA-specific qPCR primers and/or probes. Many of these methods are oriented towards the expression analysis of mature miRNAs and few are designed for the study of pre-miRNAs and pri-miRNAs. We also discuss findings from articles that compare results from existing methods. Finally, we suggest key points for the improvement of available techniques and for the future development of additional methods.

Visualization methods for differential expression analysis

BackgroundDespite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users.ResultsIn this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package “bigPint” includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The “bigPint” website is located at https://lindsayrutter.github.io/bigPintand contains short vignette articles that introduce new users to our package, all written in reproducible code.ConclusionsWe emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.

Nonparametric expression analysis using inferential replicate counts.

A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test.