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BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.ResultsIn this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.ConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.
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15The confounding effects of high genetic diversity on the determination and interpretation of differential gene expression analysis in the parasitic nematode Haemonchus contortus
The confounding effects of high genetic diversity on the determination and interpretation of differential gene expression analysis in the parasitic nematode Haemonchus contortus
Read more6Differential gene expression analysis in single-cell RNA sequencing data.
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a logistic regression model and a nonparametric method based on Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model is used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated data and real data to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity.
Read more2Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python
BackgroundDifferential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma—for microarray data –, and edgeR and DESeq2—for RNA-Seq data–, are the most widely used tools for differential gene expression analysis of bulk transcriptomic data.ResultsWe present the differential expression features of InMoose, a Python implementation of R tools limma, edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io.ConclusionsWe present a new Python implementation of state-of-the-art tools limma, edgeR, and DESeq2, to perform differential gene expression analysis of bulk transcriptomic data. This new implementation exhibits results nearly identical to the original tools, improving interoperability and reproducibility between Python and R bioinformatics pipelines.
Read more81Effect of rosiglitazone on the differential expression of diabetes-associated proteins in pancreatic islets of C57Bl/6 lep/lep mice.
The insulin sensitizer drug, rosiglitazone, has been shown to have a protective effect on pancreatic islet cell structure and function in animal models of type 2 diabetes. The identification of new molecular targets associated both with islet cell dysfunction and protection is a crucial research goal. In the present study, a proteomics approach has been used to identify such targets. Obese C57Bl/6J lep/lep mice and lean littermates were given the insulin sensitizer drug BRL49653, rosiglitazone. It normalized the impaired glucose tolerance in lep/lep mice but had no significant effect on glucose tolerance in the lean mice. Pancreatic islet polypeptides were arrayed by a two-dimensional gel electrophoresis system that separated more than 2500 individual spots. Three overexpressed and six underexpressed proteins were significant (p < 0.05) between lep/lep and lean mice, and four were modulated significantly (p < 0.05) by the rosiglitazone treatment of the obese mice. The identity of these differentially expressed proteins was made using mass spectrometric analysis and provided evidence that differential expression of actin-binding proteins may be an important aspect of defective islet function. Rosiglitazone increased carboxypeptidase B expression in both lep/lep and normal mice suggesting that this might be an independent effect of rosiglitazone that contributes to improved insulin processing.
Read more2Differential gene expression analysis of a phase II randomized clinical trial of patients with endometrial cancer (EC) treated with megestrol acetate ± pterostilbene.
e17601 Background: The oral antioxidant pterostilbene (PT) (3,5-dimethoxy-40-hydroxystilbene) has demonstrated synergistic antiproliferative effect with megestrol acetate (MA) in endometrial cancer (EC). We conducted a preoperative (window of opportunity) study enrolling endometrial cancer patients scheduled for hysterectomy, who were randomized to preoperative PT+MA vs MA alone. Methods: This prospective, randomized phase II clinical trial (NCT03671811) enrolled patients who were surgical candidates for hysterectomy. Patients were randomized 1:1 to each arm and were treated for 3 weeks with either PT+MA (arm 1) or MA alone (arm 2). Exploratory objectives included histologic response by GOG 211 criteria, and transcriptional changes using whole transcriptome analysis. For the RNA-seq analysis, differential expression analysis used the “limma” R limma package. The differentially expressed genes were selected based on the cutoff values of p-value <= 0.05 and fold change >= 1.5. The gene set enrichment analysis was performed using GSEA software v. 4.3.2 to identify the affected Hallmark and KEGG pathways, using the pre-ranked gene list ranked by the -log10(p-value) with a sign determined by the fold change direction. Results: This prospective, randomized phase II clinical trial (NCT03671811) enrolled patients who were surgical candidates for hysterectomy. Patients were randomized 1:1 to each arm and were treated for 3 weeks with either PT+MA (arm 1) or MA alone (arm 2). Exploratory objectives included histologic response by GOG 211 criteria, and transcriptional changes using whole transcriptome analysis. For the RNA-seq analysis, differential expression analysis used the “limma” R limma package. The differentially expressed genes were selected based on the cutoff values of p-value <= 0.05 and fold change >= 1.5. The gene set enrichment analysis was performed using GSEA software v. 4.3.2 to identify the affected Hallmark and KEGG pathways, using the pre-ranked gene list ranked by the -log10(p-value) with a sign determined by the fold change direction. Conclusions: In endometrial cancer, the oral antioxidant Pterostilbene increases innate immune system pathway expression, while reducing mTOR pathway expression, potentially serving as a useful adjunct to progestin hormonal therapy in EC. Clinical trial information: NCT03671811 .
Read moreComparative Analysis of R and Mathematica Package for Differential Gene Expression Analysis Using Microarray Dataset on Pancreatic Cancer
Microarrays produces enormous amounts of information requiring a series of repeated analyses to condense data. To analyze this data several computational software is used. Objective: To compare the analysis of R and Mathematica package for differential gene expression analysis using microarray dataset. Methods: Microarray Data were collected from an online database GEO (gene expression omnibus). Mathematica and R software was used for comparative analysis. In R software, Robust Multi-Array Average (RMA), was used for data normalization. While Limma package was used for DGE analysis. In Mathematica software, AffyDGED was used for normalization and DGE analysis of dataset. Results: 3,426 non-differentially expressed genes and 14936 genes with differential expression were separated from R. The thresholds for identifying "up" and "down" gene expression were estimated to be 0.98 and -0.19, respectively, using the RMA method to analyze this dataset. AffyDGED from Mathematica detected 1,832 genes as differentially expressed; of them, 1,591 genes overlap with the real and 1,944 differently expressed genes, giving the true positive rate of (1591/1944) =0.818. This indicates that 18% of the genuine list of differentially expressed genes could not be reliably identified by AffyDGED. Conclusions: R programming is one of the most popular and recommendable tools for microarrays to perform different analysis, and along with Bioconductor it makes one of the best analysis algorithms for DGE analysis. On the other hand, AffyDGED brings a contemporary algorithm useful in the real world to the Mathematica user.
Read moreP-673 Women exposed to higher levels of DEHP display altered microRNA expression profile in the pre-ovulatory follicular fluid
Study question Can higher concentrations of di-2-ethylhexyl phthalate (DEHP) in follicular fluid (FF) lead to an altered microRNA expression profile? Summary answer The present study indicates the upregulation of miR-203a-3p, miR-150-5p and miR-28-3p in the follicular fluid of women with higher DEHP concentrations (FDR&lt;0.05). What is known already Women are exposed daily to endocrine-disrupting chemicals (EDCs) such as phthalates which can be found in hundreds of personal care products and plastic items. In studies of rodents exposed to certain phthalates, high doses have been shown to change hormone levels, disrupt folliculogenesis and cause birth defects. Recently, we reported a strong inverse association between DEHP metabolites and ovarian sensitivity index (OSI) in females undergoing ART treatment. Several mechanisms have been proposed to explain the association between DEHP and fertility in rodents, however not much is known about the mechanisms in humans. Study design, size, duration FF was collected from 96 women undergoing IVF treatment after controlled ovarian stimulation during ovarian puncture. 48 participants from Estonia were recruited at Nova Vita Clinic AS in Tallinn, Estonia in 2019 and 48 participants from Sweden were recruited at Carl von Linnekliniken in Uppsala, Sweden in 2016. In both cohorts, patients signed a written informed consent form. Both cohorts were matched for age, BMI, parity and the number of previous IVF treatments. Participants/materials, setting, methods DEHP metabolite concentrations in the FF samples were measured in our earlier study by LC-MS/MS. Cell free RNA was extracted from FF samples with Qiagen miRNeasy Micro kit, sequencing libraries were prepared with QIAseq miRNA library kit and sequenced on Illumina NextSeq 550 instrument. Differential miRNA expression analysis was performed with the DESeq2 package. Pathway enrichment analysis for differentially expressed miRNA targets was conducted with the miEAA tool integrated with WikiPathways. Main results and the role of chance In total, 465 miRNAs were observed at least twice in ≥ 24 samples. Differential expression analysis was conducted between DEHP-high and DEHP-low groups in combined cohorts using the cohort as a covariate. 16 miRNAs were found to be differentially expressed between DEHP groups with FDR&lt;0.1. Three miRNAs: miR-203a-3p, miR-150-5p and miR-28-3p, were differentially expressed with FDR&lt;0.05 and were more abundant in women with high DEHP-levels in both cohorts. Of these, miR-203a-3p was negatively correlated with OSI (Pearson R = -0.27, p = 0.0089). Six miRNAs (miR-10b-5p, miR-112-5p, miR-132-5p, miR-203-3p, miR-205-5p and miR-27-3p) were found to have predicted targets in the androgen receptor signalling pathway. Limitations, reasons for caution The miRNAs found in this study need to be validated in a larger set of patients as a cohort-dependent difference in the results was observed. There is a need for further functional studies to investigate miRNAs as possible mediators of the association between DEHP and ovarian sensitivity index. Wider implications of the findings The current study presents evidence that DEHP exposure alters the expression of FF miRNAs which are associated with androgen receptor signalling. These results provide novel evidence of the potential influence of endocrine-disrupting chemicals on female reproduction. Trial registration number NA
Read more1Identification of a Relapse Signature in t(8;21) Pediatric AML
Identification of a Relapse Signature in t(8;21) Pediatric AML
Identification of high-risk genes and classification of acute myocardial infarction patients utilizing deep learning in a restricted cohort.
Identification of high-risk genes and classification of acute myocardial infarction patients utilizing deep learning in a restricted cohort.
Read more221Identifying differentially expressed transcripts from RNA-seq data with biological variation.
Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python. The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Read more11The “Window t test”: a simple and powerful approach to detect differentially expressed genes in microarray datasets
This work focuses on differential expression analysis of microarray datasets. One way to improve such statistical analyses is to integrate biological information in the design of these analyses. In this paper, we will use the relationship between the level of gene expression and variability. Using this biological information, we propose to integrate the information from multiple genes to get a better estimate of individual gene variance, when a small number of replicates are available, to increase the power of the statistical analysis. We describe a strategy named the “Window t test” that uses multiple genes which share a similar expression level to compute the variance which is then incorporated a classic t test. The performances of this new method are evaluated by comparison with classic and widely-used methods for differential expression analysis (the classic Student t test, the Regularized t test (reg t test), SAM, Limma, LPE and Shrinkage t). In each case tested, the results obtained were at least equivalent to the best performing method and, in most cases, outperformed it. Moreover, the Window t test relies on a very simple procedure requiring small computing power compared with other methods designed for microarray differential expression analysis.
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8Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.
Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.
Read more38Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.
Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis.
Read more7Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.
Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information's Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.
Read morePrecision Medicine in Psychiatry by Predicting Lithium Treatment Response using Gene Expression Biomarkers and Machine Learning
BackgroundWe tested the hypothesis that transcriptional‐level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who were later were clinically classified as lithium treatment responders using the primary outcome measure of the Clinical Global Impression Scale for Bipolar Disorder‐Severity (CGI‐BP‐S).MethodsGene expression study data was obtained from the Lithium Treatment‐Moderate dose Use Study data accessed from the National Center for Biotechnology Information's Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis using the CGI‐BP‐S responder status and Gender served as control variables for analysis using the Linear Models for Microarray and RNA‐Seq (limma) package and the Random Forests machine learning algorithm in R for Statistical Computing programming language software.ResultsIn pre‐treatment lithium responders, the following genes were found having a greater than 0.5 fold‐change and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5 while the female‐biased genes were identified as: HLA‐H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL.ConclusionsApplying machine learning methods to gene expression data, we developed a pre‐treatment gender‐specific gene‐expression‐based predictive model selective for lithium treatment responders according to the Clinical Global Impression Scale for Bipolar Disorder‐Severity with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.Support or Funding InformationNone.
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