Multiple Biological Conditions Research Articles

MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions.

The dynamic transcriptional mechanisms that govern eukaryotic cell function can now be analyzed by RNA sequencing. However, the packages currently available for the analysis of raw sequencing data do not provide automatic analysis of complex experimental designs with multiple biological conditions and multiple analysis time-points. The MultiRNAflow suite combines several packages in a unified framework allowing exploratory and supervised statistical analyses of temporal data for multiple biological conditions. The R package MultiRNAflow is freely available on Bioconductor (https://bioconductor.org/packages/MultiRNAflow/), and the latest version of the source code is available on a GitHub repository (https://github.com/loubator/MultiRNAflow).

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.ResultsTo expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.ConclusionscDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

Trajectory inference across multiple conditions with condiments

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell’s conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome.

Alternative splicing (AS) is an essential post-transcriptional mechanism that regulates many biological processes. However, identifying comprehensive types of AS events without guidance from a reference genome is still a challenge. Here, we proposed a novel method, MkcDBGAS, to identify all seven types of AS events using transcriptome alone, without a reference genome. MkcDBGAS, modeled by full-length transcripts of human and Arabidopsis thaliana, consists of three modules. In the first module, MkcDBGAS, for the first time, uses a colored de Bruijn graph with dynamic- and mixed- kmers to identify bubbles generated by AS with precision higher than 98.17% and detect AS types overlooked by other tools. In the second module, to further classify types of AS, MkcDBGAS added the motifs of exons to construct the feature matrix followed by the XGBoost-based classifier with the accuracy of classification greater than 93.40%, which outperformed other widely used machine learning models and the state-of-the-art methods. Highly scalable, MkcDBGAS performed well when applied to Iso-Seq data of Amborella and transcriptome of mouse. In the third module, MkcDBGAS provides the analysis of differential splicing across multiple biological conditions when RNA-sequencing data is available. MkcDBGAS is the first accurate and scalable method for detecting all seven types of AS events using the transcriptome alone, which will greatly empower the studies of AS in a wider field.

Quantile Regression Approach for Analyzing Similarity of Gene Expressions under Multiple Biological Conditions

Temporal gene expression data contain ample information to characterize gene function and are now widely used in bio-medical research. A dense temporal gene expression usually shows various patterns in expression levels under different biological conditions. The existing literature investigates the gene trajectory using the mean function. However, temporal gene expression curves usually show a strong degree of heterogeneity under multiple conditions. As a result, rates of change for gene expressions may be different in non-central locations and a mean function model may not capture the non-central location of the gene expression distribution. Further, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical when analyzing gene expression data. In this research, a linear quantile mixed model is used to find the trajectory of gene expression data. This method enables the changes in gene expression over time to be studied by estimating a family of quantile functions. A statistical test is proposed to test the similarity between two different gene expressions based on estimated parameters using a quantile model. Then, the performance of the proposed test statistic is examined using extensive simulation studies. Simulation studies demonstrate the good statistical performance of this proposed test statistic and show that this method is robust against normal error assumptions. As an illustration, the proposed method is applied to analyze a dataset of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, a minimum Mahalanobis distance is used to find the clustering tree for gene expressions.

Cell Type Diversity Statistic: An Entropy-Based Metric to Compare Overall Cell Type Composition Across Samples.

Changes of cell type composition across samples can carry biological significance and provide insight into disease and other conditions. Single cell transcriptomics has made it possible to study cell type composition at a fine resolution. Most single cell studies investigate compositional changes between samples for each cell type independently, not accounting for the fixed number of cells per sample in sequencing data. Here, we provide a metric of the distribution of cell type proportions in a sample that can be used to compare the overall distribution of cell types across multiple samples and biological conditions. This is the first method to measure overall cell type composition at the single cell level. We use the method to assess compositional changes in peripheral blood mononuclear cells (PBMCs) related to aging and extreme old age using multiple single cell datasets from individuals of four age groups across the human lifespan.

ScINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples. We compare scINSIGHT with state-of-the-art methods using simulated and real data, which demonstrate its improved performance. Our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

Gene Expression Variation Analysis (GEVA): A new R package to evaluate variations in differential expression in multiple biological conditions

Nowadays, there are thousands of publicly available gene expression datasets which can be analyzed in silico using specialized software or the R programming language. However, transcriptomic studies consider experimental conditions individually, giving one independent result per comparison. Here we describe the Gene Expression Variation Analysis (GEVA), a new R package that accepts multiple differential expression analysis results as input and performs multiple statistical steps, such as weighted summarization, quantiles partition, and clustering to find genes whose differential expression varied less across all experiments. The experimental conditions can be divided into groups, which we call factors, where additional ANOVA (Fisher’s and Levene’s) tests are applied to identify differentially expressed genes in response either specifically to one factor or dependently to all factors. The final results present three possible classifications for relevant genes: similar, factor-dependent, and factor-specific. To validate these results subsequently to the GEVA’s development, 28 transcriptomic datasets were tested using 11 different combinations of the available parameters, including several clustering, quantiles, and summarization methods. The final classifications were validated using knockout studies from different organisms, as they lack genes whose differential expression is expected. Although some of the final classifications differed depending on the parameters’ choice, the test results from the default parameters corroborated with the published experimental studies regarding the selected datasets. Thus, we conclude that GEVA can effectively find similarities between groups of biological conditions, and therefore could be a robust alternative for multiple comparison analyses.

Identification, semantic annotation and comparison of combinations of functional elements in multiple biological conditions.

Approaches such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) represent the standard for the identification of binding sites of DNA-associated proteins, including transcription factors and histone marks. Public repositories of omics data contain a huge number of experimental ChIP-seq data, but their reuse and integrative analysis across multiple conditions remain a daunting task. We present the Combinatorial and Semantic Analysis of Functional Elements (CombSAFE), an efficient computational method able to integrate and take advantage of the valuable and numerous, but heterogeneous, ChIP-seq data publicly available in big data repositories. Leveraging natural language processing techniques, it integrates omics data samples with semantic annotations from selected biomedical ontologies; then, using hidden Markov models, it identifies combinations of static and dynamic functional elements throughout the genome for the corresponding samples. CombSAFE allows analyzing the whole genome, by clustering patterns of regions with similar functional elements and through enrichment analyses to discover ontological terms significantly associated with them. Moreover, it allows comparing functional states of a specific genomic region to analyze their different behavior throughout the various semantic annotations. Such findings can provide novel insights by identifying unexpected combinations of functional elements in different biological conditions. The Python implementation of the CombSAFE pipeline is freely available for non-commercial use at: https://github.com/DEIB-GECO/CombSAFE. Supplementary data are available at Bioinformatics online.

Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells.

Protein post-translational modifications (PTMs) are essential elements of cellular communication. Their variations in abundance can affect cellular pathways, leading to cellular disorders and diseases. A widely used method for revealing PTM-mediated regulatory networks is their label-free quantitation (LFQ) by high-resolution mass spectrometry. The raw data resulting from such experiments are generally interpreted using specific software, such as MaxQuant, MassChroQ, or Proline for instance. They provide data matrices containing quantified intensities for each modified peptide identified. Statistical analyses are then necessary (1) to ensure that the quantified data are of good enough quality and sufficiently reproducible, (2) to highlight the modified peptides that are differentially abundant between the biological conditions under study. The objective of this chapter is therefore to provide a complete data analysis pipeline for analyzing the quantified values of modified peptides in presence of two or more biological conditions using the R software. We illustrate our pipeline starting from MaxQuant outputs dealing with the analysis of A549-ACE2 cells infected by SARS-CoV-2 at different time stamps, freely available on PRIDE (PXD020019).

Abstract 221: Develop a pathway level classifier to identify clinically relevant subtypes of glioblastoma

Abstract Introduction: Glioblastoma Multiforme (GBM) is a malignant Grade IV brain tumor. With standard treatment, the median survival for adults with GBM, IDH-wildtype, is approximately 11-15 months. We aim at identifying clinically relevant GBM subtypes based on cell signaling pathways information to facilitate personalized medicine. Methods: We rebuilt a MultiPLIER model for GBM using the recount2 compendium with updated V7.0 canonical pathways from the MSigDB database. The MultiPLIER is a machine learning model based on pathway-level information extractor (PLIER), a matrix factorization approach to identify specific pathways that regulate gene expression using a large public dataset and prior biological knowledge from multiple tissues and biological conditions. It has two inputs, the gene expression matrix, and the prior knowledge. PLIER constructed eigengene like latent variables (LVs) to approximate relevant pathways by decomposing gene expression data and a sparse matrix to specify prior information gene sets and pathways in each LV. The MultiPLIER is an unsupervised transfer learning framework to transfer the PLIER model to a specific dataset or disease with smaller sample sizes. Knowledge learned in an extensive collection of datasets can be transferred to a target domain to discover unseen patterns. We used 2315 gene sets, which have 12604 genes as prior biological information in the PLIER training. We produced a decomposition of 903 LVs with 200 LVs with high confidence (area under curve (AUC) of >0.7, false discovery rate (FDR) of <0.05). We then projected the TCGA GBM HT_HG-U133A dataset with 526 primary solid tumor samples and ten solid tissue normal samples to the MultiPLIER 903-dimension space to get a GBM-MultiPLIER model. Results: We used the univariate Cox model to select 169 survival-related LVs specific to GBM subtype studies (p <0.05). Then we used an unsupervised clustering method, consensus clustering (Monti et al., 2003), to discover GBM subtypes. Five subtypes were obtained with the p value 0.00009 of the log-rank test of survival analysis. We also performed Silhouette width, the statistical significance of clustering, and differential expression tests with the Bioconductor package, CancerSubtypes in R. We found the differentially expressed LVs (adjusted p-value<0.01) patterns between each subtype and normal samples. There are also 37 shared LVs among these five subtypes relative to normal samples (adjusted p-value<0.01). The subtype three, which is the best survival subtype, has one LV that has an estimated AUC (Area under the ROC Curve) 0.81, with a 95% confidence interval (0.75,0.86) when subtype 3 compared to other tumor samples. Conclusions: The GBM MultiPLIER model can reveal a consistent pathway or gene set differences across subtypes and capture subtype-specific patterns. These findings provide an opportunity to untangle the underlying biologic meaning further. Citation Format: Xuelian Xu, Zhen Sun, Ning Wen. Develop a pathway level classifier to identify clinically relevant subtypes of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 221.

RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data.

Ribosome profiling, also known as Ribo-seq, has become a popular approach to investigate regulatory mechanisms of translation in a wide variety of biological contexts. Ribo-seq not only provides a measurement of translation efficiency based on the relative abundance of ribosomes bound to transcripts, but also has the capacity to reveal dynamic and local regulation at different stages of translation based on positional information of footprints across individual transcripts. While many computational tools exist for the analysis of Ribo-seq data, no method is currently available for rigorous testing of the pattern differences in ribosome footprints. In this work, we develop a novel approach together with an R package, RiboDiPA, for Differential Pattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meaningful pattern differences across multiple biological conditions on a global scale, and pinpoint characteristic ribosome occupancy patterns at single codon resolution.

Mucosal chemokine CXCL17: What is known and not known.

CXCL17, the last described chemokine, has recently been found to be abundantly and specifically expressed in mucosal sites, while its receptor is still not well determined. Accumulative studies indicate that CXCL17 could potentially exhibit chemotactic, anti-inflammatory, antimicrobial activities under multiple biological conditions. However, the mechanism by which it contributes to the physiological and pathological processes within specific mucosal tissues is still far from being fully elucidated. In this present review, we therefore summarize the current available evidence of CXCL17 with specific emphasis on its biological role and pathophysiological significance, in order to aid in the advancement of CXCL17-related studies.

3DeFDR: statistical methods for identifying cell type-specific looping interactions in 5C and Hi-C data

An important unanswered question in chromatin biology is the extent to which long-range looping interactions change across developmental models, genetic perturbations, drug treatments, and disease states. Computational tools for rigorous assessment of cell type-specific loops across multiple biological conditions are needed. We present 3DeFDR, a simple and effective statistical tool for classifying dynamic loops across biological conditions from Chromosome-Conformation-Capture-Carbon-Copy (5C) and Hi-C data. Our work provides a statistical framework and open-source coding libraries for sensitive detection of cell type-specific loops in high-resolution 5C and Hi-C data from multiple cellular conditions.

A feature selection strategy for gene expression time series experiments with hidden Markov models.

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.

Selection of Reference Genes for Normalization of Gene Expression Data in Blood of Machado-Joseph Disease/Spinocerebellar Ataxia Type 3 (MJD/SCA3) Subjects.

Alongside with the emergent clinical trials for Machado-Joseph disease/Spinocerebellar ataxia type 3 (MJD/SCA3) comes the need to identify molecular biomarkers of disease that can be tracked throughout the trial. MJD is an autosomal dominant neurodegenerative disorder caused by expansion of a CAG repeat in the coding region of the ATXN3 gene. Previous findings indicate the potential of transcriptional alterations in blood of MJD patients as biomarkers of disease. Accurate quantification of gene expression levels by quantitative real-time PCR (qPCR) depends on data normalization, usually performed using reference genes. Because the expression level of routinely used housekeeping genes may vary in multiple biological and experimental conditions, reference gene controls should be validated in each specific experimental design. Here, we aimed to evaluate the expression behavior of five housekeeping genes previously reported as stably expressed in peripheral blood of patients from several disorders-peptidylprolyl isomerase B (PPIB), TNF receptor associated protein 1 (TRAP1), beta-2-microglobulin (B2M), 2,4-dienoyl-CoA reductase 1 (DECR1), and folylpolyglutamate synthase (FPGS). Expression levels of these five genes were assessed by qPCR in blood from MJD subjects (preataxic and patients) and matched controls. While all housekeeping genes, here studied, were stably expressed in our sets of samples, TRAP1 showed to be the most stable gene by NormFinder and BestKeeper. We, therefore, conclude that any of these genes could be used as reference gene in future qPCR studies using blood samples from MJD subjects.

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease.

Most gene expression datasets generated by individual researchers are too small to fully benefit fromunsupervised machine-learning methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. To address this challenge, we utilize transfer learning to extract coordinated expression patterns and uselearned patterns to analyze small rare disease datasets. We trained a pathway-level information extractor (PLIER) model on a large public data compendium comprising multiple experiments, tissues, and biological conditions and then transferred the model to small datasets in an approach we call MultiPLIER. Models constructed from the public data compendium included features that aligned well to known biological factors and were more comprehensive than those constructed from individual datasets or conditions. When transferred to rare disease datasets, the models describe biological processes related to disease severity more effectively than models trained only on a given dataset.

New insights into the pathogenicity of non-synonymous variants through multi-level analysis

Precise classification of non-synonymous single nucleotide variants (SNVs) is a fundamental goal of clinical genetics. Next-generation sequencing technology is effective for establishing the basis of genetic diseases. However, identification of variants that are causal for genetic diseases remains a challenge. We analyzed human non-synonymous SNVs from a multilevel perspective to characterize pathogenicity. We showed that computational tools, though each having its own strength and weakness, tend to be overly dependent on the degree of conservation. For the mutations at non-degenerate sites, the amino acid sites of pathogenic substitutions show a distinct distribution in the classes of protein domains compared with the sites of benign substitutions. Overlooked disease susceptibility of genes explains in part the failures of computational tools. The more pathogenic sites observed, the more likely the gene is expressed in a high abundance or in a high tissue-specific manner, and have a high node degree of protein-protein interaction. The destroyed functions due to some false-negative mutations may arise because of a reprieve from the epigenetic repressed state which shouldn’t happen in multiple biological conditions, instead of the defective protein. Our work adds more to our knowledge of non-synonymous SNVs’ pathogenicity, thus will benefit the field of clinical genetics.

Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data

In the field of molecular biology, it is often of interest to analyze microarray data for clustering genes based on similar profiles of gene expression to identify genes that are differentially expressed under multiple biological conditions. One of the notable characteristics of a gene expression profile is that it shows a cyclic curve over a course of time. To group sequences of similar molecular functions, we propose a Bayesian Dirichlet process mixture of linear regression models with a Fourier series for the regression coefficients, for each of which a spike and slab prior is assumed. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo (MCMC) posterior computation. Due to the so-called “label-switching” problem and different numbers of clusters during the MCMC computation, a post-process approach of Fritsch and Ickstadt (2009) is additionally applied to MCMC samples for an optimal single clustering estimate by maximizing the posterior expected adjusted Rand index with the posterior probabilities of two observations being clustered together. The proposed method is illustrated with two simulated data and one real data of the physiological response of fibroblasts to serum of Iyer et al. (1999).

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data.

To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between “noise” and “true” differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays.