Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples, which may indicate that these genes are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Seq data, which are generally normalized to remove technical variability. Here, we find and demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes, and that this can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular when preprocessing large-scale heterogeneous data, quantile-based normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. We therefore developed CAIMAN, a method to correct for false-positive associations that may arise from normalization of RNA-Seq data. CAIMAN utilizes a Gaussian mixture model to fit the distribution of gene expression and to adaptively select the threshold to define lowly expressed genes, which are prone to form false-positive associations. Thereafter, CAIMAN corrects the normalized expression for these genes by removing the variability across samples that might lead to false-positive associations. Moreover, CAIMAN avoids arbitrary gene filtering and retains associations to genes that only express in small subgroups of samples, highlighting its potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

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MetaMap, an interactive webtool for the exploration of metatranscriptomic reads in human disease-related RNA-seq data

10.1101/425439 ◽

2018 ◽

Author(s):

LM Simon ◽

G Tsitsiridis ◽

P Angerer ◽

FJ Theis

Keyword(s):

Human Disease ◽

Expression Patterns ◽

Heterogeneous Data ◽

Rna Seq ◽

Web Tool ◽

Differential Abundance ◽

Downstream Analysis ◽

User Friendly ◽

Differential Abundance Analysis ◽

The Impact

AbstractMotivationThe MetaMap resource contains metatranscriptomic expression data from screening >17,000 RNA-seq samples from >400 archived human disease-related studies for viral and microbial reads, so-called “metafeatures”. However, navigating this set of large and heterogeneous data is challenging, especially for researchers without bioinformatic expertise. Therefore, a user-friendly interface is needed that allows users to visualize and statistically analyse the data.ResultsWe developed an interactive frontend to facilitate the exploration of the MetaMap resource. The webtool allows users to query the resource by searching study abstracts for keywords or browsing expression patterns for specific metafeatures. Moreover, users can manually define sample groupings or use the existing annotation for downstream analysis. The web tool provides a large variety of analyses and visualizations including dimension reduction, differential abundance analysis and Krona visualizations. The MetaMap webtool represents a valuable resource for hypothesis generation regarding the impact of the microbiome in human disease.AvailabilityThe presented web tool can be accessed at https://github.com/theislab/MetaMap

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SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xianwen Ren ◽

Liangtao Zheng ◽

Zemin Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Sequencing Data ◽

Computational Framework ◽

Human Blood Cells ◽

Single Cell Rna Sequencing ◽

Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

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RNA sequencing data: hitchhiker's guide to expression analysis

10.7287/peerj.preprints.27283 ◽

2018 ◽

Author(s):

Koen Van Den Berge ◽

Katharina Hembach ◽

Charlotte Soneson ◽

Simone Tiberi ◽

Lieven Clement ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Large Scale ◽

Science Studies ◽

Rna Seq ◽

Sequencing Data ◽

Data Types ◽

The Past ◽

Long Read ◽

Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

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Detection of Alu Exonization Events in Human Frontal Cortex From RNA-Seq Data

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.727537 ◽

2021 ◽

Vol 8 ◽

Author(s):

Liliana Florea ◽

Lindsay Payer ◽

Corina Antonescu ◽

Guangyu Yang ◽

Kathleen Burns

Keyword(s):

Frontal Cortex ◽

Large Scale ◽

Reference Genome ◽

Expression Patterns ◽

Data Sets ◽

Rna Seq ◽

Mrna Isoforms ◽

Specific Expression ◽

Human Frontal Cortex ◽

Alternatively Spliced

Alu exonization events functionally diversify the transcriptome, creating alternative mRNA isoforms and accounting for an estimated 5% of the alternatively spliced (skipped) exons in the human genome. We developed computational methods, implemented into a software called Alubaster, for detecting incorporation of Alu sequences in mRNA transcripts from large scale RNA-seq data sets. The approach detects Alu sequences derived from both fixed and polymorphic Alu elements, including Alu insertions missing from the reference genome. We applied our methods to 117 GTEx human frontal cortex samples to build and characterize a collection of Alu-containing mRNAs. In particular, we detected and characterized Alu exonizations occurring at 870 fixed Alu loci, of which 237 were novel, as well as hundreds of putative events involving Alu elements that are polymorphic variants or rare alleles not present in the reference genome. These methods and annotations represent a unique and valuable resource that can be used to understand the characteristics of Alu-containing mRNAs and their tissue-specific expression patterns.

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Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

Bioinformatics ◽

10.1093/bioinformatics/btaa488 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i417-i426

Author(s):

Assya Trofimov ◽

Joseph Paul Cohen ◽

Yoshua Bengio ◽

Claude Perreault ◽

Sébastien Lemieux

Keyword(s):

Large Scale ◽

Learning Algorithm ◽

Single Gene ◽

Expression Patterns ◽

Tensor Decomposition ◽

Supplementary Information ◽

Fe Model ◽

Tensor Factorization ◽

Rna Seq ◽

Sample Representation

Abstract Motivation The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations. Results We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations. Availability and implementation A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

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Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽

10.7717/peerj.1991 ◽

2016 ◽

Vol 4 ◽

pp. e1991 ◽

Cited By ~ 9

Author(s):

Yanping Li ◽

Shilin Tian ◽

Xiaojun Yang ◽

Xin Wang ◽

Yuhai Guo ◽

...

Keyword(s):

Gene Expression ◽

Disease Resistance ◽

Powdery Mildew ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Defense Responses ◽

Differentially Expressed ◽

Rna Seq ◽

Sequencing Data ◽

Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

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Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

10.1101/375766 ◽

2018 ◽

Cited By ~ 4

Author(s):

Patrick Deelen ◽

Sipko van Dam ◽

Johanna C. Herkert ◽

Juha M. Karjalainen ◽

Harm Brugge ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Gene Expression Analysis ◽

Diagnostic Yield ◽

Genetic Diagnosis ◽

Added Value ◽

Disease Genes ◽

Rna Seq ◽

Sequencing Data ◽

Causative Gene

AbstractClinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patient’s phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

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Machine Learning-Assisted Identification of Factors Contributing to the Technical Variability Between Bulk and Single-Cell RNA-Seq Experiments

10.21203/rs.3.rs-1247889/v1 ◽

2022 ◽

Author(s):

Sofya Lipnitskaya ◽

Yang Shen ◽

Stefan Legewie ◽

Holger Klein ◽

Kolja Becker

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Single Cell ◽

Rna Sequencing ◽

Quantitative Difference ◽

Rna Seq ◽

Sequencing Data ◽

Factors Affecting ◽

Expression Variability ◽

Technical Variability

Abstract Background: Recent studies in the area of transcriptomics performed on single-cell and population levels reveal noticeable variability in gene expression measurements provided by different RNA sequencing technologies. Due to increased noise and complexity of single-cell RNA-Seq (scRNA-Seq) data over the bulk experiment, there is a substantial number of variably-expressed genes and so-called dropouts, challenging the subsequent computational analysis and potentially leading to false positive discoveries. In order to investigate factors affecting technical variability between RNA sequencing experiments of different technologies, we performed a systematic assessment of single-cell and bulk RNA-Seq data, which have undergone the same pre-processing and sample preparation procedures. Results: Our analysis indicates that variability between gene expression measurements as well as dropout events are not exclusively caused by biological variability, low expression levels, or random variation. Furthermore, we propose FAVSeq, a machine learning-assisted pipeline for detection of factors contributing to gene expression variability in matched RNA-Seq data provided by two technologies. Based on the analysis of the matched bulk and single-cell dataset, we found the 3'-UTR and transcript lengths as the most relevant effectors of the observed variation between RNA-Seq experiments, while the same factors together with cellular compartments were shown to be associated with dropouts. Conclusions: Here, we investigated the sources of variation in RNA-Seq profiles of matched single-cell and bulk experiments. In addition, we proposed the FAVSeq pipeline for analyzing multimodal RNA sequencing data, which allowed to identify factors affecting quantitative difference in gene expression measurements as well as the presence of dropouts. Hereby, the derived knowledge can be employed further in order to improve the interpretation of RNA-Seq data and identify genes that can be affected by assay-based deviations. Source code is available under the MIT license at https://github.com/slipnitskaya/FAVSeq.

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High-quality rice RNA-seq-based co-expression network for predicting gene function and regulation

10.1101/138040 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hua Yu ◽

Bingke Jiao ◽

Chengzhi Liang

Keyword(s):

Large Scale ◽

Network Inference ◽

Agronomic Traits ◽

Enrichment Analysis ◽

Circular Rna ◽

Rna Seq ◽

Sequencing Data ◽

High Quality ◽

Crop Species ◽

Functional Link

AbstractInferring the genome-scale gene co-expression network is important for understanding genetic architecture underlying the complex and various biological phenotypes. The recent availability of large-scale RNA-seq sequencing-data provides great potential for co-expression network inference. In this study, for the first time, we presented a novel heterogeneous ensemble pipeline integrating three frequently used inference methods, to build a high-quality RNA-seq-based Gene Co-expression Network (GCN) in rice, an important monocot species. The quality of the network obtained by our proposed method was first evaluated and verified with the curated positive and negative gene functional link datasets, which obviously outperformed each single method. Secondly, the powerful capability of this network for associating unknown genes with biological functions and agronomic traits was showed by enrichment analysis and case studies. Particularly, we demonstrated the potential applications of our proposed method to predict the biological roles of long non-coding RNA (lncRNA) and circular RNA (circRNA) genes. Our results provided a valuable data source for selecting candidate genes to further experimental validation during rice genetics research and breeding. To enhance identification of novel genes regulating important biological processes and agronomic traits in rice and other crop species, we released the source code of constructing high-quality RNA-seq-based GCN and rice RNA-seq-based GCN, which can be freely downloaded online at https://github.com/czllab/NetMiner.

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MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data

10.1101/269092 ◽

2018 ◽

Cited By ~ 1

Author(s):

LM Simon ◽

S Karg ◽

AJ Westermann ◽

M Engel ◽

AHA Elbehery ◽

...

Keyword(s):

High Performance Computing ◽

Human Disease ◽

High Performance ◽

Large Scale ◽

Expression Patterns ◽

Rna Seq ◽

Wide Range ◽

Eukaryotic Gene ◽

Public Repositories ◽

Performance Computing

AbstractBackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts.FindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource.ConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.

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