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Abstract Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark). 

Abstract External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications. 

Abstract Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution. However, the low capture of mRNA material within individual cells often leads to inaccurate quantification of genetic material. Consequently, a significant amount of expression values are reported as missing, which are often referred to as dropouts. To overcome this challenge, we develop a novel imputation method, named single-cell Imputation via Subspace Regression (scISR), that can reliably recover the dropout values of scRNA-seq data. The scISR method first uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and then estimates the dropout values using a subspace regression model. Our comprehensive evaluation using 25 publicly available scRNA-seq datasets and various simulation scenarios against five state-of-the-art methods demonstrates that scISR is better than other imputation methods in recovering scRNA-seq expression profiles via imputation. scISR consistently improves the quality of cluster analysis regardless of dropout rates, normalization techniques, and quantification schemes. The source code of scISR can be found on GitHub at https://github.com/duct317/scISR . 

Advances in single-cell RNA sequencing (scRNAseq) technologies have allowed us to study the heterogeneity of cell populations. The cell compositions of tissues from different hosts may vary greatly, indicating the condition of the hosts, from which the samples are collected. However, the high sequencing cost and the lack of fresh tissues make single-cell approaches less appealing. In many cases, it is practically impossible to generate single-cell data in a large number of subjects, making it challenging to monitor changes in cell type compositions in various diseases. Here we introduce a novel approach, named Deconvolution using Weighted Elastic Net (DWEN), that allows researchers to accurately estimate the cell type compositions from bulk data samples without the need of generating single-cell data. It also allows for the re-analysis of bulk data collected from rare conditions to extract more in-depth cell-type level insights. The approach consists of two modules. The first module constructs the cell type signature matrix from single-cell data while the second module estimates the cell type compositions of input bulk samples. In an extensive analysis using 20 datasets generated from scRNA-seq data of different human tissues, we demonstrate that DWEN outperforms current state-of-the-arts in estimating cell type compositions of bulk samples. 

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com . The R package will be deposited to CRAN as part of our PINSPlus software suite. 

Abstract Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods’ performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development. 

Abstract In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.