limma powers differential expression analyses for RNA-sequencing and microarray studies
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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TL;DR: The bioinformatics analyses suggest that adipose-derived mesenchymal stem cells may alleviate ulcerative colitis by communicating with macrophages to block inflammation.
Abstract: Ulcerative colitis is a chronic, non-specific inflammatory disease that affects mainly the colonic mucosa and submucosa. The pathogenesis of ulcerative colitis is unclear, which limits the development of effective treatments. In this study, single-cell sequencing data from 18 ulcerative colitis samples and 12 healthy controls were downloaded from the Single Cell Portal database, cell types were defined through cluster analysis, and genes in each cluster that were differentially expressed in ulcerative colitis were identified. These genes were enriched in functional pathways related to apoptosis, immunity and inflammation. Analysis using iTALK software suggested extensive communication among immune cells. Single-cell sequencing data from adipose-derived mesenchymal stem cells from three healthy female donors were obtained from the Sequence Read Archive database. The SingleR package was used to identify different cell types, for each of which a stemness score was calculated. Pseudotime analysis was performed to infer the trajectory of cells. SCENIC software was used to identify the gene regulatory network in adipose-derived mesenchymal stem cells, and iTALK software was performed to explore the relationship among macrophages, adipose-derived mesenchymal stem cells and enterocytes. Molecular docking confirmed the possibility of cell-cell interactions via binding between surface receptors and their ligands. The bulk data were downloaded and analyzed to validate the expression of genes. Our bioinformatics analyses suggest that ulcerative colitis involves communication between macrophages and enterocytes via ligand-receptor pairs. Our results further suggest that adipose-derived mesenchymal stem cells may alleviate ulcerative colitis by communicating with macrophages to block inflammation.
6 citations
TL;DR: Fibroblasts and the hub genes showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy and provides potential targets for developing new therapeutic agents for IPF patients.
Abstract: Background: Lung cancer is the most common comorbidity of idiopathic pulmonary fibrosis. Thus there is an urgent need for the research of IPF and carcinogenesis Objective: The objective of this study was to explore hub genes which are common in pulmonary fibrosis and lung cancer progression through bioinformatic analysis. Methods: All the analysis was performed in R software. Differentially expressed genes (DEGs) were explored by comparing gene expression profiles between IPF tissues and healthy lung tissues from GSE24206, GSE53845, GSE101286 and GSE110147 datasets. Venn Diagram analysis was used to identify the overlapping genes, while GO and KEGG pathway enrichment analysis were used to explore the biological functions of the DEGs using clusterprofiler package. Hub genes were identified by analyzing protein-protein interaction networks using Cytoscape software. Nomogram was constructed using the rms package. Tumor immune dysfunction and exclusion (TIDE) and Genomics of Drug Sensitivity in Cancer (GDSC) analysis was used to quantify the immunotherapy and chemotherapy sensitivity of non-small cell lung cancer (NSCLC) patients. Results: COL1A1, COL3A1, MMP1, POSTN1 and TIMP3 were identified as the top five hub genes. The five hub genes were used to construct a diagnostic nomogram that was validated in another IPF dataset. Since the hub genes were also associated with lung cancer progression, we found that the nomogram also had diagnostic value in NSCLC patients. These five genes achieved a statistically difference of overall survival in NSCLC patients (p < 0.05). The expression of the five hub genes was mostly enriched in fibroblasts. Fibroblasts and the hub genes also showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy. Conclusion: We identified five hub genes as potential biomarkers of IPF and NSCLC progression. This finding may give insight into the underlying molecular mechanisms of IPF and lung cancer progression and provides potential targets for developing new therapeutic agents for IPF patients.
6 citations
TL;DR: In this article, the authors identified and characterized a T vaginalis hypothetical protein, TVAG_157210 (TvAD1), as a surface protein that plays an integral role in parasite adherence to the host.
Abstract: Trichomonas vaginalis is a highly prevalent, sexually transmitted parasite which adheres to mucosal epithelial cells to colonize the human urogenital tract Despite adherence being crucial for this extracellular parasite to thrive within the host, relatively little is known about the mechanisms or key molecules involved in this process Here, we have identified and characterized a T vaginalis hypothetical protein, TVAG_157210 (TvAD1), as a surface protein that plays an integral role in parasite adherence to the host Quantitative proteomics revealed TvAD1 to be ∼4-fold more abundant in parasites selected for increased adherence (MA parasites) than the isogenic parental (P) parasite line De novo modeling suggested that TvAD1 binds N-acetylglucosamine (GlcNAc), a sugar comprising host glycosaminoglycans (GAGs) Adherence assays utilizing GAG-deficient cell lines determined that host GAGs, primarily heparan sulfate (HS), mediate adherence of MA parasites to host cells TvAD1 knockout (KO) parasites, generated using CRISPR-Cas9, were found to be significantly reduced in host cell adherence, a phenotype that is rescued by overexpression of TvAD1 in KO parasites In contrast, there was no significant difference in parasite adherence to GAG-deficient lines by KO parasites compared with wild-type, which is contrary to that observed for KO parasites overexpressing TvAD1 Isothermal titration calorimetric (ITC) analysis showed that TvAD1 binds to HS, indicating that TvAD1 mediates host cell adherence via HS interaction In addition to characterizing the role of TvAD1 in parasite adherence, these studies reveal a role for host GAG molecules in T vaginalis adherenceIMPORTANCE The ability of the sexually transmitted parasite Trichomonas vaginalis to adhere to its human host is critical for establishing and maintaining an infection Yet how parasites adhere to host cells is poorly understood In this study, we employed a novel adherence selection method to identify proteins involved in parasite adherence to the host This method led to the identification of a protein, with no previously known function, that is more abundant in parasites with increased capacity to bind host cells Bioinformatic modeling and biochemical analyses revealed that this protein binds a common component on the host cell surface proteoglycans Subsequent creation of parasites that lack this protein directly demonstrated that the protein mediates parasite adherence via an interaction with host cell proteoglycans These findings both demonstrate a role for this protein in T vaginalis adherence to the host and shed light on host cell molecules that participate in parasite colonization
6 citations
TL;DR: The results suggest that IL7R inhibits tumor growth by regulating the proportion of immune infiltrating cells in the tumor immune microenvironment and could be a beneficial prognostic marker in patients with lung adenocarcinoma and has great potential in immune therapy.
Abstract: Tumor microenvironment plays an important role in the development, progression, and prognosis of lung adenocarcinoma. Exploring new biomarkers based on the immune microenvironment of lung adenocarcinoma can effectively predict the prognosis and provide effective clinical treatment. In this study, we used the ESTIMATE algorithm to score the immune and stromal components in lung adenocarcinoma data downloaded from the TCGA database. The result showed that the immune/stromal score was associated with clinical features and prognosis of lung adenocarcinoma patients. Interleukin-7 receptor (IL7R) is an important prognostic biomarker identified by intersection analysis of protein-protein interaction networks and Cox regression survival analysis. According to TCGA and Oncomine database analysis, IL7R expression in adenocarcinoma tissues was significantly lower than that in normal lung tissues and was further verified in clinical tissue samples. Survival analysis showed IL7R was an independent prognostic factor of lung adenocarcinoma. IL7R expression was positively correlated with the overall survival and progression-free survival of lung adenocarcinoma patients and negatively correlated with tumor size. Our results suggest that IL7R inhibits tumor growth by regulating the proportion of immune infiltrating cells in the tumor immune microenvironment. IL7R could be a beneficial prognostic marker in patients with lung adenocarcinoma and has great potential in immune therapy.
6 citations
TL;DR: In this paper , the authors performed a systematic review and patient-level meta-analysis of COVID-19 transcriptomic signatures, spanning disease severity, from whole blood, PBMCs, and BALF.
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
83,420 citations
"limma powers differential expressio..." refers background in this paper
...Users can control either the family-wise type I error rate or the false discovery rate (46)....
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TL;DR: An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
43,884 citations
"limma powers differential expressio..." refers methods in this paper
...Such a plot is called a Bland-Altman plot [36] or a Tukey mean-difference plot [10]....
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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations