kegg pathway analysis r tutorialkegg pathway analysis r tutorial

The gene ID system used by kegga for each species is determined by KEGG. The multi-types and multi-groups expression data can be visualized in one pathway map. Springer Nature. Examples of widely used statistical enrichment methods are introduced as well. How to perform KEGG pathway analysis in R? Note. PubMedGoogle Scholar. Policy. An over-represention analysis is then done for each set. ADD COMMENT link 5.4 years ago by Fabio Marroni 2.9k. Examples are "Hs" for human for "Mm" for mouse. Ignored if species.KEGG or is not NULL or if gene.pathway and pathway.names are not NULL. In the bitr function, the param fromType should be the same as keyType from the gseGO function above (the annotation source). These statistical FEA methods assess This is . Functional Analysis for RNA-seq | Introduction to DGE - ARCHIVED Luo W, Pant G, Bhavnasi YK, Blanchard SG, Brouwer C. Pathview Web: user friendly pathway visualization and data integration. I have a couple hundred nucleotide sequences from a Fungus genome. Nucleic Acids Res, 2017, Web Server issue, doi: Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration Immunology. kegga can be used for any species supported by KEGG, of which there are more than 14,000 possibilities. We will focus on KEGG pathways here and solve 2013 there are 450 reference pathways in KEGG. Gene Data accepts data matrices in tab- or comma-delimited format (txt or csv). GAGE: generally applicable gene set enrichment for pathway analysis. In general, there will be a pair of such columns for each gene set and the name of the set will appear in place of "DE". First column gives gene IDs, second column gives pathway IDs. To perform GSEA analysis of KEGG gene sets, clusterProfiler requires the genes to be . Pathway Selection set to Auto on the New Analysis page. BMC Bioinformatics, 2009, 10, pp. That's great, I didn't know very useful if you are already using edgeR! Several accessor functions are provided to For metabolite (set) enrichment analysis (MEA/MSEA) users might also be interested in the The resulting list object can be used for various ORA or GSEA methods, e.g. The funding body did not play any role in the design of the study, or collection, analysis, or interpretation of data, or in writing the manuscript. 1, Example Gene Compared to other GESA implementations, fgsea is very fast. If Entrez Gene IDs are not the default, then conversion can be done by specifying "convert=TRUE". In this case, the subset is your set of under or over expressed genes. 2007. provided by Bioconductor packages. organism data packages and/or Bioconductors Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. We previously developed an R/BioConductor package called Pathview, which maps, integrates and visualizes a wide range of data onto KEGG pathway graphs.Since its publication, Pathview has been widely used in omics studies and data analyses, and has become the leading tool in its category. roy.granit 880. Manage cookies/Do not sell my data we use in the preference centre. The goana method for MArrayLM objects produces a data frame with a row for each GO term and the following columns: number of up-regulated differentially expressed genes. all genes profiled by an assay) and assess whether annotation categories are Please consider contributing to my Patreon where I may do merch and gather ideas for future content:https://www.patreon.com/AlexSoupir The row names of the data frame give the GO term IDs. The resulting list object can be used adjust analysis for gene length or abundance? PANEV: an R package for a pathway-based network visualization Either a vector of length nrow(de) or the name of the column of de$genes containing the Entrez Gene IDs. number of down-regulated differentially expressed genes. Tutorial: RNA-seq differential expression & pathway analysis with Sailfish, DESeq2, GAGE, and Pathview, https://github.com/stephenturner/annotables, gage package workflow vignette for RNA-seq pathway analysis, Click here if you're looking to post or find an R/data-science job, Click here to close (This popup will not appear again). KEGG analysis implied that the PI3K/AKT signaling pathway might play an important role in treating IS by HXF. The authors declare that they have no competing interests. However, these options are NOT needed if your data is already relative Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. R-HSA, R-MMU, R-DME, R-CEL, ). Genome Biology 11, R14. Reconstruct (used to be called Reconstruct Pathway) is the basic mapping tool used for linking KO annotation (K number assignment) data to KEGG pathway maps, BRITE hierarchies and tables, and KEGG modules. Ontology Options: [BP, MF, CC] We can use the bitr function for this (included in clusterProfiler). Understand the theory of how functional enrichment tools yield statistically enriched functions or interactions. Gene Ontology and KEGG Enrichment Analysis - GitHub Pages xX _gbH}[fn6;m"K:R/@@]DWwKFfB$62LD(M+R`wG[HA$:zwD-Tf+i+U0 IMK72*SR2'&(M7 p]"E$%}JVN2Ne{KLG|ad>mcPQs~MoMC*yD"V1HUm(68*c0*I$8"*O4>oe A~5k1UNz&q QInVO2I/Q{Kl. Emphasizes the genes overlapping among different gene sets. keyType This is the source of the annotation (gene ids). The options vary for each annotation. Also, you just have the two groups no complex contrasts like in limma. . Data 2, Example Compound PANEV: an R package for a pathway-based network visualization. in using R in general, you may use the Pathview Web server: pathview.uncc.edu and its comprehensive pathway analysis workflow. data.frame giving full names of pathways. and visualization. KEGG stands for, Kyoto Encyclopedia of Genes and Genomes. The KEGG database contains curated sets of genes that are known to interact in the same biological pathway. If 260 genes are categorized as axon guidance (2.6% of all genes have category axon guidance), and in an experiment we find 1000 genes are differentially expressed and 200 of those genes are in the category axon guidance (20% of DE genes have category axon guidance), is that significant? As a result, the advantage of the KEGG-PATH model is demonstrated through the functional analysis of the bovine mammary transcriptome during lactation. First column gives pathway IDs, second column gives pathway names. The final video in the pipeline! https://doi.org/10.1101/060012. %PDF-1.5 If you intend to do a full pathway analysis plus data visualization (or integration), you need to set VP Project design, implementation, documentation and manuscript writing. 2016. for ORA or GSEA methods, e.g. Unlike the goseq package, the gene identifiers here must be Entrez Gene IDs and the user is assumed to be able to supply gene lengths if necessary. First, it is useful to get the KEGG pathways: Of course, hsa stands for Homo sapiens, mmu would stand for Mus musuculus etc. Determine how functions are attributed to genes using Gene Ontology terms. This will help the Pathview project in return. a character vector of Entrez Gene IDs, or a list of such vectors, or an MArrayLM fit object. Specify the layout, style, and node/edge or legend attributes of the output graphs. Figure 2: Batch ORA result of GO slim terms using 3 test gene sets. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. 2. topGO Example Using Kolmogorov-Smirnov Testing Our first example uses Kolmogorov-Smirnov Testing for enrichment testing of our arabadopsis DE results, with GO annotation obtained from the Bioconductor database org.At.tair.db. To aid interpretation of differential expression results, a common technique is to test for enrichment in known gene sets. Note. 0. The results were biased towards significant Down p-values and against significant Up p-values. Could anyone please suggest me any good R package? systemPipeR package. under the org argument (e.g. hsa, ath, dme, mmu, ). Pathview: an R/Bioconductor package for pathway-based data integration Duan, Yuzhu, Daniel S Evans, Richard A Miller, Nicholas J Schork, Steven R Cummings, and Thomas Girke. Thanks. See alias2Symbol for other possible values for species. and numerous statistical methods and tools (generally applicable gene-set enrichment (GAGE) (), GSEA (), SPIA etc.) Not adjusted for multiple testing. Cookies policy. The following load_reacList function returns the pathway annotations from the reactome.db Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a pre-defined set of genes (ex: those beloging to a specific GO term or KEGG pathway) shows statistically significant, concordant differences between two biological states. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The MArrayLM method extracts the gene sets automatically from a linear model fit object. When users select "Sort by Fold Enrichment", the minimum pathway size is raised to 10 to filter out noise from tiny gene sets. Please check the Section Basic Analysis and the help info on the function for details. These include among many other I am using R/R-studio to do some analysis on genes and I want to do a GO-term analysis. Discuss functional analysis using over-representation analysis, functional class scoring, and pathway topology methods. In case of so called over-represention analysis (ORA) methods, such as Fishers The MArrayLM methods performs over-representation analyses for the up and down differentially expressed genes from a linear model analysis. See http://www.kegg.jp/kegg/catalog/org_list.html or http://rest.kegg.jp/list/organism for possible values. GS Testing and manuscript review. Gene ontology analysis for RNA-seq: accounting for selection bias. In addition, the expression of several known defense related genes in lettuce and DEGs selected from RNA-Seq analysis were studied by RT-qPCR (described in detail in Supplementary Text S1 ), using the method described previously ( De . expression levels or differential scores (log ratios or fold changes). Functional Enrichment Analysis | GEN242 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. p-value for over-representation of the GO term in the set. MetaboAnalystR package that interfaces with the MataboAnalyst web service. The mRNA expression of the top 10 potential targets was verified in the brain tissue. used for functional enrichment analysis (FEA). The statistical approach provided here is the same as that provided by the goseq package, with one methodological difference and a few restrictions. I currently have 10 separate FASTA files, each file is from a different species. . Now, some filthy details about the parameters for gage. continuous/discrete data, matrices/vectors, single/multiple samples etc. But, our pathway analysis downstream will use KEGG pathways, and genes in KEGG pathways are annotated with Entrez gene IDs. in the vignette of the fgsea package here. edge base for understanding biological pathways and functions of cellular processes. KEGG pathways. ENZYME EVIDENCE EVIDENCEALL FLYBASE FLYBASECG FLYBASEPROT Tutorial: RNA-seq differential expression & pathway analysis with Data Ignored if gene.pathway and pathway.names are not NULL. more highly enriched among the highest ranking genes compared to random That's great, I didn't know. Examples of KEGG format are "hsa" for human, "mmu" for mouse of "dme" for fly. 1 Overview. both the query and the annotation databases can be composed of genes, proteins, Basics of this are sort of light in the official Aldex tutorial, which frames in the more general RNAseq/whatever. The top five were photosynthesis, phenylpropanoid biosynthesis, metabolism of starch and sucrose, photosynthesis-antenna proteins, and zeatin biosynthesis (Figure 4B, Table S5). lookup data structure for any organism supported by BioMart (H Backman and Girke 2016). R: Gene Ontology or KEGG Pathway Analysis - Massachusetts Institute of This tutorial shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathway analysis using GAGE.Using data from GSE37704, with processed data available on Figshare DOI: 10.6084/m9.figshare.1601975.This dataset has six samples from GSE37704, where expression was quantified by either: (A) mapping to to GRCh38 using STAR then counting reads mapped to genes with featureCounts . The KEGG database contains curated sets of genes that are known to interact in the same biological pathway. All authors have read and approved the final version of the manuscript. The orange diamonds represent the pathways belonging to the network without connection with any candidate gene, Comparison between PANEV and reference study results (Qiu et al., 2014), PANEV enrichment result of KEGG pathways considering the 452 genes identified by the Qiu et al. This example shows the multiple sample/state integration with Pathview Graphviz view. The output from kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column.. Summary of the tabular result obtained by PANEV using the data from Qui et al. See 10.GeneSetTests for a description of other functions used for gene set testing. is a generic concept, including multiple types of whether functional annotation terms are over-represented in a query gene set. 2016. By default this is obtained automatically by getGeneKEGGLinks(species.KEGG). In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules. Well use these KEGG pathway IDs downstream for plotting. It organizes data in several overlapping ways, including pathway, diseases, drugs, compounds and so on. three-letter KEGG species identifier. View the top 20 enriched KEGG pathways with topKEGG. In the "FS7 vs. FS0" comparison, 701 DEGs were annotated to 111 KEGG pathways. Approximate time: 120 minutes. KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. https://doi.org/10.1186/s12859-020-3371-7, DOI: https://doi.org/10.1186/s12859-020-3371-7. PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Entrez Gene identifiers. The KEGG pathway diagrams are created using the R package pathview (Luo and Brouwer . package for a species selected under the org argument (e.g. For more information please see the full documentation here: https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html, Follow along interactively with the R Markdown Notebook: Extract the entrez Gene IDs from the data frame fit2$genes. There are four types of KEGG modules: pathway modules - representing tight functional units in KEGG metabolic pathway maps, such as M00002 (Glycolysis, core module involving three-carbon compounds . The following introduces gene and protein annotation systems that are widely used for functional enrichment analysis (FEA). Both the absolute or original expression levels and the relative expression levels (log2 fold changes, t-statistics) can be visualized on pathways. The following introduceds a GOCluster_Report convenience function from the Search (used to be called Search Pathway) is the traditional tool for searching mapped objects in the user's dataset and mark them in red. By default this is obtained automatically using getKEGGPathwayNames(species.KEGG, remove=TRUE). It is normal for this call to produce some messages / warnings. 1 and Example Gene Luo W, Pant G, Bhavnasi YK, Blanchard SG, Brouwer C. Pathview Web: user friendly pathway visualization and data integration. Sci. statement and Posted on August 28, 2014 by January in R bloggers | 0 Comments. A sample plot from ReactomeContentService4R is shown below. This more time consuming step needs to be performed only once. Please also cite GAGE paper if you are doing pathway analysis besides visualization, i.e. Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. How to do KEGG Pathway Analysis with a gene list? Dipartimento Agricoltura, Ambiente e Alimenti, Universit degli Studi del Molise, 86100, Campobasso, Italy, Department of Support, Production and Animal Health, School of Veterinary Medicine, So Paulo State University, Araatuba, So Paulo, 16050-680, Brazil, Istituto di Zootecnica, Universit Cattolica del Sacro Cuore, 29122, Piacenza, Italy, Dipartimento di Bioscienze e Territorio, Universit degli Studi del Molise, 86090, Pesche, IS, Italy, Dipartimento di Medicina Veterinaria, Universit di Perugia, 06126, Perugia, Italy, Dipartimento di Scienze Agrarie ed Ambientali, Universit degli Studi di Udine, 33100, Udine, Italy, You can also search for this author in GENENAME GO GOALL MAP ONTOLOGY ONTOLOGYALL kegg.gs and go.sets.hs. The default for kegga with species="Dm" changed from convert=TRUE to convert=FALSE in limma 3.27.8. Acad. for pathway analysis. Genome-wide association study of milk fatty acid composition in Italian Simmental and Italian Holstein cows using single nucleotide polymorphism arrays. Frontiers | Assessment of transcriptional reprogramming of lettuce Moreover, HXF significantly reduced neurological impairment, cerebral infarct volume, brain index, and brain histopathological damage in I/R rats. endobj The first part shows how to generate the proper catdb Bioinformatics - KEGG Pathway Visualization in R - YouTube Possible values are "BP", "CC" and "MF". Falcon, S, and R Gentleman. The row names of the data frame give the GO term IDs. However, the latter are more frequently used. A very useful query interface for Reactome is the ReactomeContentService4R package. KEGG Module Enrichment Analysis | R-bloggers (2014). However, gage is tricky; note that by default, it makes a [] Network pharmacology-based prediction and validation of the active Enrichment Analysis (GSEA) algorithms use as query a score ranked list (e.g. The default method accepts a gene set as a vector of gene IDs or multiple gene sets as a list of vectors. Example 4 covers the full pathway analysis. Figure 1: Fireworks plot depicting genome-wide view of reactome pathways. MD Conception of biologically relevant functionality, project design, oversight and, manuscript review. Correspondence to kegga requires an internet connection unless gene.pathway and pathway.names are both supplied.. If NULL then all Entrez Gene IDs associated with any gene ontology term will be used as the universe. The ability to supply data.frame annotation to kegga means that kegga can in principle be used in conjunction with any user-supplied set of annotation terms. For KEGG pathway enrichment using the gseKEGG() function, we need to convert id types. (2010). column number or column name specifying for which coefficient or contrast differential expression should be assessed. This R Notebook describes the implementation of over-representation analysis using the clusterProfiler package. transcript or protein IDs, for example ENTREZ Gene, Symbol, RefSeq, GenBank Accession Number, #ok, so most variation is in the first 2 axes for pathway # 3-4 axes for kegg p=plot_ordination(pw,ord_pw,type="samples",color="Facility",shape="Genotype") p=p+geom . More importantly, we reverted to 0.76 for default gene counting method, namely all protein-coding genes are used as the background by default . 10.1093/bioinformatics/btt285. estimation is based on an adaptive multi-level split Monte-Carlo scheme. This example shows the ID mapping capability of Pathview. https://doi.org/10.1093/bioinformatics/btl567. by fgsea. In addition, this work also attempts to preliminarily estimate the impact direction of each KEGG pathway by a gradient analysis method from principal component analysis (PCA). 2005;116:52531. PDF KEGGgraph: a graph approach to KEGG PATHWAY in R and Bioconductor Gene Data and/or Compound Data will also be taken as the input data << Its P-value KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. uniquely mappable to KEGG gene IDs. Natl. The knowl-edge from KEGG has proven of great value by numerous work in a wide range of fields [Kanehisaet al., 2008]. gene.data This is kegg_gene_list created above Over-Representation Analysis with ClusterProfiler The default for kegga with species="Dm" changed from convert=TRUE to convert=FALSE in limma 3.27.8. Pathview Science is collaborative and learning is the same.The image at the bottom left of the thumbnail is modified from AllGenetics.EU. Over-representation (or enrichment) analysis is a statistical method that determines whether genes from pre-defined sets (ex: those beloging to a specific GO term or KEGG pathway) are present more than would be expected (over-represented) in a subset of your data. toType in the bitr function has to be one of the available options from keyTypes(org.Dm.eg.db) and must map to one of kegg, ncbi-geneid, ncib-proteinid or uniprot because gseKEGG() only accepts one of these 4 options as its keytype parameter. The goseq package has additional functionality to convert gene identifiers and to provide gene lengths. Similar to above. Over-representation (or enrichment) analysis is a statistical method that determines whether genes from pre-defined sets (ex: those beloging to a specific GO term or KEGG pathway) are present more than would be expected (over-represented) in a subset of your data. /Filter /FlateDecode query the database. U. S. A. Pathway-based analysis is a powerful strategy widely used in omics studies. The fitted model object of the leukemia study from Chapter 2, fit2, has been loaded in your workspace. Figure 3: Enrichment plot for selected pathway. concordance:KEGGgraph.tex:KEGGgraph.Rnw:1 22 1 1 0 35 1 1 2 4 0 1 2 18 1 1 2 1 0 1 1 3 0 1 2 6 1 1 3 5 0 2 2 1 0 1 1 8 0 1 2 1 1 1 2 1 0 1 1 17 0 2 1 8 0 1 2 10 1 1 2 1 0 1 1 5 0 2 1 7 0 1 2 3 1 1 2 1 0 1 1 12 0 1 2 1 1 1 2 13 0 1 2 3 1 1 2 1 0 1 1 13 0 2 2 14 0 1 2 7 1 1 2 1 0 4 1 6 0 1 1 7 0 1 2 4 1 1 2 1 0 4 1 8 0 1 2 5 1 1 17 2 1 1 2 1 0 2 1 1 8 6 0 1 1 1 2 2 1 1 4 7 0 1 2 4 1 1 2 1 0 4 1 8 0 1 2 29 1 1 2 1 0 4 1 7 0 1 2 6 1 1 2 1 0 4 1 1 2 5 1 1 2 4 0 1 2 7 1 1 2 4 0 1 2 14 1 1 2 1 0 2 1 17 0 2 1 11 0 1 2 4 1 1 2 1 0 1 2 1 1 1 2 5 1 4 0 1 2 5 1 1 2 4 0 1 2 1 1 1 2 1 0 1 1 7 0 2 1 8 0 1 2 2 1 1 2 1 0 3 1 3 0 1 2 2 1 1 9 12 0 1 2 2 1 1 2 1 0 2 1 1 3 5 0 1 2 12 1 1 2 42 0 1 2 11 1 102 (43): 1554550. Pathview Web: user friendly pathway visualization and data integration following uses the keegdb and reacdb lists created above as annotation systems. Part of 161, doi: 10.1186/1471-2105-10-161, Pathway based data integration and visualization, Example Gene Data In this case, the subset is your set of under or over expressed genes. ADD COMMENT link 5.4 years ago by roy.granit 880. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv. KEGG pathway are divided into seven categories. trend=FALSE is equivalent to prior.prob=NULL. First, it is useful to get the KEGG pathways: Of course, "hsa" stands for Homo sapiens, "mmu" would stand for Mus musuculus etc. This tutorial shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathway analysis using. 60 0 obj Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. goana uses annotation from the appropriate Bioconductor organism package. This includes code to inspect how the annotations ShinyGO 0.77 - South Dakota State University Palombo V, Milanesi M, Sgorlon S, Capomaccio S, Mele M, Nicolazzi E, et al. Policy. BMC Bioinformatics 21, 46 (2020). To aid interpretation of differential expression results, a common technique is to test for enrichment in known gene sets. By the way, if I want to visualise say the logFC from topTable, I can create a named numeric vector in one go: Another useful package is SPIA; SPIA only uses fold changes and predefined sets of differentially expressed genes, but it also takes the pathway topology into account. The graph helps to interpret functional profiles of cluster of genes. Pathways are stored and presented as graphs on the KEGG server side, where nodes are First, the package requires a vector or a matrix with, respectively, names or rownames that are ENTREZ IDs. If you have suggestions or recommendations for a better way to perform something, feel free to let me know! If prior.prob=NULL, the function computes one-sided hypergeometric tests equivalent to Fisher's exact test. by fgsea. In addition Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Not adjusted for multiple testing. SBGNview Quick Start - bioconductor.org /Length 691 Its vignette provides many useful examples, see here. data.frame linking genes to pathways. Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration Which, according to their philosphy, should work the same way. Subramanian, A, P Tamayo, V K Mootha, S Mukherjee, B L Ebert, M A Gillette, A Paulovich, et al. The violet diamonds represent the first-level (1L) pathways (in this case: Type I diabetes mellitus, Insulin resistance, and AGE-RAGE signaling pathway in diabetic complications) connected with candidate genes. In the example of org.Dm.eg.db, the options are: ACCNUM ALIAS ENSEMBL ENSEMBLPROT ENSEMBLTRANS ENTREZID systemPipeR: Workflow Design and Reporting Environment, Environments dplyr, tidyr and some SQLite, https://doi.org/10.1093/bioinformatics/btl567, https://doi.org/10.1186/s12859-016-1241-0, Many additional packages can be found under Biocs KEGG View page. GAGE: generally applicable gene set enrichment for pathway analysis. Luo W, Friedman M, etc. http://www.kegg.jp/kegg/catalog/org_list.html. If you supply data as original expression levels, but you want to visualize the relative expression levels (or differences) between two states. Commonly used gene sets include those derived from KEGG pathways, Gene Ontology terms, MSigDB, Reactome, or gene groups that share some other functional annotations, etc. A wide range of databases and resources have been built (KEGG (), Reactome (), Wikipathways (), MetaCyc (), PANTHER (), Pathway Commons etc.) If this is done, then an internet connection is not required. spatial and temporal information, tissue/cell types, inputs, outputs and connections. are organized and how to access them. optional numeric vector of the same length as universe giving the prior probability that each gene in the universe appears in a gene set. This vector can be used to correct for unwanted trends in the differential expression analysis associated with gene length, gene abundance or any other covariate (Young et al, 2010). Entrez Gene IDs can always be used. . >> The limma package is already loaded.