Data 1, Department of Bioinformatics and Genomics. By default this is obtained automatically by getGeneKEGGLinks(species.KEGG). Luo W, Friedman M, etc. There are many options to do pathway analysis with R and BioConductor. Acad. GO terms or KEGG pathways) as a network (helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories). Use of this site constitutes acceptance of our User Agreement and Privacy License: Artistic-2.0. First, it is useful to get the KEGG pathways: Of course, "hsa" stands for Homo sapiens, "mmu" would stand for Mus musuculus etc. Marco Milanesi was supported by grant 2016/057877, So Paulo Research Foundation (FAPESP). Test for enriched KEGG pathways with kegga. All authors have read and approved the final version of the manuscript. More importantly, we reverted to 0.76 for default gene counting method, namely all protein-coding genes are used as the background by default . The following load_keggList function returns the pathway annotations from the KEGG.db package for a species selected kegga requires an internet connection unless gene.pathway and pathway.names are both supplied.. The plotEnrichment can be used to create enrichment plots. Now, lets process the results to pull out the top 5 upregulated pathways, then further process that just to get the IDs. The default goana and kegga methods accept a vector prior.prob giving the prior probability that each gene in the universe appears in a gene set. If prior probabilities are specified, then a test based on the Wallenius' noncentral hypergeometric distribution is used to adjust for the relative probability that each gene will appear in a gene set, following the approach of Young et al (2010). Well use these KEGG pathway IDs downstream for plotting. The network graph visualization helps to interpret functional profiles of . I would suggest KEGGprofile or KEGGrest. KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. Policy. To perform GSEA analysis of KEGG gene sets, clusterProfiler requires the genes to be . To visualise the changes on the pathway diagram from KEGG, one can use the package pathview. Im using D melanogaster data, so I install and load the annotation org.Dm.eg.db below. VP Project design, implementation, documentation and manuscript writing. Not adjusted for multiple testing. For simplicity, the term gene sets is used See help on the gage function with, For experimentally derived gene sets, GO term groups, etc, coregulation is commonly the case, hence. Approximate time: 120 minutes. This is . An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv. The output from kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column.. PATH PMID REFSEQ SYMBOL UNIGENE UNIPROT. KEGG analysis implied that the PI3K/AKT signaling pathway might play an important role in treating IS by HXF. The limma package is already loaded. The gene ID system used by kegga for each species is determined by KEGG. Results. The mapping against the KEGG pathways was performed with the pathview R package v1.36. 66 0 obj Provided by the Springer Nature SharedIt content-sharing initiative. spatial and temporal information, tissue/cell types, inputs, outputs and connections. 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. 60 0 obj The following introduces gene and protein annotation systems that are widely used for functional enrichment analysis (FEA). By default, kegga obtains the KEGG annotation for the specified species from the http://rest.kegg.jp website. A wide range of databases and resources have been built (KEGG (), Reactome (), Wikipathways (), MetaCyc (), PANTHER (), Pathway Commons etc.) Note we use the demo gene set data, i.e. 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. In this case, the subset is your set of under or over expressed genes. signatureSearch: environment for gene expression signature searching and functional interpretation. Nucleic Acids Res., October. The default for kegga with species="Dm" changed from convert=TRUE to convert=FALSE in limma 3.27.8. You can also do that using edgeR. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. Nucleic Acids Res, 2017, Web Server issue, doi: 10.1093/ nar/gkx372 Note. Basics of this are sort of light in the official Aldex tutorial, which frames in the more general RNAseq/whatever. developed for pathway analysis. Next, get results for the HoxA1 knockdown versus control siRNA, and reorder them by p-value. 2005;116:52531. Set the species to "Hs" for Homo sapiens. While tricubeMovingAverage does not enforce monotonicity, it has the advantage of numerical stability when de contains only a small number of genes. To aid interpretation of differential expression results, a common technique is to test for enrichment in known gene sets. edge base for understanding biological pathways and functions of cellular processes. These include among many other The cnetplot depicts the linkages of genes and biological concepts (e.g. (2014). When users select "Sort by Fold Enrichment", the minimum pathway size is raised to 10 to filter out noise from tiny gene sets. gene list (Sergushichev 2016). 2016. However, the latter are more frequently used. The species can be any character string XX for which an organism package org.XX.eg.db is installed. For human and mouse, the default (and only choice) is Entrez Gene ID. Pathways are stored and presented as graphs on the KEGG server side, where nodes are 161, doi. 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. If you supply data as original expression levels, but you want to visualize the relative expression levels (or differences) between two states. and visualization. The resulting list object can be used for various ORA or GSEA methods, e.g. Additional examples are available . Ignored if gene.pathway and pathway.names are not NULL. terms. Gene Data accepts data matrices in tab- or comma-delimited format (txt or csv). First column gives gene IDs, second column gives pathway IDs. Understand the theory of how functional enrichment tools yield statistically enriched functions or interactions. Compared to other GESA implementations, fgsea is very fast. Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration The row names of the data frame give the GO term IDs. For metabolite (set) enrichment analysis (MEA/MSEA) users might also be interested in the Incidentally, we can immediately make an analysis using gage. For kegga, the species name can be provided in either Bioconductor or KEGG format. View the top 20 enriched KEGG pathways with topKEGG. https://doi.org/10.1186/s12859-020-3371-7, DOI: https://doi.org/10.1186/s12859-020-3371-7. Palombo V, Milanesi M, Sgorlon S, Capomaccio S, Mele M, Nicolazzi E, et al. That's great, I didn't know. http://genomebiology.com/2010/11/2/R14. California Privacy Statement, Entrez Gene identifiers. This example covers an integration pathway analysis workflow based on Pathview. Please check the Section Basic Analysis and the help info on the function for details. Genome Biology 11, R14. either the standard Hypergeometric test or a conditional Hypergeometric test that uses the all genes profiled by an assay) and assess whether annotation categories are systemPipeR package. We also see the importance of exploring the results a little further when P53 pathway is upregulated as a whole but P53, while having higher levels in the P53+/+ samples, didn't show as much of an increase by treatment than did P53-/-.Creating DESeq2 object:https://www.youtube.com/watch?v=5z_1ziS0-5wCalculating Differentially Expressed genes:https://www.youtube.com/watch?v=ZjMfiPLuwN4Series github with the subsampled data so the whole pipeline can be done on most computers.https://github.com/ACSoupir/Bioinformatics_YouTubeI use these videos to practice speaking and teaching others about processes. I want to perform KEGG pathway analysis preferably using R package. Traffic: 2118 users visited in the last hour, http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html, http://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html, User Agreement and Privacy However, there are a few quirks when working with this package. It organizes data in several overlapping ways, including pathway, diseases, drugs, compounds and so on. Moreover, HXF significantly reduced neurological impairment, cerebral infarct volume, brain index, and brain histopathological damage in I/R rats. gene.data This is kegg_gene_list created above BMC Bioinformatics, 2009, 10, pp. 2016. A sample plot from ReactomeContentService4R is shown below. 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. BMC Bioinformatics 21, 46 (2020). 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 . lookup data structure for any organism supported by BioMart (H Backman and Girke 2016). KEGG MODULE is a collection of manually defined functional units, called KEGG modules and identified by the M numbers, used for annotation and biological interpretation of sequenced genomes. In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules. The MArrayLM object computes the prior.prob vector automatically when trend is non-NULL. Params: 0. exact and hypergeometric distribution tests, the query is usually a list of include all terms meeting a user-provided P-value cutoff as well as GO Slim hsa, ath, dme, mmu, ). trend=FALSE is equivalent to prior.prob=NULL. The last two column names above assume one gene set with the name DE. BMC Bioinformatics, 2009, 10, pp. This param is used again in the next two steps: creating dedup_ids and df2. Pathway Selection set to Auto on the New Analysis page. Numeric value between 0 and 1. character string specifying the species. annotations, such as KEGG and Reactome. a character vector of Entrez Gene IDs, or a list of such vectors, or an MArrayLM fit object. The mRNA expression of the top 10 potential targets was verified in the brain tissue. An over-represention analysis is then done for each set. The following load_reacList function returns the pathway annotations from the reactome.db The final video in the pipeline! optional numeric vector of the same length as universe giving the prior probability that each gene in the universe appears in a gene set. used for functional enrichment analysis (FEA). as to handle metagenomic data. In the "FS3 vs. FS0" group, 937 DEGs were enriched in 111 KEGG pathways. Part of Call, Since we mapped and counted against the Ensembl annotation, our results only have information about Ensembl gene IDs. 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. First, import the countdata and metadata directly from the web. 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. Nucleic Acids Res, 2017, Web Server issue, doi: Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration data.frame giving full names of pathways. in the vignette of the fgsea package here. (2014) study and considering three levels of interactions Type I diabetes mellitus, Insulin resistance, and AGE-RAGE signaling pathway in diabetic complications as 1L pathways, Screenshot of network-based visualization result obtained by PANEV using the data from Qui et al. for pathway analysis. To aid interpretation of differential expression results, a common technique is to test for enrichment in known gene sets. If you intend to do a full pathway analysis plus data visualization (or integration), you need to set The output from kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column.. data.frame linking genes to pathways. The sets in There are many options to do pathway analysis with R and BioConductor. provided by Bioconductor packages. The top five were photosynthesis, phenylpropanoid biosynthesis, metabolism of starch and sucrose, photosynthesis-antenna proteins, and zeatin biosynthesis (Figure 4B, Table S5). If NULL then all Entrez Gene IDs associated with any gene ontology term will be used as the universe. organism KEGG Organism Code: The full list is here: https://www.genome.jp/kegg/catalog/org_list.html (need the 3 letter code). In contrast to this, Gene Set KEGG view retains all pathway meta-data, i.e. query the database. The options vary for each annotation. Specify the layout, style, and node/edge or legend attributes of the output graphs. The format of the IDs can be seen by typing head(getGeneKEGGLinks(species)), for examplehead(getGeneKEGGLinks("hsa")) or head(getGeneKEGGLinks("dme")). Which, according to their philosphy, should work the same way. consortium in an SQLite database. 2005. expression levels or differential scores (log ratios or fold changes). . I currently have 10 separate FASTA files, each file is from a different species. There are four KEGG mapping tools as summarized below. If Entrez Gene IDs are not the default, then conversion can be done by specifying "convert=TRUE". 1, Example Gene by fgsea. Mariasilvia DAndrea. Posted on August 28, 2014 by January in R bloggers | 0 Comments. The fgsea function performs gene set enrichment analysis (GSEA) on a score ranked Note that KEGG IDs are the same as Entrez Gene IDs for most species anyway. Frequently, you also need to the extra options: Control/reference, Case/sample, and Compare in the dialogue box. http://www.kegg.jp/kegg/catalog/org_list.html. The final video in the pipeline! Sci. pathway.id The user needs to enter this. I define this as kegg_organism first, because it is used again below when making the pathview plots. See alias2Symbol for other possible values. 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. In case of so called over-represention analysis (ORA) methods, such as Fishers Users wanting to use Entrez Gene IDs for Drosophila should set convert=TRUE, otherwise fly-base CG annotation symbol IDs are assumed (for example "Dme1_CG4637"). Now, some filthy details about the parameters for gage. Possible values include "Hs" (human), "Mm" (mouse), "Rn" (rat), "Dm" (fly) or "Pt" (chimpanzee), but other values are possible if the corresponding organism package is available. compounds or other factors. p-value for over-representation of GO term in up-regulated genes. UNIPROT, Enzyme Accession Number, etc. Sergushichev, Alexey. Gene Data and/or Compound Data will also be taken as the input data for pathway analysis. SS Testing and manuscript review. 2005; Sergushichev 2016; Duan et al. enrichment methods are introduced as well. 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. Figure 1: Fireworks plot depicting genome-wide view of reactome pathways. Both the absolute or original expression levels and the relative expression levels (log2 fold changes, t-statistics) can be visualized on pathways. %PDF-1.5 keyType This is the source of the annotation (gene ids). This example shows the ID mapping capability of Pathview. This example shows the multiple sample/state integration with Pathview Graphviz view. Terms and Conditions, Check which options are available with the keytypes command, for example keytypes(org.Dm.eg.db). Alternatively one can supply the required pathway annotation to kegga in the form of two data.frames. The If you intend to do a full pathway analysis plus data visualization (or integration), you need to set Pathway Selection below to Auto. uniquely mappable to KEGG gene IDs. This R Notebook describes the implementation of over-representation analysis using the clusterProfiler package. By default this is obtained automatically using getKEGGPathwayNames(species.KEGG, remove=TRUE). Set up the DESeqDataSet, run the DESeq2 pipeline. Possible values are "BP", "CC" and "MF". The resulting list object can be used See all annotations available here: http://bioconductor.org/packages/release/BiocViews.html#___OrgDb (there are 19 presently available). Ignored if universe is NULL. GAGE: generally applicable gene set enrichment for pathway analysis. For Drosophila, the default is FlyBase CG annotation symbol. In addition Ignored if universe is NULL. The MArrayLM method extracts the gene sets automatically from a linear model fit object. Please cite our paper if you use this website. 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: The default method accepts a gene set as a vector of gene IDs or multiple gene sets as a list of vectors. 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. are organized and how to access them. 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 MetaboAnalystR package that interfaces with the MataboAnalyst web service. very useful if you are already using edgeR! Will be computed from covariate if the latter is provided. keyType one of kegg, ncbi-geneid, ncib-proteinid or uniprot. The first part shows how to generate the proper catdb Not adjusted for multiple testing. Upload your gene and/or compound data, specify species, pathways, ID type etc. In the "FS7 vs. FS0" comparison, 701 DEGs were annotated to 111 KEGG pathways. The KEGG database contains curated sets of genes that are known to interact in the same biological pathway. 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). Gene Data and/or Compound Data will also be taken as the input data Frequently, you also need to the extra options: Control/reference, Case/sample, Science is collaborative and learning is the same.The image at the bottom left of the thumbnail is modified from AllGenetics.EU. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. #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 . relationships among the GO terms for conditioning (Falcon and Gentleman 2007). Similar to above. 10.1093/bioinformatics/btt285. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. If you have suggestions or recommendations for a better way to perform something, feel free to let me know! whether functional annotation terms are over-represented in a query gene set. Which KEGG pathways are over-represented in the differentially expressed genes from the leukemia study? 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. Description: PANEV is an R package set for pathway-based network gene visualization. https://github.com/gencorefacility/r-notebooks/blob/master/ora.Rmd. kegg.gs and go.sets.hs. Please consider contributing to my Patreon where I may do merch and gather ideas for future content:https://www.patreon.com/AlexSoupir Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. package for a species selected under the org argument (e.g. endstream Numerous pathway analysis methods and data types are implemented in R/Bioconductor, yet there has not been a dedicated and established tool for pathway-based data integration and visualization. These functions perform over-representation analyses for Gene Ontology terms or KEGG pathways in one or more vectors of Entrez Gene IDs. continuous/discrete data, matrices/vectors, single/multiple samples etc. Figure 3: Enrichment plot for selected pathway. The following introduceds a GOCluster_Report convenience function from the optional numeric vector of the same length as universe giving a covariate against which prior.prob should be computed. Manage cookies/Do not sell my data we use in the preference centre. and numerous statistical methods and tools (generally applicable gene-set enrichment (GAGE) (), GSEA (), SPIA etc.) We can use the bitr function for this (included in clusterProfiler). The default for kegga with species="Dm" changed from convert=TRUE to convert=FALSE in limma 3.27.8. PANEV: an R package for a pathway-based network visualization, https://doi.org/10.1186/s12859-020-3371-7, https://cran.r-project.org/web/packages/visNetwork, https://cran.r-project.org/package=devtools, https://bioconductor.org/packages/release/bioc/html/KEGGREST.html, https://github.com/vpalombo/PANEV/tree/master/vignettes, https://doi.org/10.1371/journal.pcbi.1002375, https://doi.org/10.1016/j.tibtech.2005.05.011, https://doi.org/10.1093/bioinformatics/bti565, https://doi.org/10.1093/bioinformatics/btt285, https://doi.org/10.1016/j.csbj.2015.03.009, https://doi.org/10.1093/bioinformatics/bth456, https://doi.org/10.1371/journal.pcbi.1002820, https://doi.org/10.1038/s41540-018-0055-2, https://doi.org/10.1371/journal.pone.0032455, https://doi.org/10.1371/journal.pone.0033624, https://doi.org/10.1016/S0198-8859(02)00427-5, https://doi.org/10.1111/j.1365-2567.2005.02254.x, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/. How to perform KEGG pathway analysis in R? 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However, gage is tricky; note that by default, it makes a pairwise comparison between samples in the reference and treatment group. GENENAME GO GOALL MAP ONTOLOGY ONTOLOGYALL 161, doi: 10.1186/1471-2105-10-161, Pathway based data integration and visualization, Example Gene Data /Length 691 Examples of widely used statistical 5. MD Conception of biologically relevant functionality, project design, oversight and, manuscript review. species Same as organism above in gseKEGG, which we defined as kegg_organism gene.idtype The index number (first index is 1) correspoding to your keytype from this list gene.idtype.list, Next-Generation Sequencing Analysis Resources, NGS Sequencing Technology and File Formats, Gene Set Enrichment Analysis with ClusterProfiler, Over-Representation Analysis with ClusterProfiler, Salmon & kallisto: Rapid Transcript Quantification for RNA-Seq Data, Instructions to install R Modules on Dalma, Prerequisites, data summary and availability, Deeptools2 computeMatrix and plotHeatmap using BioSAILs, Exercise part4 Alternative approach in R to plot and visualize the data, Seurat part 3 Data normalization and PCA, Loading your own data in Seurat & Reanalyze a different dataset, JBrowse: Visualizing Data Quickly & Easily, https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html, https://github.com/gencorefacility/r-notebooks/blob/master/ora.Rmd, http://bioconductor.org/packages/release/BiocViews.html#___OrgDb, https://www.genome.jp/kegg/catalog/org_list.html. The row names of the data frame give the GO term IDs. stream H Backman, Tyler W, and Thomas Girke. The output from kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column. This will help the Pathview project in return. (2014) study and considering three levels for the investigation. This includes code to inspect how the annotations roy.granit 880. 1 Overview. p-value for over-representation of the GO term in the set. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. AnntationHub. Which KEGG pathways are over-represented in the differentially expressed genes from the leukemia study? Luo W, Pant G, Bhavnasi YK, Blanchard SG, Brouwer C. Pathview Web: user friendly pathway visualization and data integration. Here gene ID Here we are going to look at the GO and KEGG pathways calculated from the DESeq2 object we previously created. following uses the keegdb and reacdb lists created above as annotation systems. See 10.GeneSetTests for a description of other functions used for gene set testing. The results were biased towards significant Down p-values and against significant Up p-values. We have to use `pathview`, `gage`, and several data sets from `gageData`. statement and However, gage is tricky; note that by default, it makes a [] The following provide sample code for using GO.db as well as a organism Privacy Can be logical, or a numeric vector of covariate values, or the name of the column of de$genes containing the covariate values. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The GOstats package allows testing for both over and under representation of GO terms using Figure 2: Batch ORA result of GO slim terms using 3 test gene sets. This section introduces a small selection of functional annotation systems, largely PubMedGoogle Scholar. Correspondence to First, the package requires a vector or a matrix with, respectively, names or rownames that are ENTREZ IDs. By the way, if I want to visualise say the logFC from topTable, I can create a named numeric vector in one go: Another useful package is SPIA; SPIA only uses fold changes and predefined sets of differentially expressed genes, but it also takes the pathway topology into account. It is normal for this call to produce some messages / warnings. KEGG pathways. 3. https://doi.org/10.1073/pnas.0506580102. Either a vector of length nrow(de) or the name of the column of de$genes containing the Entrez Gene IDs. 2007. If trend=TRUE or a covariate is supplied, then a trend is fitted to the differential expression results and this is used to set prior.prob. Bioinformatics, 2013, 29(14):1830-1831, doi: Luo W, Friedman M, etc.