userListEnrichment(
geneR, labelR,
fnIn = NULL, catNmIn = fnIn,
nameOut = "enrichment.csv",
useBrainLists = FALSE, useBloodAtlases = FALSE, omitCategories = "grey",
outputCorrectedPvalues = TRUE, useStemCellLists = FALSE,
outputGenes = FALSE,
minGenesInCategory = 1,
useBrainRegionMarkers = FALSE, useImmunePathwayLists = FALSE,
usePalazzoloWang = FALSE)1) Text files (must end in ".txt") with one list per file, where the first line is the list descriptor and the remaining lines are gene names corresponding to that list, with one gene per line. For example Ribosome RPS4 RPS8 ...
2) Gene / category files (must be csv files), where the first line is the column headers corresponding to Genes and Lists, and the remaining lines correspond to the genes in each list, for any number of genes and lists. For example: Gene, Category RPS4, Ribosome RPS8, Ribosome ... NDUF1, Mitohcondria NDUF3, Mitochondria ... MAPT, AlzheimersDisease PSEN1, AlzheimersDisease PSEN2, AlzheimersDisease ...
3) Module membership (kME) table in csv format. Currently, the module assignment is the only thing that is used, so as long as the Gene column is 2nd and the Module column is 3rd, it doesn't matter what is in the other columns. For example,
PSID, Gene, Module,
If you have any suggestions for lists to add to this function, please e-mail Jeremy Miller at jeremyinla@gmail.com
------------------------------------- References for the pre-defined brain lists (useBrainLists=TRUE, in alphabetical order by category descriptor) are as follows:
ABA ==> Cell type markers from: Lein ES, et al. (2007) Genome-wide atlas of gene expression in the adult mouse brain. Nature 445:168-176.
ADvsCT_inCA1 ==> Lists of genes found to be increasing or decreasing with Alzheimer's disease in 3 studies: 1. Blalock => Blalock E, Geddes J, Chen K, Porter N, Markesbery W, Landfield P (2004) Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. PNAS 101:2173-2178. 2. Colangelo => Colangelo V, Schurr J, Ball M, Pelaez R, Bazan N, Lukiw W (2002) Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. J Neurosci Res 70:462-473. 3. Liang => Liang WS, et al (2008) Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiological genomics 33:240-56.
Bayes ==> Postsynaptic Density Proteins from: Bayes A, et al. (2011) Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nat Neurosci. 14(1):19-21.
Blalock_AD ==> Modules from a network using the data from: Blalock E, Geddes J, Chen K, Porter N, Markesbery W, Landfield P (2004) Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. PNAS 101:2173-2178.
CA1vsCA3 ==> Lists of genes enriched in CA1 and CA3 relative to other each and to other areas of the brain, from several studies: 1. Ginsberg => Ginsberg SD, Che S (2005) Expression profile analysis within the human hippocampus: comparison of CA1 and CA3 pyramidal neurons. J Comp Neurol 487:107-118. 2. Lein => Lein E, Zhao X, Gage F (2004) Defining a molecular atlas of the hippocampus using DNA microarrays and high-throughput in situ hybridization. J Neurosci 24:3879-3889. 3. Newrzella => Newrzella D, et al (2007) The functional genome of CA1 and CA3 neurons under native conditions and in response to ischemia. BMC Genomics 8:370. 4. Torres => Torres-Munoz JE, Van Waveren C, Keegan MG, Bookman RJ, Petito CK (2004) Gene expression profiles in microdissected neurons from human hippocampal subregions. Brain Res Mol Brain Res 127:105-114. 5. GorLorT => In either Ginsberg or Lein or Torres list.
Cahoy ==> Definite (10+ fold) and probable (1.5+ fold) enrichment from: Cahoy JD, et al. (2008) A transcriptome database for astrocytes, neurons, and oligodendrocytes: A new resource for understanding brain development and function. J Neurosci 28:264-278.
CTX ==> Modules from the CTX (cortex) network from: Oldham MC, et al. (2008) Functional organization of the transcriptome in human brain. Nat Neurosci 11:1271-1282.
DiseaseGenes ==> Probable (C or better rating as of 16 Mar 2011) and possible (all genes in database as of ~2008) genetics-based disease genes from: http://www.alzforum.org/
EarlyAD ==> Genes whose expression is related to cognitive markers of early Alzheimer's disease vs. non-demented controls with AD pathology, from: Parachikova, A., et al (2007) Inflammatory changes parallel the early stages of Alzheimer disease. Neurobiology of Aging 28:1821-1833.
HumanChimp ==> Modules showing region-specificity in both human and chimp from: Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 103: 17973-17978.
HumanMeta ==> Modules from the human network from: Miller J, Horvath S, Geschwind D (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci 107:12698-12703.
JAXdiseaseGene ==> Genes where mutations in mouse and/or human are known to cause any disease. WARNING: this list represents an oversimplification of data! This list was created from the Jackson Laboratory: Bult CJ, Eppig JT, Kadin JA, Richardson JE, Blake JA; Mouse Genome Database Group (2008) The Mouse Genome Database (MGD): Mouse biology and model systems. Nucleic Acids Res 36 (database issue):D724-D728.
Lu_Aging ==> Modules from a network using the data from: Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, Yankner B (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429:883-891.
MicroglialMarkers ==> Markers for microglia and macrophages from several studies: 1. GSE772 => Gan L, et al. (2004) Identification of cathepsin B as a mediator of neuronal death induced by Abeta-activated microglial cells using a functional genomics approach. J Biol Chem 279:5565-5572. 2. GSE1910 => Albright AV, Gonzalez-Scarano F (2004) Microarray analysis of activated mixed glial (microglia) and monocyte-derived macrophage gene expression. J Neuroimmunol 157:27-38. 3. AitGhezala => Ait-Ghezala G, Mathura VS, Laporte V, Quadros A, Paris D, Patel N, et al. Genomic regulation after CD40 stimulation in microglia: relevance to Alzheimer's disease. Brain Res Mol Brain Res 2005;140(1-2):73-85. 4. 3treatments_Thomas => Thomas, DM, Francescutti-Verbeem, DM, Kuhn, DM (2006) Gene expression profile of activated microglia under conditions associated with dopamine neuronal damage. The FASEB Journal 20:515-517.
MitochondrialType ==> Mitochondrial genes from the somatic vs. synaptic fraction of mouse cells from: Winden KD, et al. (2009) The organization of the transcriptional network in specific neuronal classes. Mol Syst Biol 5:291.
MO ==> Markers for many different things provided to my by Mike Oldham. These were originally from several sources: 1. 2+_26Mar08 => Genetics-based disease genes in two or more studies from http://www.alzforum.org/ (compiled by Mike Oldham). 2. Bachoo => Bachoo, R.M. et al. (2004) Molecular diversity of astrocytes with implications for neurological disorders. PNAS 101, 8384-8389. 3. Foster => Foster, LJ, de Hoog, CL, Zhang, Y, Zhang, Y, Xie, X, Mootha, VK, Mann, M. (2006) A Mammalian Organelle Map by Protein Correlation Profiling. Cell 125(1): 187-199. 4. Morciano => Morciano, M. et al. Immunoisolation of two synaptic vesicle pools from synaptosomes: a proteomics analysis. J. Neurochem. 95, 1732-1745 (2005). 5. Sugino => Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9, 99-107 (2006).
MouseMeta ==> Modules from the mouse network from: Miller J, Horvath S, Geschwind D (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci 107:12698-12703.
Sugino/Winden ==> Conservative list of genes in modules from the network from: Winden K, Oldham M, Mirnics K, Ebert P, Swan C, Levitt P, Rubenstein J, Horvath S, Geschwind D (2009). The organization of the transcriptional network in specific neuronal classes. Molecular systems biology 5. NOTE: Original data came from this neuronal-cell-type-selection experiment in mouse: Sugino K, Hempel C, Miller M, Hattox A, Shapiro P, Wu C, Huang J, Nelson S (2006). Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat Neurosci 9:99-107
Voineagu ==> Several Autism-related gene categories from: Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. (2011). Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474(7351):380-4
------------------------------------- References for the pre-defined blood atlases (useBloodAtlases=TRUE, in alphabetical order by category descriptor) are as follows:
Blood(composite) ==> Lists for blood cell types with this label are made from combining marker genes from the following three publications: 1. Abbas AB, Baldwin D, Ma Y, Ouyang W, Gurney A, et al. (2005). Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 6(4):319-31. 2. Grigoryev YA, Kurian SM, Avnur Z, Borie D, Deng J, et al. (2010). Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory T, monocytes and B cells. PLoS One. 5(10):e13358. 3. Watkins NA, Gusnanto A, de Bono B, De S, Miranda-Saavedra D, et al. (2009). A HaemAtlas: characterizing gene expression in differentiated human blood cells. Blood. 113(19):e1-9.
Gnatenko ==> Top 50 marker genes for platelets from: Gnatenko DV, et al. (2009) Transcript profiling of human platelets using microarray and serial analysis of gene expression (SAGE). Methods Mol Biol. 496:245-72.
Gnatenko2 ==> Platelet-specific genes on a custom microarray from: Gnatenko DV, et al. (2010) Class prediction models of thrombocytosis using genetic biomarkers. Blood. 115(1):7-14.
Kabanova ==> Red blood cell markers from: Kabanova S, et al. (2009) Gene expression analysis of human red blood cells. Int J Med Sci. 6(4):156-9.
Whitney ==> Genes corresponding to individual variation in blood from: Whitney AR, et al. (2003) Individuality and variation in gene expression patterns in human blood. PNAS. 100(4):1896-1901.
------------------------------------- References for the pre-defined stem cell (SC) lists (useStemCellLists=TRUE, in alphabetical order by category descriptor) are as follows:
Cui ==> genes differentiating erythrocyte precursors (CD36+ cells) from multipotent human primary hematopoietic stem cells/progenitor cells (CD133+ cells), from: Cui K, Zang C, Roh TY, Schones DE, Childs RW, Peng W, Zhao K. (2009). Chromatin signatures in multipotent human hematopoietic stem cells indicate the fate of bivalent genes during differentiation. Cell Stem Cell 4:80-93
Lee ==> gene lists related to Polycomb proteins in human embryonic SCs, from (a highly-cited paper!): Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, Chevalier B, Johnstone SE, Cole MF, Isono K, et al. (2006) Control of developmental regulators by polycomb in human embryonic stem cells. Cell 125:301-313
------------------------------------- References and more information for the pre-defined human brain region lists (useBrainRegionMarkers=TRUE):
HBA ==> Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. (2012) An Anatomically Comprehensive Atlas of the Adult Human Brain Transcriptome. Nature (in press)
Three categories of marker genes are presented:
1. globalMarker(top200) = top 200 global marker genes for 22 large brain structures. Genes are ranked based on fold change enrichment (expression in region vs. expression in rest of brain) and the ranks are averaged between brains 2001 and 2002 (human.brain-map.org).
2. localMarker(top200) = top 200 local marker genes for 90 large brain structures. Same as 1, except fold change is defined as expression in region vs. expression in larger region (format: -------------------------------------
More information for the pre-defined immune pathways lists (useImmunePathwayLists=TRUE): ImmunePathway ==> These lists were created by Brian Modena (a member of Daniel R Salomon's lab at Scripps Research Institute), with input from Sunil M Kurian and Dr. Salomon, using Ingenuity, WikiPathways and literature search to assemble them. They reflect knowledge-based immune pathways and were in part informed by Dr. Salomon and colleague's work in expression profiling of biopsies and peripheral blood but not in some highly organized process. These lists are not from any particular publication, but are culled to include only genes of reasonably high confidence. -------------------------------------
References for the pre-defined lists from CHDI (usePalazzoloWang=TRUE, in alphabetical order by category descriptor) are as follows: Biocyc NCBI Biosystems ==> Several gene sets from the "Biocyc" component of NCBI Biosystems: Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH (2010) The NCBI BioSystems database. Nucleic Acids Res. 38(Database issue):D492-6. Kegg NCBI Biosystems ==> Several gene sets from the "Kegg" component of NCBI Biosystems: Geer LY et al 2010 (full citation above). Palazzolo and Wang ==> These gene sets were compiled from a variety of sources by Mike Palazzolo and Jim Wang at CHDI. Pathway Interaction Database NCBI Biosystems ==> Several gene sets from the "Pathway Interaction Database" component of NCBI Biosystems: Geer LY et al 2010 (full citation above). PMID 17500595 Kaltenbach 2007 ==> Several gene sets from: Kaltenbach LS, Romero E, Becklin RR, Chettier R, Bell R, Phansalkar A, et al. (2007) Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet. 3(5):e82 PMID 22348130 Schaefer 2012 ==> Several gene sets from: Schaefer MH, Fontaine JF, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA (2012) HIPPIE: Integrating protein interaction networks with experiment based quality scores. PLoS One. 7(2):e31826 PMID 22556411 Culver 2012 ==> Several gene sets from: Culver BP, Savas JN, Park SK, Choi JH, Zheng S, Zeitlin SO, Yates JR 3rd, Tanese N. (2012) Proteomic analysis of wild-type and mutant huntingtin-associated proteins in mouse brains identifies unique interactions and involvement in protein synthesis. J Biol Chem. 287(26):21599-614 PMID 22578497 Cajigas 2012 ==> Several gene sets from: Cajigas IJ, Tushev G, Will TJ, tom Dieck S, Fuerst N, Schuman EM. (2012) The local transcriptome in the synaptic neuropil revealed by deep sequencing and high-resolution imaging. Neuron. 74(3):453-66 Reactome NCBI Biosystems ==> Several gene sets from the "Reactome" component of NCBI Biosystems: Geer LY et al 2010 (full citation above). Wiki Pathways NCBI Biosystems ==> Several gene sets from the "Wiki Pathways" component of NCBI Biosystems: Geer LY et al 2010 (full citation above). Yang ==> These gene sets were compiled from a variety of sources by Mike Palazzolo and Jim Wang at CHDI.
# Example: first, read in some gene names and split them into categories
data(BrainLists);
listGenes = unique(as.character(BrainLists[,1]))
set.seed(100)
geneR = sort(sample(listGenes,2000))
categories = sort(rep(standardColors(10),200))
categories[sample(1:2000,200)] = "grey"
write(c("TESTLIST1",geneR[300:400], sep=""),"TESTLIST1.txt")
write(c("TESTLIST2",geneR[800:1000],sep=""),"TESTLIST2.txt")
# Now run the function!
testResults = userListEnrichment(geneR, labelR=categories,
fnIn=c("TESTLIST1.txt","TESTLIST2.txt"),
catNmIn=c("TEST1","TEST2"),
nameOut = "testEnrichment.csv",useBrainLists=TRUE, omitCategories ="grey")
# To see a list of all significant enrichments, either open
# the file "testEnrichments.csv" in the current directory, or type:
testResults$sigOverlaps
# To see all of the overlapping genes between two categories
#(whether or not the p-value is significant), type
#restResults$ovGenes$'<labelR> -- <comparisonCategory>'. For example:
testResults$ovGenes$"black -- TESTLIST1__TEST1"
testResults$ovGenes$"red -- salmon_M12_Ribosome__HumanMeta"
# More detailed overlap information is in the pValue output. For example:
head(testResults$pValue)Run the code above in your browser using DataLab