Stringgaussnet: user's guide
Contents
1. H H H H H H H H H node1 NUDT3 NUDT3 P2RX1 aOonr Wer SGMS2 node2 Interaction P2RX1 SIMoNeInference F1i3A1 SIMoNeInference F13A1 SIMoNeInference NUDT3 FAM204A SIMoNeInference LRRC4 SIMoNeInference DEGenes preview GeneSymbol Ensemblid NUDT3 P2RX1 SGMS2 WDR25 F13A1 NUDT3 ENSGO0000112664 P2RX1 ENSGO0000108405 SGMS2 ENSG00000164023 WDR25 ENSGO0000176473 F1i3A1 ENSGO0000124491 Theta Rho P Value 0 38204142 0 9008297 0 08337518 0 7824086 0 04243323 0 7665932 0 14221514 0 7543550 0 44771356 0 8002982 P Value Fold Change 4 60e 06 8 45e 06 4 13e 05 4 45e 05 6 12e 05 0 60 0 52 0 36 0 56 2 63 We have unique edges with Theta scores and spearman s plot GlobalSIMoNeNet Common between global and cluster 33 Here we have the network displayed with the plot function provided with the POU5F1B simone package WGCNA network inference and comparison with SIMoNe SIMoNe is a powerful statistical approach to infer non supervised gaussian networks However the algorithm principle is not intuitive for any beginner in the graph theory domain and statistical inference This is why we propose to compare with a more trivial approach WGCNA Stringgaussnet allows to compute spearman s test between all pairs of genes and respective rhos are converted to similarity scores with TNFSF13B 13 oOOCoO0C oO logFC 0 7369656 0 9434165 1 4739312 0 8365013 1 3950622. chipnum status LPStime subject 1 21 Patient HO I18 2 22 Patient HO I17 3 23 Patient HO I4 4 24 Patient HO I1 5 25 Patient HO I2 We can see here what sample descriptions look like LPStime is the LPS stimulation duration for MD DCs SpAData lt DEGeneExpr t SpADataExpression SpADEGenes print SpAData 5 Object of class DEGeneExpr package stringgaussnet Number of samples 57 Number of genes 75 DataExpression preview NUDT3 P2RX1 SGMS2 WDR25 F1i3A1 FAM204A LRRC4 21 10 25609 7 779726 7 478363 7 395941 13 53042 8 865439 6 196102 22 10 17532 7 713649 7 426126 7 482414 13 39109 8 743199 6 715109 23 10 09892 7 736853 7 514560 7 598745 13 37703 8 716703 7 489576 24 10 24614 7 995944 7 962326 7 658044 13 65390 8 732653 6 611568 25 10 02167 7 884883 6 473152 7 645395 13 67628 8 581200 6 637558 EIF4H SAP130 SLU7 POUS5F1B POLR1ID TTC39C ADAMTS15 21 10 355450 10 17557 8 517671 6 425804 9 553580 7 429002 7 779027 22 10 104375 10 18127 7 944881 6 761638 9 350876 7 147367 8 855387 23 11 089198 10 28844 7 796919 6 634245 9 377427 7 614758 9 434300 24 10 188845 10 28227 8 153847 6 697428 9 390345 7 488865 7 944880 25 9 787701 10 14357 7 824845 6 638128 9 036362 7 173620 8 397508 HH TNFSF13B TSPYL5 CSF3R FAU TFAM FAIM2 CITED2 21 10 17795 8 561254 7 383586 10 299998 6 797904 6 753533 11 23097 22 10 84027 8 214381 7 135964 10 300351 6 786641 10 433228 11 00969
3. 23 10 90312 8 316282 7 008943 10 109672 6 656924 9 121431 10 86765 24 10 87046 8 395607 7 803192 9 546860 6 669851 10 279958 11 39621 25 10 64746 8 070187 7 843946 9 631554 6 621111 9 579760 11 83044 SIGLEC15 MBIP HAUS1 RFC3 TBCK TRIM24 ANAPC15 PIGB 21 7 730661 8 265060 9 547622 6 717628 9 507269 9 164891 10 42642 8 663999 22 8 486743 8 373733 9 286334 6 572705 9 561226 9 012223 10 77170 8 803228 23 8 774401 8 302195 9 210819 6 721516 9 318380 9 086190 10 71807 9 102257 24 8 724044 8 059957 8 926340 6 859887 9 486730 9 269940 10 73594 8 996247 25 8 460145 7 942978 8 907234 6 503483 9 719901 8 715967 10 48000 8 208808 DNAJA4 RBBP9 KIAA0O907 MUT FBX018 USP30 NDP OLR1 21 8 272130 8 143958 9 352399 8 188985 8 265647 8 903849 7 663387 6 220560 22 8 370383 7 665083 9 118642 8 275170 8 363612 8 475835 7 667593 6 641498 23 8 160428 7 922728 8 891830 8 369798 8 099168 8 736526 6 961104 7 722695 24 8 407753 8 101788 9 011711 8 337796 8 353551 8 483161 6 080360 5 939017 25 8 047116 7 251024 9 234585 7 995534 7 991599 8 523176 5 715268 6 317065 HH PLP2 MNDA IFT52 COX20 BAK1P1 CRTAP RPS15 ALG10B 21 11 36747 8 173222 7 287404 7 968966 6 412227 11 28916 10 43043 7 672037 22 11 09409 9 675167 6 828304 8 073868 6 570384 11 00393 10 43679 6 686629 23 11 20175 8 463822 6 903684 8 310869 6 447920 10 97360 10 63966 7 217247 24 11 78391 9 009064 7 119040 8 297897 6 767743 11 39861 10 35509 7 628294 25 10 61192 8 8243
4. AddAnnotations TRUE SIMoNeOptions list AddAnnotations TRUE WGCNAOptions list AddAnnotattions TRUE This is how you should do if you wanted to add gene annotations for all network generations eH HR RH HR HR Network exportation into files and Cytoscape An important feature of stringgaussnet is to allow to visualize and manipulate all generated networks only in few steps without any knowledge in network files manipulations and package object structures Firstly a generic export function is available to save all generated networks in standard edge and node attributes format Let s see here an example for SIMoNeNet library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe GlobalSIMoNeNet lt getSIMoNeNet GaussianSpAData AddAnnotations TRUE GlobalSIMoNeNet lt FilterEdges GlobalSIMoNeNet 0 4 export GlobalSIMoNeNet YourDirPath Replace YourDirPath by the directory where will be saved edge and node attributes If the directory exists this will not be overwritten excepted if you use the parameter overwrite IRUE But the most interesting part is to be able to import automatically all generated networks from R objects to Cytoscape without any requirement of secondary language Cytoscape is
5. a powerful software to compute biological networks Automatic importation through stringgaussnet uses the plugin cyREST which works like a local API Thus for this feature Cytoscape must be running and this plugin installed At least java version 8 must be installed on the computer which is the case for the most windows running computers Moreover this importation is not operating system dependent such as the R programming language For more information about software requirements please see Hardware and software requirements Cytoscape uses styles to display imported networks Our package generates predefined styles which can be then easily modified in Cytoscape But you can choose to handle custom styles available in the running Cytoscape session Node sizes and colors are dependent by default on fold change and p value from DE genes analysis but you can choose what attributes can be at the basis of those properties Edge views depend on attributes from each kind of generated network e g spearman s rho for gaussian networks Indirect interactions from ShortPathSTRINGNet are displayed with dashed blue lines The layout can also be set which is by default force directed Let s see an example for MultiNetworks 25 library stringgaussnet data SpADataExpression data SpADEGenes data SpASamples SpAData lt DEGeneExpr t SpADataExpression SpADEGenes StatusFactor lt SpASamples status names StatusFactor lt SpASamples
6. chipnum NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe MultiSpAData lt MultiDEGeneExpr GaussianSpAData DEGeneExpr t SpADataExpression 18 34 SpADEGenes 18 34 DEGeneExpr t SpADataExpression 35 51 SpADEGenes 35 51 MultiSpANetworks lt MultiNetworks MultiSpAData SelectInteractionsSTRING c coexpression experimental knowledge STRINGThreshold 0 9 FilterSIMoNeOptions list Threshold 0 4 Factors StatusFactor STRINGOptions list AddAnnotations TRUE SIMoNeOptions list AddAnnotations TRUE WGCNAOptions list AddAnnotations TRUE resetCytoscapeSession We reset and create an empty session in Cytoscape Please be sure that Cytoscape is running with the cyREST plugin installed addMultiGraphToCytoscape MultiSpANetworks points size map P Value points fill map logFC We add automatically all generated networks in the Cytoscape session with predefined styles saveCytoscapeSession YourFilePath We can save the current Cytoscape session in a cys file Replace YourFilePath with the path where you would like to save The cys extension is automatically added if necessary References Chiquet Julien Alexander Smith Gilles Grasseau Catherine Matias and Christophe Ambroise 2009 SIMoNe Statistical Inference for MOdular NEtworks Bioinformatics Oxford England
7. lowest important information because the most part of genes go through this component to arrive in others To use this feature you can use the parameter AddAnnotations TRUE for each network creation function Let s see an example for SIMoNeNet library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe GlobalSIMoNeNet lt getSIMoNeNet GaussianSpAData AddAnnotations TRUE Here we use the parameter AddAnnotations IRUE to add annotations to genes from the network print GlobalSIMoNeNet 5 Object of class SIMoNeNet package stringgaussnet Number of nodes 17 Number of interactions 36 Edges preview nodel node2 Interaction Theta Rho P Value 1 NUDT3 P2RX1 SIMoNeInference 0 38204142 0 9008297 0 00000000 2 NUDT3 WDR25 SIMoNeInference 0 04485639 0 3279103 0 01311308 3 NUDT3 F13A1 SIMoNeInference 0 08337518 0 7824086 0 00000000 4 P2RX1 F13A1 SIMoNeInference 0 04243323 0 7665932 0 00000000 5 NUDT3 FAM204A SIMoNeInference 0 14221514 0 7543550 0 00000000 HH DEGenes preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 SGMS2 SGMS2 ENS
8. results can be obtained for example by LIMMA This variable corresponds to a data frame with genes as rows and statistics as columns The minimum suggested columns are fold changes and p values for the visualization in Cytoscape but you are free to set other values as properties for node color and size Genes in expression data must be exactly the same as those in DE genes statistics Those data frames must be firstly combined in an object of class DEGeneExpr which is then a list with its own print function giving the number of samples and genes and a preview of the both data frames In the example data we provided transcriptomic profiles of monocyte derived dendritic cells MD DCs from 9 patients affected with ankylosing spondylitis and 10 healthy controls Transcriptomic data were obtained using microarrays Gene expression levels in patients and in controls were then compared with LIMMA Talpin et al 2014 We limited the number of DE genes to 75 Let s see how it looks like in the example data inside the package library stringgaussnet data SpADataExpression Import example expression data data SpADEGenes Import example DE genes analysis results data SpASamples Import example sample description We firstly import all example data from stringgaussnet package SpASamples is not compulsory for using stringgaussnet but is useful for creating a factor for subsetting gaussian networks generation head SpASamples 5
9. to infer gene networks starting from a list of DE genes by integrating both of these approaches with ease and flexibility The main objective of this tool is to be much flexible in function of your needs and to proceed automatically all necessary steps Semantic networks are constructed by extracting all wished interaction types and possible additional nodes by requesting the STRING Application Programming Interface API This tool can also reduce the network by calculating shortest paths between a given number of targeted genes Gaussian networks are inferred by SIMoNe with a possible ad hoc filtering of edges on spearman s rho coefficients This tool can also use a WGCNA like approach a simple correlation calculation with a soft thresholding to compare results with SIMoNe This package integrates R commands allowing to export automatically all created networks in Cytoscape through the cyREST plugin Cline et al 2007 Expression data DE gene statistics getWGCNANet SEENE EN E TTE EA A iA API getSTRINGNet DEGeneExpr getSIMoNeNet string db MultiDEGeneExpr SIMoNe WGCNA STRINGNet getShortestPaths compareGaussNetworks ShortPathSTRINGNet WGCNANet MultiNetworks cyREST add Multi GraphToCytoscape Cytoscape Figure 1 Stringgaussnet operating principle Starting from expression data and DE gene statistics it is possible to create all kinds of semantic and gaussian networks and then to export g
10. 07 6 888093 8 136609 7 137114 11 16194 10 16442 6 972385 ENY2 PIAS2 FBXL4 HSPH1 PTPLA COX7B EDEM3 21 9 043001 9 677796 8 894753 10 96024 7 228479 7 967758 9 340578 22 8 295627 9 842157 8 790365 11 17939 7 226040 7 412543 9 267813 23 8 808935 9 400758 8 702894 11 01773 6 944539 8 053695 8 921224 24 8 651800 9 368522 8 476081 11 29588 6 667556 7 071694 9 336514 25 8 298026 9 675684 8 861115 11 21505 7 316875 7 129328 9 400450 HH CTBP1 AS1 HSPA1A SELL P4HA1 CKAP2 ELMO1 GTSF1 21 8 373863 10 60785 5 866700 9 232794 9 014485 10 88822 5 578275 22 8 126615 10 85421 5 726393 9 200150 8 656653 10 68401 5 890657 23 8 484404 10 42632 6 278107 9 226551 8 244108 10 68071 5 845840 24 8 157645 10 87067 5 989361 9 794671 8 899038 10 76450 5 937787 25 8 084694 10 64348 6 251385 9 481582 8 180849 10 71228 4 922962 HH RPS4X FHL3 SEMA3C RPLiOA SPATA20 ITPRIP ZNF804A 21 10 20354 9 475687 9 297923 9 780683 8 969004 8 030501 8 230648 22 10 22314 9 537080 9 813916 9 642061 9 218515 8 032228 7 943240 23 10 44483 9 741039 9 820876 10 185917 9 321994 8 382751 8 378532 24 10 22493 9 920751 9 454757 9 691150 9 098727 8 347813 7 909494 25 10 72840 8 996963 9 572225 9 261363 8 838107 7 775624 7 602159 HH ANKRD36BP 1 BACE2 PORCN USP40 RND3 ACADM GPR180 21 7 906530 7 728812 8 595057 7 496214 5 611142 9 417765 7 893242 7 22 7 161434 7 261641 8 539727 7 473354 6 057174 9 251663 8 089585 23 6 989984 7 505255 8 345492 7 083213
11. 25 3 417 18 doi 10 1093 bioinformatics btn637 Cline Melissa S Michael Smoot Ethan Cerami Allan Kuchinsky Nerius Landys Chris Workman Rowan Christmas et al 2007 Integration of Biological Networks and Gene Expression Data Using Cytoscape Nature Protocols 2 10 2366 82 doi 10 1038 nprot 2007 324 Cotney Justin Rebecca A Muhle Stephan J Sanders Li Liu A Jeremy Willsey Wei Niu Wenzhong Liu et al 2015 The Autism Associated Chromatin Modifier CHD8 Regulates Other Autism Risk Genes During Human Neurodevelopment Nature Communications 6 March doi 10 1038 ncomms7404 bi Dong Jun and Steve Horvath 2007 Understanding Network Concepts in Modules BMC Systems Biology 1 24 doi 10 1186 1752 0509 1 24 Lin Ying Vusumuzi Leroy Sibanda Hong Mei Zhang Hui Hu Hui Liu and An Yuan Guo 2015 MiRNA 26 2 and TF Co Regulatory Network Analysis for the Pathology and Recurrence of Myocardial Infarction Scientific Reports 5 April doi 10 1038 srep09653 Smyth Gordon K 2004 Linear Models and Empirical Bayes Methods for Assessing Differential Expres sion in Microarray Experiments Statistical Applications in Genetics and Molecular Biology 3 Article3 doi 10 2202 1544 6115 1027 Talpin Alice F licie Costantino Nelly Bonilla Ariane Leboime Franck Letourneur S bastien Jacques Florent Dumont et al 2014 Monocyte Derived Dendritic Cells from HLA B27 Axial Spondyloarthritis S
12. 5 914650 9 560359 7 692392 24 7 254221 7 185580 8 189664 7 286869 5 472070 9 639629 8 128288 25 6 451797 7 973019 8 424378 7 440147 5 745261 8 978150 7 554904 H TRBC2 PARVG 21 7 062023 9 102626 22 7 054005 9 251641 23 8 127213 9 117042 24 6 748430 9 739595 25 7 617592 9 866001 H DEGenesResults preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 SGMS2 SGMS2 ENSGO00000164023 4 13e 05 0 36 1 4739312 WDR25 WDR25 ENSGO0000176473 4 45e 05 0 56 0 8365013 F13A1 F13A1 ENSGO0000124491 6 12e 05 2 63 1 3950628 Here we see that we have 57 samples and 75 DE genes There is a preview of the expression data Row names correspond to the column chipnum in SpAsamples For each gene in row we have attributes that will be added further as node attributes in our networks Notably we have here the both gene identifiers HGNC symbols and Ensembl IDs and p values and fold changes computed by LIMMA Semantic network creation with STRING Stringgaussnet allows to construct a protein protein interaction PPI network using the DE gene names Then all known PPIs between those genes are explored To this aim the package uses the STRING API which is a query application that works through the construction of a specific URL which is also called an uniform resource identifier URI in this case The ente
13. 68 6 570384 CRTAP RPS15 ALG10B ENY2 PIAS2 FBXL4 HSPH1 PTPLA 21 11 28916 10 43043 7 672037 9 043001 9 677796 8 894753 10 96024 7 228479 22 11 00393 10 43679 6 686629 8 295627 9 842157 8 790365 11 17939 7 226040 COX7B 21 7 967758 22 7 412543 DEGenesResults preview GeneSymbol EnsemblId P Value Fold Change logFC USP30 USP30 ENSGO0000135093 0 00320 0 60 0 7369656 NDP NDP ENSGO0000124479 0 00321 0 13 2 9434165 We have by the specific print function a preview of each DEGeneExpr object in the list 1tiSpANetworks lt MultiNetworks MultiSpAData SelectInteractionsSTRING c coexpression experimental knowledge STRINGThreshold 0 9 FilterSIMoNeOptions list Threshold 0 4 Factors StatusFactor We create an object of class MultiNetworks which allows to generate all kinds of networks in multiple lists of DEGeneExpr objects in one line int MultiSpANetworks Object of class MultiNetworks package stringgaussnet 3 object s of class DEGeneExpr used List1 List2 List3 3 method s of network creation used STRING SIMoNe WGCNA A factor with 2 levels has been entered by the user Control Patient We simply have a summary of the used method to generate the MultiNetworks object 24 MultiSpANetworks lt Mul tiNetworks Mul tiSpAData SelectInteractionsSTRING c coezpression experimental knowledge STRINGThreshold 0 9 FilterSIMoNeOptions list Threshold 0 4 Factors StatusFactor STRINGOptions list
14. 69656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 EIF4H 35545 10438 TSPYL5 561254 214381 We can see that we have a preview of inferred networks for each level par mfrow c 2 1 plot StatusFactorSIMoNeNet interactiveMode F 21 Control porxi TNFSF13B WDR25 aie LRRC4 EIFABamTsi5 POLR1D Patient EIF4H ADAMTS15 SPYL5 SAP130 R1D POU5F1B TNFSF13B NUDT3 F13A1 FAM204A Here we can display networks inferred in patients and controls specifically like in the Figure 5 with the provided function in the simone package compareFactorNetworks StatusFactorSIMoNeNet Here we can have a series of plots to compare results of inferred networks for each level You can see help compareFactorNetworks for more details StatusFactorSIMoNeNet lt FactorNetworks GaussianSpAData StatusFactor SIMoNe List AddAnnotations TRUE This is how you should do if you wanted to add gene annotations Multiple networks generation from a list of differential analysis results One can create an object of class MultiDEGeneExpr a list of DEGeneExpr objects Then both kinds of networks semantic or Gaussian can be generated for each data set and stored in a MultiNetworks object This wrapper is useful to explore all possible interactions between a set of DE genes lists All options for each network generation are accessible through an unique function Let s have a look in an example library stringgaus
15. 79735 0 5840833 0 9 0 41475 Median score 0 7390000 0 2379735 0 4700000 0 9 0 46200 textmining Count 15 0000000 Min score 0 1791130 Max score 0 7420000 Mean score 0 3596075 Median score 0 3150000 Interactions with added nodes H coexpression cooccurence experimental fusion knowledge Count 4522 0000000 114 0000000 4094 0000000 1 000 3271 0000000 Min score 0 0640000 0 0057970 0 0430000 0 485 0 3600000 Max score 0 9750000 0 5250000 0 9990000 0 485 0 9000000 Mean score 0 7717379 0 2544413 0 7985677 0 485 0 8955793 Median score 0 9360000 0 2405000 0 9300000 0 485 0 9000000 neighborhood textmining Count 1120 0000000 4778 0000000 Min score 0 0650000 0 0023760 Max score 0 6080000 0 9990000 Mean score 0 3567384 0 4198716 Median score 0 4620000 0 4010000 Here we have score summaries for each interaction source and by making a distinction between initial and added nodes PPISpASTRINGNet lt selectInteractionTypes SpASTRINGNet c coexpression experimental knowledge 0 9 Here we select only interactions of kind coexpression experimental and knowledge with a score filtering threshold of 0 9 print PPISpASTRINGNet 5 Object of class STRINGNet package stringgaussnet Total number of nodes 197 Number of initial nodes 25 Number of added nodes 172 Total number of interactions 4027 Number of interact
16. 8 rhos and p values a 1 rho 2 in order to keep the correlation signs Then those scores are converted to proximity scores A with A 1 1 exp a o 0 5 a is the soft power threshold and is by default 8 in the package A function is provided to help in choosing this parameter by giving a series of plots representing relations between A and rho Figure 3 Then a filtering step is proceeded with a threshold t A being superior to t or inferior to 1 t By default t is 0 85 as suggested by the WGCNA tutorial Dissimilarities and modules computations are not implemented because the main purpose is to compare with SIMoNe and to empower its use Proximity calculation parameters i Alpha values e 6 e 12 7 14 8 e 16 ss 9 18 So 10 20 So gt lt a oa pad N Q 0 0 0 2 0 4 0 6 0 8 1 0 Similarity Figure 3 A plot to help in choosing the soft thresholding parameter Different alpha values are used to visualize different possibilities The obtained network is saved in a WGCNANet class object with print and summary functions much similar to SIMoNeNet This is also possible to draw the inferred network in the same way as for SIMoNeNet A function is provided to compare inferred networks from both SIMoNe and WGCNA with venn diagram displaying respective connectivities and a series of plots showing correlations of picked interactions Figure 4 Please see help compareGaussNetworks for mor
17. 9 Mean score 0 9589772 0 975184 0 9 Median score 0 9670000 0 989000 0 9 Object of class STRINGNet package stringgaussnet Total number of nodes 235 Number of initial nodes 53 Number of added nodes 182 Total number of interactions 5099 Number of interactions between initial nodes 17 Number of interactions with added nodes 5082 Edges preview node1 node2 Interaction Score coexpression RPLP1 RPL36 coexpression 0 931 experimental RPLP1 RPL36 experimental 0 929 knowledge RPLP1 RPL36 knowledge 0 900 textmining RPLP1 RPL36 textmining 0 233 combined_score RPLP1i RPL36 combined_score 0 999 DEGenes preview GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 SGMS2 SGMS2 ENSGO0000164023 4 13e 05 0 36 1 4739312 F13A1 F13A1 ENSGO00000124491 6 12e 05 2 63 1 3950628 LRRC4 LRRC4 ENSGO0000128594 8 03e 05 0 42 1 2515388 EIF4H EIF4H ENSGO0000106682 1 11e 04 0 34 1 5563933 Short paths from STRINGNet The generated network can be large and dense As a STRINGNet object it can be reduced by computing shortest paths between genes of a user s list Figure 2 To this aim combined scores S are converted to distances D for each node pair i with Di max Si 1 Si where max Si is the maximum of S over all interactions The distance represents a value comprised between 1 and 2 and higher is the score lower is th
18. GO0000164023 4 13e 05 0 36 1 4739312 WDR25 WDR25 ENSGO0000176473 4 45e 05 0 56 0 8365013 F13A1 F13A1 ENSGO0000124491 6 12e 05 2 63 1 3950628 Annotations preview ensembl_gene_id localization hgnc_symbol chromosome_name band ADAMTS15 ENSGO0000166106 extracellular ADAMTS15 11 q24 3 CSF3R ENSG00000119535 extracellular CSF3R 1 p34 3 EIF4H ENSG00000106682 cytoplasm EIF4H 7 q11 23 17 H H H H H H H H H H H H H F13A1 ENSG00000124491 extracellular F13A1 6 p25 1 FAM204A ENSGOO000165669 lt NA gt FAM204A 10 q26 11 strand start_position end_position ADAMTS15 1 130448974 130476641 CSF3R 1 36466043 36483278 EIF4H 1 74174245 74197101 F13A1 1 6144085 6321013 FAM204A 1 118297930 118342328 descript ADAMTS15 ADAM metallopeptidase with thrombospondin type 1 motif 15 Source HGNC Symbol Acc HGNC 163 CSF3R colony stimulating factor 3 receptor granulocyte Source HGNC Symbol Acc HGNC 24 EIF4H eukaryotic translation initiation factor 4H Source HGNC Symbol Acc HGNC 127 F13A1 coagulation factor XIII A1 polypeptide Source HGNC Symbol Acc HGNC 35 FAM204A family with sequence similarity 204 member A Source HGNC Symbol Acc HGNC 257 We can see that we have gene annotations added by biomaRt and gene product localizations provided by Gene Ontology Multiple gaussian networks inference as a function of a factor An overlay of functions allows you to create multiple networks in only one st
19. R25 SIMoNeInference 0 8923138 0 5286492 3 143367e 05 5 NUDT3 F13A1 SIMoNeInference 0 9580987 0 7824086 0 000000e 00 DEGenes preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 SGMS2 SGMS2 ENSGO0000164023 4 13e 05 0 36 1 4739312 WDR25 WDR25 ENSGO0000176473 4 45e 05 0 56 0 8365013 F13A1 F1i3A1 ENSGO0000124491 6 12e 05 2 63 1 3950628 aOPWNR We have adjacency scores and spearman s rhos and p values for each edge plot GlobalWGCNANet Network inferred by WGCNA alpha 8 threshold 0 85 ADAMTS15 SAP130 POU5F1B Here we have the network displayed with the plot function provided in the simone package 16 Adding annotations to genes For each of those different described kinds of networks this is possible to add gene annotations as node attributes This option is usable at the same step as for network generation This enrichment adds two kinds of informations First stringgaussnet uses the R package biomaRt to get mainly genomic localization and gene description Secondly it adds cellular component terms with the package GO db Because several components can be linked to one gene a prioritization is performed to rank genes products localizations from nuclear the most important and then extracellular plasma membrane and cytoplasm Indeed we suppose that cytoplasmic localization is the
20. Stringgaussnet user s guide Emmanuel Chaplais 2015 07 22 Contents Introduction 1 Hardware and software requirements 2 Starting with differential analysis results 3 Semantic network creation with STRING 6 Short paths from STRINGNet 9 SIMoNe network inference 11 WGCNA network inference and comparison with SIMoNe 13 Adding annotations to genes 17 Multiple gaussian networks inference as a function of a factor 18 Multiple networks generation from a list of differential analysis results 22 Network exportation into files and Cytoscape 25 References 26 Introduction Analysis of genes differentially expressed DE depending on a condition has become a standard procedure in current biology However identification of biologically relevant DE genes is far from being trivial Yet efficient prioritization of DE genes is an essential step before undertaking rate limiting wet lab experiments Smyth 2004 In this regard the network theory appears as a powerful framework The aim is to connect genes the nodes by means of their interactions the edges Dong and Horvath 2007 These interactions may be based on prior knowledge found in databases or extracted from the experimental dataset e g using coexpression information Xue et al 2014 Verfaillie et al 2015 Lin et al 2015 Cotney et al 2015 Sophisticated but distinct tools are available to implement either one of these approaches separately We introduce stringgaussnet an R package that allows
21. e distance The shortest paths between each pair of given nodes are computed with the Dijkstra s algorithm provided in the R package igraph This method creates an object of class ShortPathSTRINGNet with unique edges giving distances and intermediates as attributes The print and summary functions are quite similar to STRINGNet but they focus more on the distance attributes than on the scores It is then possible to filter on the distance if you wish to look only for closest interactions Figure 2 From STRINGnet A to ShortPathSTRINGNet B object We can see that we considerably reduce displayed information noise for large networks Dashed blue lines in B represent indirect interactions Let s take a look in the example library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes SpASTRINGNet lt getSTRINGNet SpAData PPISpASTRINGNet lt selectInteractionTypes SpASTRINGNet c coexpression experimental knowledge 0 9 shortPathSpANet lt getShortestPaths PPISpASTRINGNet Here we get the short paths STRING network with default parameters You can type help getShortestPaths for more details shortPathSpANet lt FilterEdges shortPathSpANet 5 Here we can filter on the distance between two nodes print shortPathSpANet 5 Object of class ShortPathSTRINGNet package stringgaussnet Total number of nodes 18 Number of
22. e informations 14 Spearman P Values Rhos SIMoNe n WGCNA Common oO Ea o s nm 4 ae t oo 0 5 0 6 0 7 0 8 0 9 abs Rho Figure 4 Comparison plot of spearman s rho and p value between SIMoNe WGCNA infered networks We can notice that SIMoNe removes a lot of interactions thanks to the partial correlation computation Let s have a look in the example library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe pickWGCNAParam GaussianSpAData Here we use a list of plots to help in choosing the right parameter for WGCNA computing You can see help pickWGCNAParam for more details Global WGCNANet lt getWGCNANet GaussianSpAData Here we get the WGCNA network with default parameters You can type help getWGCNANet for more details print GlobalWGCNANet 5 15 Object of class WGCNANet package stringgaussnet Number of nodes 17 Number of interactions 41 Edges preview nodel node2 Interaction Adjacency Rho P Value 1 NUDT3 P2RX1 SIMoNeInference 0 9734888 0 9008297 0 000000e 00 2 NUDT3 SGMS2 SIMoNeInference 0 8836258 0 5068058 7 250921e 05 3 P2RX1 SGMS2 SIMoNeInference 0 8724723 0 4807493 1 840595e 04 4 SGMS2 WD
23. e lack of information concerning their precise algorithm Let s see an example in our package library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes SpASTRINGNet lt getSTRINGNet SpAData Here we get the STRING network with default parameters You can type help getSTRINGNet for more details print SpASTRINGNet 5 Object of class STRINGNet package stringgaussnet Total number of nodes 235 Number of initial nodes 53 Number of added nodes 182 Total number of interactions 5099 Number of interactions between initial nodes 17 Number of interactions with added nodes 5082 Edges preview node1 node2 Interaction Score coexpression RPLP1 RPL36 coexpression 0 931 experimental RPLP1 RPL36 experimental 0 929 knowledge RPLP1 RPL36 knowledge 0 900 textmining RPLP1 RPL36 textmining 0 233 combined_score RPLP1 RPL36 combined_score 0 999 DEGenes preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDTS3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 SGMS2 SGMS2 ENSGO0000164023 4 13e 05 0 36 1 4739312 F13A1 F13A1 ENSG00000124491 6 12e 05 2 63 1 3950628 LRRC4 LRRC4 ENSGO0000128594 8 03e 05 0 42 1 2515388 EIF4H EIF4H ENSGO0000106682 1 11e 04 0 34 1 5563933 We can see that STRING gave a network with 53 from the 75 initial genes entered in the API 183 additi
24. ep with all options con figurable in the same method One can create multiple Gaussian networks from the same DEGene Expr object depending on a grouping factor and for a given list of genes The package then allows to compare networks inferred for multiple levels of the factor and for the same DE genes list Figure 5 18 Figure 5 Example of infered SIMoNe networks between two groups A and B of a given factor disease status in our example Stringgaussnet allows to generate automatically the both networks in one step Here is an example for SIMoNeNet and patient status library stringgaussnet data SpADataExpression data SpADEGenes data SpASamples SpAData lt DEGeneExpr t SpADataExpression SpADEGenes StatusFactor lt SpASamples status names StatusFactor lt SpASamples chipnum We create a factor vector based on the status NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe StatusFactorSIMoNeNet lt FactorNetworks GaussianSpAData StatusFactor SIMoNe Level Control 19 H Found a network with 34 edges HH Found a network with 34 edges H Found a network with 34 edges Level Patient H Found a network with 38 edges H Found a network with 38 edges H Found a network with 34 edges We infer different SIMoNe networks on different groups of samples patien
25. esResults preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 H List2 Object of class DEGeneExpr package stringgaussnet H Number of samples 57 Number of genes 17 H DataExpression preview H FAU TFAM FAIM2 CITED2 SIGLEC15 MBIP HAUS1 21 10 30000 6 797904 6 753533 11 23097 7 730661 8 265060 9 547622 22 10 30035 6 786641 10 433228 11 00969 8 486743 8 373733 9 286334 H RFC3 TBCK TRIM24 ANAPC15 PIGB DNAJA4 RBBP9 KIAA0907 21 6 717628 9 507269 9 164891 10 42642 8 663999 8 272130 8 143958 9 352399 23 H H H H H H H H H H H H H H H H H H H H H H H H H H H H H Mu pr H H H H H H H 22 6 572705 9 561226 9 012223 10 77170 8 803228 8 370383 7 665083 9 118642 MUT FBX018 21 8 188985 8 265647 22 8 275170 8 363612 DEGenesResults preview GeneSymbol EnsemblId P Value Fold Change logFC FAU FAU ENSGO0000149806 0 000603 0 56 0 8365013 TFAM TFAM ENSGOO0000108064 0 000640 0 63 0 6665763 List3 Object of class DEGeneExpr package stringgaussnet Number of samples 57 Number of genes 17 DataExpression preview USP30 NDP OLR1 PLP2 MNDA IFT52 COX20 BAK1P1 21 8 903849 7 663387 6 220560 11 36747 8 173222 7 287404 7 968966 6 412227 22 8 475835 7 667593 6 641498 11 09409 9 675167 6 828304 8 0738
26. initial nodes 18 Number of added nodes 0 Number of intermediate nodes 20 Total number of interactions 151 Number of interactions between initial nodes 151 Number of interactions with added nodes 0 Edges preview node1 node2 Interaction Distance NIntermediates Intermediates 1 SAP130 POLR1ID shortestpathway 3 109328 2 MAGOH POLR2E 2 SAP130 CSF3R shortestpathway 3 120991 2 MAGOH UBC 3 SAP130 FAU shortestpathway 2 103994 1 MAGOH 4 SAP130 CITED2 shortestpathway 4 139095 3 DNMT1 PCNA EP300 5 SAP130 RFC3 shortestpathway 3 101098 2 DNMT1 PCNA DEGenes preview HH GeneSymbol EnsemblId P Value Fold Change logFC SAP130 SAP130 ENSGO0000136715 0 000115 0 65 0 6214884 POLR1D POLR1D ENSGO0000186184 0 000224 0 65 0 6214884 CSF3R CSF3R ENSGO0000119535 0 000564 0 32 1 6438562 FAU FAU ENSGO0000149806 0 000603 0 56 0 8365013 CITED2 CITED2 ENSGO0000164442 0 000729 0 40 1 3219281 Here we don t have any added node because we summarized the network only between initial nodes We can see that 20 genes were used as intermediates We have unique edges with distance number of intermediates and intermediate names as attributes SIMoNe network inference Another use of this package is to infer a gaussian network from expression data We implemented the use of the R package simone in order to strongly reduce noise from indirect interactions without supervision Chiquet e
27. ions between initial nodes 7 Number of interactions with added nodes 4020 Edges preview node1 node2 Interaction Score coexpression RPL39 RPL36 coexpression 0 966 experimental RPL39 RPL36 experimental 0 986 knowledge RPL39 RPL36 knowledge 0 900 coexpression1 RPL11 RPL13 coexpression 0 975 experimental1 RPL11 RPL13 experimental 0 999 H DEGenes preview H GeneSymbol EnsemblId P Value Fold Change logFC LRRC4 LRRC4 ENSGO0000128594 8 03e 05 0 42 1 2515388 SAP130 SAP130 ENSGO0000136715 1 15e 04 0 65 0 6214884 SLU7 SLU7 ENSGO0000164609 1 50e 04 0 63 0 6665763 POLR1D POLR1D ENSGO0000186184 2 24e 04 0 65 0 6214884 TNFSF13B TNFSF13B ENSGO0000102524 3 15e 04 0 61 0 7131189 summary PPISpASTRINGNet All interactions H coexpression experimental knowledge Count 2597 0000000 2352 0000000 3232 0 Min score 0 9000000 0 9000000 0 9 Max score 0 9750000 0 9990000 0 9 Mean score 0 9589634 0 9751926 0 9 Median score 0 9670000 0 9890000 0 9 Interactions between initial nodes HH coexpression experimental knowledge Count 5 0000 4 00000 7 0 Min score 0 9180 0 93000 0 9 Max score 0 9750 0 99900 0 9 Mean score 0 9518 0 98025 0 9 Median score 0 9580 0 99600 0 9 Interactions with added nodes HH coexpression experimental knowledge Count 2592 0000000 2348 000000 3225 0 Min score 0 9000000 0 900000 0 9 Max score 0 9750000 0 999000 0
28. ndependently installed on the same machine http www cytoscape org download php Please use at least the version 3 2 1 and make sure to have installed java runtime environnment with version gt 8 https www java com download The communication between R and Cytoscape can not be performed without the plugin cyREST version gt 0 9 17 http apps cytoscape org apps cyrest In order to test if the plugin works fine please turn on Cytoscape and launch the command checkCytoscapeRunning in R If this does not work please try to create a new variable called port number with a value of 1234 Then restart your computer If this still does not work check your Cytoscape cyREST and java versions Starting with differential analysis results Stringgaussnet is not a tool of differential analysis for gene expressions Other powerful tools exist like limma and it is considered that you have already identified key DE genes to analyze in a network before using this R package The differential analysis results constitute the basis of the package use and two data frames are required which are combined into an object of class DEGeneExpr Those are expression data and DE genes statistics Expression data must count samples as row names and genes as column names Values are usually normalized for efficient correlation computation DE genes statistics can be obtained by analyzing expression data to get genes that are affected by a given phenotype Those
29. ode1 node2 Interaction Theta Rho P Value 1 NUDT3 P2RX1 SIMoNeInference 0 2182752 0 8758621 4 668259e 07 2 NUDT3 F13A1 SIMoNeInference 0 3869448 0 8491657 7 174537e 07 DEGenes preview GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 Patient Object of class DEGeneExpr package stringgaussnet Number of samples 27 Number of genes 17 DataExpression preview NUDT3 P2RX1 SGMS2 WDR25 F1i3A1 FAM204A LRRC4 21 10 25609 7 779726 7 478363 7 395941 13 53042 8 865439 6 196102 10 22 10 17532 7 713649 7 426126 7 482414 13 39109 8 743199 6 715109 10 SAP130 SLU7 POUS5FiB POLRiD TTC39C ADAMTS15 TNFSF13B 21 10 17557 8 517671 6 425804 9 553580 7 429002 7 779027 10 17795 8 22 10 18127 7 944881 6 761638 9 350876 7 147367 8 855387 10 84027 8 CSF3R 21 7 383586 22 7 135964 DEGenesResults preview GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 Object of class SIMoNeNet package stringgaussnet Number of nodes 17 Number of interactions 28 Edges preview nodel node2 Interaction Theta Rho P Value 1 NUDT3 P2RX1 SIMoNeInference 0 3228745 0 8925519 1 164776e 06 3 NUDT3 FAM204A SIMoNeInference 0 1920048 0 6990232 7 705345e 05 DEGenes preview GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 73
30. onal nodes were used to construct the network We can see also that 17 interactions between initial nodes were found on contrary to 5168 including added nodes Multiple edges are not taken into account which means that the print function displays unique pairs of genes as interactions We have here for each edge an interaction source attribute and the corresponding score given by STRING Combined scores are also entered with the label combined As we can see differential analysis results are used as node attributes summary SpASTRINGNet All interactions H coexpression cooccurence experimental fusion knowledge Count 4533 0000000 116 0000000 4106 0000000 1 000 3278 0000000 Min score 0 0640000 0 0057970 0 0430000 0 485 0 3600000 Max score 0 9750000 0 5250000 0 9990000 0 485 0 9000000 Mean score 0 7713536 0 2541574 0 7979408 0 485 0 8955888 Median score 0 9360000 0 2405000 0 9300000 0 485 0 9000000 neighborhood textmining Count 1124 0000000 4793 000000 Min score 0 0650000 0 002376 Max score 0 6080000 0 999000 Mean score 0 3569448 0 419683 Median score 0 4620000 0 401000 Interactions between initial nodes coexpression cooccurence experimental knowledge neighborhood Count 11 0000000 2 0000000 12 0000000 7 0 4 00000 Min score 0 1570000 0 1009470 0 1090000 0 9 0 27300 Max score 0 9750000 0 3750000 0 9990000 0 9 0 46200 Mean score 0 6133636 0 23
31. pA Patients Display Altered Functional Capacity and Deregulated Gene Expression Arthritis Research 8 Therapy 16 4 417 doi 10 1186 s13075 014 0417 0 Verfaillie Annelien Hana Imrichova Zeynep Kalender Atak Michael Dewaele Florian Rambow Gert Hulselmans Valerie Christiaens et al 2015 Decoding the Regulatory Landscape of Melanoma Reveals TEADS as Regulators of the Invasive Cell State Nature Communications 6 April doi 10 1038 ncomms7683 Xue Jia Susanne V Schmidt Jil Sander Astrid Draffehn Wolfgang Krebs Inga Quester Dominic De Nardo et al 2014 Transcriptome Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation Immunity 40 2 274 88 doi 10 1016 j immuni 2014 01 006 27
32. pearman s rho This is useful for large networks considering that SIMoNe can infer interactions without strong correlations A function is provided to plot a network preview based on the original simone package Let s see how it works with the example data library stringgaussnet data SpADataExpression data SpADEGenes SpAData lt DEGeneExpr t SpADataExpression SpADEGenes NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe We select a reasonable number of genes for SIMoNe network inference We advice to take a number of genes being inferior to the sample size pickSIMoNeParam GausstianSpAData We use a series of plot provided with the simone package to see which penalty level we can use for the graphical LASSO regression GlobalSIMoNeNet lt getSIMoNeNet GaussianSpAData Found a network with 36 edges Found a network with 36 edges Found a network with 42 edges Here we get the SIMoNe network with default parameters You can type help getSIMoNeNet for more details GlobalSIMoNeNet lt FilterEdges GlobalSIMoNeNet 0 4 Here we can filter on the absolute values of rho being superior to 0 4 print GlobalSIMoNeNet 5 Object of class SIMoNeNet package stringgaussnet Number of nodes 16 Number of interactions 25 Edges preview 12 H H H H H
33. raphs into Cytoscape Dashed square represents original methods of stringgaussnet Main functions are displayed by ending with brackets Hardware and software requirements This R package is operating system independent However some precautions should be taken before using stringgaussnet Firstly one considers that the user you already knows how to use basic functions from R and to install necessary packages The hardware limitations depends on the network sizes you wish to compute You mainly have to consider that whatever was your differential analysis tool stringgaussnet will surely require a lower memory usage because you will analyze a subset of gene expression data A computer with at least 2 Go of RAM a sufficient free disk space gt 1 Go and a reasonably recent CPU lt 5 years old is recommended Stringgaussnet has only been tested with R version of at least 3 2 We can not guarantee stable computation with previous versions Otherwise some R packages must be installed to use all functions from stringgaussnet which are e AnnotationDbi e GO db e VennDiagram e simone e biomaRt e limma e pspearman e igraph e httr e RJSONIO e Reurl e org Hs eg db Regarding packages from CRAN you must install on his own all necessary secondary packages Packages from bioconductor are advised to be installed with the function biocLite In order to be able to export networks into Cytoscape from R this software must be i
34. red gene names can be HGNC symbols or Ensembl IDs the latter being more specific but less intuitive Due to STRING server limitations the number of characters with all gene IDs is limited to 8198 which represents around 400 genes The number of additional nodes can be set but the API gives at least 10 additional genes Then this package proposes to remove all additional nodes a posteriori by your request Those added nodes are useful to see indirect interactions between initial nodes By default two times the number of initial DE genes identifiers are requested to STRING in order to get a maximal covering Different species can be curated the default being homo sapiens Species are entered with taxon identifiers To see correspondence please have a look here http www uniprot org taxonomy This request through STRING API constructs an object of class STRINGNet with a network with multiple edges depending on sources and combined scores Its print function gives the number of initial and added nodes and respective numbers of interactions The summary function displays minimum maximum mean and median scores from different sources of interactions From this object it is possible to select specific sources of interactions and to filter on the scores given by STRING After this step stringgaussnet calculates a new combined score based on the calculation given for STRING version 8 1 Computation of more recent methods is not implemented due to th
35. snet data SpADataExpression data SpADEGenes data SpASamples SpAData lt DEGeneExpr t SpADataExpression SpADEGenes 22 StatusFactor lt SpASamples status names StatusFactor lt SpASamples chipnum NodesForSIMoNe lt rownames SpADEGenes 1 17 GaussianSpAData lt DEGeneExpr t SpADataExpression NodesForSIMoNe SpADEGenes NodesForSIMoNe MultiSpAData lt MultiDEGeneExpr GaussianSpAData DEGeneExpr t SpADataExpression 18 34 SpADEGenes 18 34 DEGeneExpr t SpADataExpression 35 51 SpADEGenes 35 51 We create multiple lists of DE genes results and then a list of DEGeneExpr objects by subsetting the original data print MultiSpAData Object of class MultiDEGeneExpr package stringgaussnet H 3 objects of class DEGeneExpr Listi List2 List3 H Listi Object of class DEGeneExpr package stringgaussnet H Number of samples 57 Number of genes 17 H DataExpression preview H NUDT3 P2RX1 SGMS2 WDR25 F13A1 FAM204A LRRC4 EIF4H 21 10 25609 7 779726 7 478363 7 395941 13 53042 8 865439 6 196102 10 35545 22 10 17532 7 713649 7 426126 7 482414 13 39109 8 743199 6 715109 10 10438 H SAP130 SLU7 POUSF1B POLR1D TTC39C ADAMTS15 TNFSF13B TSPYL5 21 10 17557 8 517671 6 425804 9 553580 7 429002 7 779027 10 17795 8 561254 22 10 18127 7 944881 6 761638 9 350876 7 147367 8 855387 10 84027 8 214381 H CSF3R 21 7 383586 22 7 135964 H DEGen
36. t al 2009 All options from SIMoNe are changeable in stringgaussnet and default values are given for users 11 who want to discover this tool The default method to select the inferred model by SIMoNe in our package helps to make a choice with a better compromise The number of edges is selected by computing the mean between those with maximal AIC and BIC scores You can choose otherwise a fixed edges number or to base only on AIC or BIC score Without choice from you the algorithm computes a network with or without clustering constraints and selects only common edges between the both models A function is provided to help in selecting the best model inferred by SIMoNe with a series of graphs which are already implemented in the simone package Notably you can see all BIC and AIC scores as a function of the penalty level given in the graphical LASSO method In addition to the theta score given by SIMoNe stringgaussnet computes spearman s test for each inferred edge with an AS89 approximation of null distribution This inference creates an object of class SIMoNeNet with a network of unique edges including theta spearman s rho and p value as attributes The associated print function displays the number of nodes and edges with a preview of node and edge attributes The summary function summarizes theta scores spearman s rho and their absolute values and spearman s test p values This is possible to filter on edge attributes notably on s
37. ts and controls StatusFactorSIMoNeNet lt FilterEdges StatusFactorSIMoNeNet 0 4 We can filter on edges like for SIMoNeNet print StatusFactorSIMoNeNet Object of class FactorNetworks package stringgaussnet HH Levels distribution Control Patient 30 27 Control Object of class DEGeneExpr package stringgaussnet Number of samples 30 Number of genes 17 DataExpression preview NUDT3 P2RX1 SGMS2 WDR25 F1i3A1 FAM204A LRRC4 31 10 53476 8 356974 7 519848 7 661825 13 35763 8 663832 6 544332 32 10 47816 8 205153 7 915057 7 674115 13 61540 8 873416 6 676184 11 10315 SAP130 SLU7 POUS5F1B POLRiD TTC39C ADAMTS15 TNFSF13B 31 10 21238 8 166741 6 673612 9 473133 7 468633 7 515297 10 46620 32 10 39199 8 212908 6 523527 9 760605 7 596354 8 080091 11 00189 CSF3R 31 7 508418 32 8 067584 DEGenesResults preview HH GeneSymbol EnsemblId P Value Fold Change logFC NUDT3 NUDT3 ENSGO0000112664 4 60e 06 0 60 0 7369656 P2RX1 P2RX1 ENSGO0000108405 8 45e 06 0 52 0 9434165 HH Object of class SIMoNeNet package stringgaussnet Number of nodes 16 Number of interactions 23 20 EIF4H 10 73350 TSPYL5 8 811882 8 496030 H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H Edges preview n
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