{"product_id":"molecular-data-analysis-using-r-isbn-9781119165026","title":"Molecular Data Analysis Using R","description":"This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data.  The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology.  The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories.  \u003cbr\u003e\u003cbr\u003eKey features include:\u003cbr\u003e• Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered.\u003cbr\u003e• First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology.\u003cbr\u003e• Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users. \u003cp\u003eForeword, xiii\u003c\/p\u003e \u003cp\u003ePreface, xv\u003c\/p\u003e \u003cp\u003eAcknowledgements, xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website, xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to R statistical environment, 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy R?, 1\u003c\/p\u003e \u003cp\u003eInstalling R, 2\u003c\/p\u003e \u003cp\u003eInteracting with R, 2\u003c\/p\u003e \u003cp\u003eGraphical interfaces and integrated development environment (IDE) integration, 3\u003c\/p\u003e \u003cp\u003eScripting and sourcing, 3\u003c\/p\u003e \u003cp\u003eThe R history and the R environment file, 4\u003c\/p\u003e \u003cp\u003ePackages and package repositories, 4\u003c\/p\u003e \u003cp\u003eComprehensive R Archive Network, 5\u003c\/p\u003e \u003cp\u003eBioconductor, 6\u003c\/p\u003e \u003cp\u003eWorking with data, 7\u003c\/p\u003e \u003cp\u003eBasic operations in R, 8\u003c\/p\u003e \u003cp\u003eSome basics of graphics in R, 10\u003c\/p\u003e \u003cp\u003eGetting help in R, 12\u003c\/p\u003e \u003cp\u003eFiles for practicing, 13\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 14\u003c\/p\u003e \u003cp\u003eReferences, 14\u003c\/p\u003e \u003cp\u003eWebliography, 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Simple sequence analysis, 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSequence files, 17\u003c\/p\u003e \u003cp\u003eFASTA sequence format, 18\u003c\/p\u003e \u003cp\u003eGenBank flat file format, 19\u003c\/p\u003e \u003cp\u003eReading sequence files into R, 20\u003c\/p\u003e \u003cp\u003eObtaining sequences from remote databases, 21\u003c\/p\u003e \u003cp\u003eSeqinr package, 21\u003c\/p\u003e \u003cp\u003eApe package, 22\u003c\/p\u003e \u003cp\u003eDescriptive statistics of nucleotide sequences, 24\u003c\/p\u003e \u003cp\u003eDescriptive statistics of proteins, 28\u003c\/p\u003e \u003cp\u003eAligned sequences, 31\u003c\/p\u003e \u003cp\u003eVisualization of genes and transcripts in a professional way, 34\u003c\/p\u003e \u003cp\u003eFiles for practicing, 37\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 38\u003c\/p\u003e \u003cp\u003eReferences, 38\u003c\/p\u003e \u003cp\u003eWebliography, 39\u003c\/p\u003e \u003cp\u003ePackages, 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Annotating gene groups, 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnrichment analysis: an overview, 41\u003c\/p\u003e \u003cp\u003eOverview of two different methods, 41\u003c\/p\u003e \u003cp\u003eEnrichment analysis results, 42\u003c\/p\u003e \u003cp\u003eCommon aspects of the two different approaches, 43\u003c\/p\u003e \u003cp\u003eOverrepresentation analysis, 46\u003c\/p\u003e \u003cp\u003eHypergeometric test using GOstats, 47\u003c\/p\u003e \u003cp\u003eORA analysis using topGO, 48\u003c\/p\u003e \u003cp\u003eEnrichment analysis of microarray sets with topGO, 51\u003c\/p\u003e \u003cp\u003eGene set enrichment analysis, 52\u003c\/p\u003e \u003cp\u003eGSEA with R, 56\u003c\/p\u003e \u003cp\u003eFiles for practicing, 61\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 61\u003c\/p\u003e \u003cp\u003eReferences, 62\u003c\/p\u003e \u003cp\u003eWebliography, 62\u003c\/p\u003e \u003cp\u003ePackages, 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Next-generation sequencing: introduction and genomic applications, 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHigh-throughput sequencing background, 65\u003c\/p\u003e \u003cp\u003eExperimental background, 66\u003c\/p\u003e \u003cp\u003eSingle-end and paired-end sequencing reads, 67\u003c\/p\u003e \u003cp\u003eAssemble reads, 69\u003c\/p\u003e \u003cp\u003eHow many reads? Depth of coverage, 71\u003c\/p\u003e \u003cp\u003eStoring data in files, 72\u003c\/p\u003e \u003cp\u003eFASTQ, 72\u003c\/p\u003e \u003cp\u003eSAM and BAM files, 76\u003c\/p\u003e \u003cp\u003eVariant call format files, 77\u003c\/p\u003e \u003cp\u003eGeneral data analysis workflow, 77\u003c\/p\u003e \u003cp\u003eData processing considerations, 78\u003c\/p\u003e \u003cp\u003eQuality checking and screening read sequences, 80\u003c\/p\u003e \u003cp\u003eQuality checking for one file, 82\u003c\/p\u003e \u003cp\u003eQuality inspection for multiple files in a project, 82\u003c\/p\u003e \u003cp\u003eQuality filtering of FASTQ files, 83\u003c\/p\u003e \u003cp\u003eHandling alignment files and genomic variants, 84\u003c\/p\u003e \u003cp\u003eAlignment and variation visualization, 88\u003c\/p\u003e \u003cp\u003eSimple handling of VCF files, 89\u003c\/p\u003e \u003cp\u003eGenomic applications: low- and medium-depth sequencing, 91\u003c\/p\u003e \u003cp\u003eAneuploidity sequencing and copy number variation identification, 92\u003c\/p\u003e \u003cp\u003eSNP identification and validation, 92\u003c\/p\u003e \u003cp\u003eExome sequencing, 93\u003c\/p\u003e \u003cp\u003eGenomic region resequencing, 93\u003c\/p\u003e \u003cp\u003eFull genome and metagenome sequencing, 94\u003c\/p\u003e \u003cp\u003eFiles for practicing, 94\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 94\u003c\/p\u003e \u003cp\u003eReferences, 95\u003c\/p\u003e \u003cp\u003eWebliography, 97\u003c\/p\u003e \u003cp\u003ePackages, 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Quantitative transcriptomics: qRT-PCR, 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTranscriptome, 99\u003c\/p\u003e \u003cp\u003ePolymerase chain reaction, 100\u003c\/p\u003e \u003cp\u003eStandards for qPCR, 102\u003c\/p\u003e \u003cp\u003eR packages, 104\u003c\/p\u003e \u003cp\u003eUnderstanding delta Ct, 104\u003c\/p\u003e \u003cp\u003eCalculation of delta Ct, 105\u003c\/p\u003e \u003cp\u003eRequirements for real delta Ct calculations, 107\u003c\/p\u003e \u003cp\u003eAbsolute quantification, 110\u003c\/p\u003e \u003cp\u003eValue prediction, the professional way, 114\u003c\/p\u003e \u003cp\u003eRelative quantification using the ddCt method, 115\u003c\/p\u003e \u003cp\u003eComparison of two conditions, 116\u003c\/p\u003e \u003cp\u003eComparison of multiple experimental conditions, 118\u003c\/p\u003e \u003cp\u003eQuality control with melting curve, 121\u003c\/p\u003e \u003cp\u003eFiles for practicing, 123\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 123\u003c\/p\u003e \u003cp\u003eReferences, 123\u003c\/p\u003e \u003cp\u003eWebliography, 124\u003c\/p\u003e \u003cp\u003ePackages, 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Advanced transcriptomics: gene expression microarrays, 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMicroarray analysis: probes and samples, 125\u003c\/p\u003e \u003cp\u003eExperimental background, 126\u003c\/p\u003e \u003cp\u003eArchiving and publishing microarray data, 128\u003c\/p\u003e \u003cp\u003eMinimum information standard, 128\u003c\/p\u003e \u003cp\u003eData preprocessing, 128\u003c\/p\u003e \u003cp\u003eAccessing data from CEL files, 129\u003c\/p\u003e \u003cp\u003eQuality control, 131\u003c\/p\u003e \u003cp\u003eNormalization, 132\u003c\/p\u003e \u003cp\u003eDifferential gene expression, 133\u003c\/p\u003e \u003cp\u003eAnnotating results, 136\u003c\/p\u003e \u003cp\u003eCreating normalized expression set from Illumina data, 138\u003c\/p\u003e \u003cp\u003eAutomated data access from GEO, 140\u003c\/p\u003e \u003cp\u003eFiles for practicing, 142\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 142\u003c\/p\u003e \u003cp\u003eReferences, 143\u003c\/p\u003e \u003cp\u003eWebliography, 144\u003c\/p\u003e \u003cp\u003ePackages, 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Next-generation sequencing in transcriptomics: RNA-seq experiments, 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHigh-throughput RNA sequencing background, 145\u003c\/p\u003e \u003cp\u003eExperimental background, 145\u003c\/p\u003e \u003cp\u003eRNA-seq applications, 146\u003c\/p\u003e \u003cp\u003eDifferential expression with different resolutions, 147\u003c\/p\u003e \u003cp\u003ePreparing count tables, 148\u003c\/p\u003e \u003cp\u003eAlignment files to read counts, 148\u003c\/p\u003e \u003cp\u003eDifferential expression in simple comparison, 151\u003c\/p\u003e \u003cp\u003eA naive t-test approach, 151\u003c\/p\u003e \u003cp\u003eSingle factor analysis with edgeR, 153\u003c\/p\u003e \u003cp\u003eDifferential expression with DESeq, 156\u003c\/p\u003e \u003cp\u003eComplex experimental arrangements, 159\u003c\/p\u003e \u003cp\u003eExperimental factors and design matrix, 160\u003c\/p\u003e \u003cp\u003eGLM with edgeR, 161\u003c\/p\u003e \u003cp\u003eGLMs with DESeq, 162\u003c\/p\u003e \u003cp\u003eHeatmap visualization, 163\u003c\/p\u003e \u003cp\u003eFiles for practicing, 164\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 164\u003c\/p\u003e \u003cp\u003eReferences, 165\u003c\/p\u003e \u003cp\u003eWebliography, 166\u003c\/p\u003e \u003cp\u003ePackages, 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Deciphering the regulome: from ChIP to ChIP-seq, 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChromatin immunoprecipitation, 167\u003c\/p\u003e \u003cp\u003eExperimental background, 168\u003c\/p\u003e \u003cp\u003eFragment analysis, 168\u003c\/p\u003e \u003cp\u003eChIP data in ENCODE, 169\u003c\/p\u003e \u003cp\u003eChIP with tiling microarrays, 169\u003c\/p\u003e \u003cp\u003eHigh-throughput sequencing of ChIP fragments, 176\u003c\/p\u003e \u003cp\u003eConnecting annotation to peaks, 181\u003c\/p\u003e \u003cp\u003eAnalysis of binding site motifs, 182\u003c\/p\u003e \u003cp\u003eFiles for practicing, 186\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 187\u003c\/p\u003e \u003cp\u003eReferences, 187\u003c\/p\u003e \u003cp\u003eWebliography, 188\u003c\/p\u003e \u003cp\u003ePackages, 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Inferring regulatory and other networks from gene expression data, 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGene regulatory networks, 191\u003c\/p\u003e \u003cp\u003eData for gene network inference, 192\u003c\/p\u003e \u003cp\u003eReconstruction of co-expression networks, 193\u003c\/p\u003e \u003cp\u003eGene regulatory network inference focusing of master regulators, 201\u003c\/p\u003e \u003cp\u003eIntegrated interpretation of genes with GeneAnswers, 207\u003c\/p\u003e \u003cp\u003eFiles for practicing, 211\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 212\u003c\/p\u003e \u003cp\u003eReferences, 213\u003c\/p\u003e \u003cp\u003ePackages, 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analysis of biological networks, 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA gentle introduction to networks, 215\u003c\/p\u003e \u003cp\u003eNetworks and their components and features, 215\u003c\/p\u003e \u003cp\u003eRandom networks, 220\u003c\/p\u003e \u003cp\u003eBiological networks, 221\u003c\/p\u003e \u003cp\u003eFiles for storing network information, 223\u003c\/p\u003e \u003cp\u003eImportant network metrics in biology, 227\u003c\/p\u003e \u003cp\u003eDistance-based measures, 228\u003c\/p\u003e \u003cp\u003eDegree and related measures, 230\u003c\/p\u003e \u003cp\u003eVulnerability, 231\u003c\/p\u003e \u003cp\u003eCommunity structure of a network, 234\u003c\/p\u003e \u003cp\u003eGraph visualization, 236\u003c\/p\u003e \u003cp\u003eCytoscape, 240\u003c\/p\u003e \u003cp\u003eFiles for practicing, 241\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 241\u003c\/p\u003e \u003cp\u003eReferences, 242\u003c\/p\u003e \u003cp\u003eWebliography, 243\u003c\/p\u003e \u003cp\u003ePackages, 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Proteomics: mass spectrometry, 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMass spectrometry and proteomics: why and how?, 245\u003c\/p\u003e \u003cp\u003eFile formats for MS data, 246\u003c\/p\u003e \u003cp\u003eAccessing the raw data of published studies, 247\u003c\/p\u003e \u003cp\u003eIdentification of peptides in the samples, 249\u003c\/p\u003e \u003cp\u003ePeptide mass fingerprinting, 249\u003c\/p\u003e \u003cp\u003ePeptide identification by using MS\/MS spectra, 250\u003c\/p\u003e \u003cp\u003eQuantitative proteomics, 254\u003c\/p\u003e \u003cp\u003eGetting protein-specific annotation, 258\u003c\/p\u003e \u003cp\u003eFiles for practicing, 259\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 259\u003c\/p\u003e \u003cp\u003eReferences, 259\u003c\/p\u003e \u003cp\u003eWebliography, 260\u003c\/p\u003e \u003cp\u003ePackages, 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Measuring protein abundance with ELISA, 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnzyme-linked immunosorbent assays, 261\u003c\/p\u003e \u003cp\u003eAccessing ELISA data, 264\u003c\/p\u003e \u003cp\u003eConcentration calculation with a standard curve, 264\u003c\/p\u003e \u003cp\u003ePreparing reference data, 267\u003c\/p\u003e \u003cp\u003eFitting linear model, 268\u003c\/p\u003e \u003cp\u003eFitting of a logistic model, 269\u003c\/p\u003e \u003cp\u003eConcentration calculations by employing models, 270\u003c\/p\u003e \u003cp\u003eComparative calculations using concentrations, 271\u003c\/p\u003e \u003cp\u003eFiles for practicing, 277\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 277\u003c\/p\u003e \u003cp\u003eReferences, 277\u003c\/p\u003e \u003cp\u003ePackages, 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Flow cytometry: counting and sorting stained cells, 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTheoretical aspects of flow cytometry, 279\u003c\/p\u003e \u003cp\u003eExperiment types: diagnosis versus discovery, 280\u003c\/p\u003e \u003cp\u003eMeasurement arrangements, 281\u003c\/p\u003e \u003cp\u003eFluorescent dyes, 281\u003c\/p\u003e \u003cp\u003eTubes versus plates, 285\u003c\/p\u003e \u003cp\u003eInstruments, 285\u003c\/p\u003e \u003cp\u003eWhat about data?, 287\u003c\/p\u003e \u003cp\u003eFiles, 287\u003c\/p\u003e \u003cp\u003eWorkflows, 288\u003c\/p\u003e \u003cp\u003eData preprocessing, 289\u003c\/p\u003e \u003cp\u003eHandling all samples together, 290\u003c\/p\u003e \u003cp\u003eCompensation, 292\u003c\/p\u003e \u003cp\u003eQuality assurance, 292\u003c\/p\u003e \u003cp\u003eUsing workflow objects and transformation, 296\u003c\/p\u003e \u003cp\u003eNormalization, 298\u003c\/p\u003e \u003cp\u003eCell population identification, 299\u003c\/p\u003e \u003cp\u003eManual gating, 300\u003c\/p\u003e \u003cp\u003eAutomatic gating, 304\u003c\/p\u003e \u003cp\u003eRelating cell populations to external variables, 305\u003c\/p\u003e \u003cp\u003eReporting results, 307\u003c\/p\u003e \u003cp\u003eMIFlowCyt, 307\u003c\/p\u003e \u003cp\u003eFlowRepository.org, 308\u003c\/p\u003e \u003cp\u003eFiles for practicing, 308\u003c\/p\u003e \u003cp\u003eStudy exercises and questions, 309\u003c\/p\u003e \u003cp\u003eReferences, 309\u003c\/p\u003e \u003cp\u003eWebliography, 310\u003c\/p\u003e \u003cp\u003ePackages, 310\u003c\/p\u003e \u003cp\u003eGlossary, 311\u003c\/p\u003e \u003cp\u003eIndex, 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCsaba Ortutay\u003c\/b\u003e is a bioinformatician from Finland who has taught several bioinformatics courses at different European universities (Finland, Ireland, and Hungary) for over a decade. He is also active as a researcher publishing in the field of computational immunology.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eZsuzsanna Ortutay\u003c\/b\u003e is a molecular immunologist at the University of Tampere, Finland, frequently utilizing diverse molecular lab methods.\u003c\/p\u003e \u003cp\u003eThis book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data.  The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey features include:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBroad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered.\u003c\/li\u003e \u003cli\u003eFirst book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology.\u003c\/li\u003e \u003cli\u003eFocuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eFurther, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989647507685,"sku":"NP9781119165026","price":107.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119165026.jpg?v=1761784948","url":"https:\/\/k12savings.com\/products\/molecular-data-analysis-using-r-isbn-9781119165026","provider":"K12savings","version":"1.0","type":"link"}