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<h4 class="author"><em>Claudia Cava, Isabella Castiglioni</em></h4>
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<h1>Contents</h1>
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<li><a href="#getnetdata-searching-network-data-for-download"><code>getNETdata</code>: Searching network data for download</a></li>
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</ul></li>
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<li><a href="#integration-data-integration-between-kegg-pathway-and-network-data"><code>Integration data</code>: Integration between KEGG pathway and network data</a><ul>
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+<li><a href="#path_net-network-of-interacting-genes-for-each-pathway-according-a-network-type-phintcolocgenintpathshpd"><code>path_net</code>: Network of interacting genes for each pathway according a network type (PHint,COloc,GENint,PATH,SHpd)</a></li>
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<li><a href="#list_path_net-list-of-interacting-genes-for-each-pathway-list-of-genes-according-a-network-type-phintcolocgenintpathshpd"><code>list_path_net</code>: List of interacting genes for each pathway (list of genes) according a network type (PHint,COloc,GENint,PATH,SHpd)</a></li>
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<li><a href="#pathway-summary-indexes-score-for-each-pathway"><code>Pathway summary indexes</code>: Score for each pathway</a><ul>
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<li><a href="#st_dv-standard-deviations-of-genes-for-each-pathway-starting-from-a-matrix-of-gene-expression"><code>st_dv</code>: Standard deviations of genes for each pathway starting from a matrix of gene expression</a></li>
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<div id="introduction" class="section level1">
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<h1>Introduction</h1>
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-<p>Motivation: New technologies have made possible to identify marker gene signatures. However, gene expression-based signatures present some limitations because they do not consider metabolic role of the genes and are affected by genetic heterogeneity across patient cohorts. Considering the activity of entire pathways rather than the expression levels of individual genes can be a way to exceed these limits. This tool <code>StarTrek</code> presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network and TCGA data. New measures are under development.</p>
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+<p>Motivation: New technologies have made possible to identify marker gene signatures. However, gene expression-based signatures present some limitations because they do not consider metabolic role of the genes and are affected by genetic heterogeneity across patient cohorts. Considering the activity of entire pathways rather than the expression levels of individual genes can be a way to exceed these limits. This tool <code>StarBioTrek</code> presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network and TCGA data. New measures are under development.</p>
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<p>To install use the code below.</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">source</span>(<span class="st">"https://bioconductor.org/biocLite.R"</span>)
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-<span class="kw">biocLite</span>(<span class="st">"StarTrek"</span>)</code></pre></div>
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<h1><code>Get data</code>: Get KEGG pathway, network and TCGA data</h1>
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<li><strong>sens_syst </strong> Sensory_system</li>
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<p>The following code is an example to download the pathways involved in Transcription:</p>
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-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">path<-<span class="kw">getKEGGdata</span>(<span class="dt">KEGG_path=</span><span class="st">"Transcript"</span>)</code></pre></div>
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<p>For example the group Transcript contains different pathways:</p>
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<caption>List of patwhays for the group Transcript</caption>
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<p>For default the organism is homo sapiens. The example show the shared protein domain network for Saccharomyces_cerevisiae. For more information see <code>SpidermiR</code> package.</p>
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+<h2><code>path_net</code>: Network of interacting genes for each pathway according a network type (PHint,COloc,GENint,PATH,SHpd)</h2>
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+<p>The function <code>path_net</code> creates a network of interacting genes for each pathway. Interacting genes are genes belonging to the same pathway and the interaction is given from network chosen by the user, according the paramenters of the function <code>getNETdata</code>. The output will be a network of genes belonging to the same pathway.</p>
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+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">network_path<-<span class="kw">path_net</span>(<span class="dt">pathway=</span>path,<span class="dt">net_type=</span>netw)</code></pre></div>
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+<pre><code>## [1] "1 PATHWAY Cell cycle - Homo sapiens (human)"
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+## [1] "2 PATHWAY p53 signaling pathway - Homo sapiens (human)"</code></pre>
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<p>The function <code>list_path_net</code> creates a list of interacting genes for each pathway. Interacting genes are genes belonging to the same pathway and the interaction is given from network chosen by the user, according the paramenters of the function <code>getNETdata</code>. The output will be a list of genes belonging to the same pathway and those having an interaction in the network.</p>
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+<p>Starting from a matrix of gene expression (rows are genes and columns are samples, TCGA data) the function <code>GE_matrix</code> creates a of gene expression levels for each pathway given by the user:</p>
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<p>Starting from a matrix of gene expression (rows are genes and columns are samples, TCGA data) the function <code>average</code> creates an average matrix of gene expression for each pathway:</p>
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<h2><code>st_dv</code>: Standard deviations of genes for each pathway starting from a matrix of gene expression</h2>
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<p>Starting from a matrix of gene expression (rows are genes and columns are samples, TCGA data) the function <code>st_dv</code> creates a standard deviation matrix of gene expression for each pathway:</p>
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<p>Starting from a matrix of gene expression (rows are genes and columns are samples, TCGA data) the function <code>euc_dist_crtlk</code> creates an euclidean distance matrix of gene expression for pairwise pathway.</p>
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-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">score_euc_dist<-<span class="kw">euc_dist_crtlk</span>(<span class="dt">dataFilt=</span>Data_CANCER_normUQ_filt,<span class="dt">pathway=</span>path)</code></pre></div>
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<p>Starting from a matrix of gene expression (rows are genes and columns are samples, TCGA data) the function <code>ds_score_crtlk</code> creates an discriminating score matrix for pairwise pathway as measure of cross-talk. Discriminating score is given by |M1-M2|/S1+S2 where M1 and M2 are mean and S1 and S2 standard deviation of expression levels of genes in a pathway 1 and and in a pathway 2 .</p>
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<h2><code>svm_classification</code>: SVM classification</h2>
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<p>Given the substantial difference in the activities of many pathways between two classes (e.g. normal and cancer), we examined the effectiveness to classify the classes based on their pairwise pathway profiles. This function is used to find the interacting pathways that are altered in a particular pathology in terms of Area Under Curve (AUC).AUC was estimated by cross-validation method (k-fold cross-validation, k=10).It randomly selected some fraction of TCGA data (e.g. nf= 60; 60% of original dataset) to form the training set and then assigned the rest of the points to the testing set (40% of original dataset). For each pairwise pathway the user can obtain using the methods mentioned above a score matrix ( e.g.dev_std_crtlk ) and can focus on the pairs of pathways able to differentiate a particular subtype with respect to the normal type.</p>
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-res_class<-<span class="kw">svm_classification</span>(<span class="dt">TCGA_matrix=</span>score_euc_dist,<span class="dt">nfs=</span>nf,<span class="dt">normal=</span><span class="kw">colnames</span>(norm[,<span class="dv">1</span>:<span class="dv">12</span>]),<span class="dt">tumour=</span><span class="kw">colnames</span>(tumo[,<span class="dv">1</span>:<span class="dv">12</span>]))</code></pre></div>
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-## [1] "Basaltranscriptionfactors_Spliceosome"</code></pre>
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+res_class<-<span class="kw">svm_classification</span>(<span class="dt">TCGA_matrix=</span>score_euc_dist[<span class="dv">1</span>:<span class="dv">2</span>,],<span class="dt">nfs=</span>nf,<span class="dt">normal=</span><span class="kw">colnames</span>(norm[,<span class="dv">1</span>:<span class="dv">12</span>]),<span class="dt">tumour=</span><span class="kw">colnames</span>(tumo[,<span class="dv">1</span>:<span class="dv">12</span>]))</code></pre></div>
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+<pre><code>## [1] "Cellcycle_p53signalingpathway"</code></pre>
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+<p><img 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" /></p>
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+## [36] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
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+## [37] graph_1.50.0
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+## [38] roxygen2_5.0.1
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+## [44] iterators_1.0.8
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+## [46] gtable_0.2.0
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+## [48] XVector_0.12.1
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+## [50] kernlab_0.9-25
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+## [51] Rgraphviz_2.16.0
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+## [52] shape_1.4.2
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+## [53] prabclus_2.2-6
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+## [54] DEoptimR_1.0-6
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+## [55] scales_0.4.1
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+## [56] DESeq_1.24.0
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+## [57] mvtnorm_1.0-5
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+## [58] DBI_0.5-1
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+## [59] GGally_1.2.0
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+## [60] edgeR_3.14.0
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+## [62] Rcpp_0.12.7
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+## [63] xtable_1.8-2
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+## [64] matlab_1.0.2
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-## [94] KernSmooth_2.23-15
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-## [95] minqa_1.2.4
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-## [96] R.oo_1.20.0
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-## [97] htmltools_0.3.5
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-## [104] MASS_7.3-45
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-## [106] ShortRead_1.30.0
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-## [108] readr_1.0.0
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-## [110] R.methodsS3_1.7.1
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-## [111] gdata_2.17.0
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-## [113] GenomicRanges_1.24.3
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-## [114] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
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393
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-## [116] coin_1.1-2
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-## [126] digest_0.6.10
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-## [127] ConsensusClusterPlus_1.36.0
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-## [128] graph_1.50.0
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-## [129] Biostrings_2.40.2
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409
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-## [132] dendextend_1.3.0
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419
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|
422
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|
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-## [147] KEGGREST_1.12.3
|
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-## [148] httr_1.2.1
|
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-## [149] DEoptimR_1.0-6
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-## [150] survival_2.39-4
|
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-## [151] prabclus_2.2-6
|
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-## [152] iterators_1.0.8
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-## [153] Rgraphviz_2.16.0
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-## [156] org.Hs.eg.db_3.3.0
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-## [158] caTools_1.17.1
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-## [162] ape_3.5</code></pre>
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+## [68] htmlwidgets_0.8
|
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+## [69] httr_1.2.1
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+## [70] gplots_3.0.1
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+## [71] RColorBrewer_1.1-2
|
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+## [72] fpc_2.1-10
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+## [73] modeltools_0.2-21
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+## [74] reshape_0.8.6
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+## [75] XML_3.98-1.4
|
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+## [76] R.methodsS3_1.7.1
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+## [77] flexmix_2.3-13
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+## [78] nnet_7.3-12
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+## [79] visNetwork_1.0.2
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+## [80] munsell_0.4.3
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+## [81] tools_3.3.1
|
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+## [82] downloader_0.4
|
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+## [83] gsubfn_0.6-6
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+## [84] RSQLite_1.0.0
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+## [85] devtools_1.12.0
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+## [86] evaluate_0.10
|
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+## [87] stringr_1.1.0
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+## [88] yaml_2.1.13
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+## [89] org.Hs.eg.db_3.3.0
|
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+## [90] knitr_1.15
|
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+## [91] robustbase_0.92-6
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+## [92] caTools_1.17.1
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+## [93] KEGGREST_1.12.3
|
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+## [94] dendextend_1.3.0
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+## [95] coin_1.1-2
|
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+## [96] TCGAbiolinks_2.3.2
|
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+## [97] EDASeq_2.6.2
|
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+## [98] nlme_3.1-128
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+## [99] whisker_0.3-2
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+## [100] R.oo_1.21.0
|
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+## [101] xml2_1.0.0
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+## [102] biomaRt_2.28.0
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+## [103] curl_2.2
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+## [104] e1071_1.6-7
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+## [105] affyio_1.42.0
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+## [106] tibble_1.2
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+## [107] geneplotter_1.50.0
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+## [108] stringi_1.1.2
|
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+## [109] highr_0.6
|
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403
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+## [110] GenomicFeatures_1.24.5
|
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+## [111] lattice_0.20-34
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405
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+## [112] trimcluster_0.1-2
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406
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+## [113] Matrix_1.2-7.1
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407
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+## [114] networkD3_0.2.13
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408
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+## [115] GlobalOptions_0.0.10
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409
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+## [116] parmigene_1.0.2
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+## [117] data.table_1.9.6
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+## [118] bitops_1.0-6
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+## [119] dnet_1.0.9
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+## [120] rtracklayer_1.32.2
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+## [121] GenomicRanges_1.24.3
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+## [122] R6_2.2.0
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+## [123] latticeExtra_0.6-28
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+## [124] affy_1.50.0
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+## [125] hwriter_1.3.2
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+## [126] ShortRead_1.30.0
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+## [127] gridExtra_2.2.1
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+## [128] KernSmooth_2.23-15
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+## [129] codetools_0.2-15
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+## [130] MASS_7.3-45
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+## [131] gtools_3.5.0
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425
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+## [132] assertthat_0.1
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+## [133] chron_2.3-47
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+## [134] SummarizedExperiment_1.2.3
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+## [135] proto_1.0.0
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+## [136] rjson_0.2.15
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+## [137] withr_1.0.2
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431
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+## [138] SpidermiR_1.4.2
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432
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+## [139] GenomicAlignments_1.8.4
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433
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+## [140] Rsamtools_1.24.0
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434
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+## [141] multcomp_1.4-6
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435
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+## [142] diptest_0.75-7
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+## [143] class_7.3-14
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+## [144] rmarkdown_1.1</code></pre>
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<div id="references" class="section level1 unnumbered">
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