# Hipathia Package

Hipathia package implements the Canonical Circuit Activity Analysis method for the quantification of the signaling pathways activity presented in Hidalgo et al. This method has been implemented in the webtool http://hipathia.babelomics.org, allowing the user to compare signal propagation in an experiment, and train and use a predictor based on the activation of the canonical circuits or subpathways. The package hipathia has been conceived as a functional tool for R users which allows more control on the analysis pipeline than the web implementation does.

This document will introduce you to the hipathia package and how to use it to analyze your data.

Hipathia is a method for the computation of signal transduction along signaling pathways taking as input transcriptomics data. The method is independent on the pathways database, it only needs information about the topology of the graph and the genes included in each node.

However, due to computational cost, hipathia needs to preprocess the graphs to be fully efficient. In the current implementation we have developed a module which has preprocessed 145 KEGG pathway KGML files, which are ready to be analyzed.

Further versions of the package will allow the user to preprocess their own graph pathways to be analyzed with hipathia.

In order to install the hipathia package, type on your R console

    ## try http:// if https:// URLs are not supported
    if (!requireNamespace("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("hipathia")

In order to illustrate the hipathia package functionalities an example dataset has been prepared. Data has been downloaded from The Cancer Genome Atlas data repository, from the BRCA-US project, release 20. 20 tumor and 20 normal samples of RNA-Seq data have been randomly selected and normalized.

Specifically, raw data has been corrected for batch effect using the `ComBat` function from package sva, then corrected for RNA composition bias applying TMM normalization from package edgeR, and finally log-transformed.

    library(hipathia)
    data("brca")
    brca

    ## class: SummarizedExperiment
    ## dim: 3187 40
    ## metadata(0):
    ## assays(1): raw
    ## rownames(3187): 2 8647 ... 3925 219699
    ## rowData names(0):
    ## colnames(40): TCGA.BH.A1FM.11B.23R.A13Q.07 TCGA.E2.A1LB.11A.22R.A144.07
    ##   ... TCGA.A2.A0CT.01A.31R.A056.07 TCGA.BH.A18U.01A.21R.A12D.07
    ## colData names(1): group

The dataset `brca` is a SummarizedExperiment object, including the gene expression of the 40 samples in the assay `raw`, and the information about whether each sample comes from Tumor or Normal tissues in the `group` columns of the `colData` dataFrame.

    hhead(assay(brca), 4)

    ##      TCGA.BH.A1FM.11B.23R.A13Q.07 TCGA.E2.A1LB.11A.22R.A144.07
    ## 2                      10.5317320                     9.732938
    ## 8647                   -3.3266788                    -3.457515
    ## 5244                   -0.3600828                    -1.139309
    ## 1244                    2.2876961                     1.724625
    ##      TCGA.BH.A208.11A.51R.A157.07 TCGA.BH.A18K.11A.13R.A12D.07
    ## 2                       9.7958036                    10.868669
    ## 8647                   -2.5261155                    -3.584934
    ## 5244                   -0.7368491                    -1.257797
    ## 1244                    1.0217356                     1.467979

    colData(brca)

    ## DataFrame with 40 rows and 1 column
    ##                                    group
    ##                              <character>
    ## TCGA.BH.A1FM.11B.23R.A13Q.07      Normal
    ## TCGA.E2.A1LB.11A.22R.A144.07      Normal
    ## TCGA.BH.A208.11A.51R.A157.07      Normal
    ## TCGA.BH.A18K.11A.13R.A12D.07      Normal
    ## TCGA.E9.A1RC.11A.33R.A157.07      Normal
    ## ...                                  ...
    ## TCGA.AO.A12A.01A.21R.A115.07       Tumor
    ## TCGA.AR.A0TR.01A.11R.A084.07       Tumor
    ## TCGA.A8.A07E.01A.11R.A034.07       Tumor
    ## TCGA.A2.A0CT.01A.31R.A056.07       Tumor
    ## TCGA.BH.A18U.01A.21R.A12D.07       Tumor

Hipathia has been designed to work with matrices encapsulated as SummarizedExperiment objects, in which also the experimental design has been included. However, it is also possible to work in hipathia with matrix objects, as long as the experimental design is provided when needed.

Imagine we have the expression data stored in a matrix object called `brca_data` and the experimental design stored in a data frame with one column called `brca_design`. Then, in order to summarize this data in a `SummarizedExperiment` object we should only run:

    brca <- SummarizedExperiment(assays=SimpleList(raw=brca_data),
                                 colData=brca_design)

Note that the data frame object provided as `colData` parameter should be ordered as the columns in the matrix provided as assay. For further information on this kind of objects please refer to SummarizedExperiment.

When executing a function which needs as input parameter the experimental design (such as the Wilcoxon test in `do_wilcoxon`, or `heatmap_plot`), parameter `group` may take two different objects. In case parameter `data` is a matrix, `group` should be a vector giving the class to which each sample belongs, in the same order than the data matrix. In case parameter `data` is a `SummarizedExperiment`, `group` may be either a vector as above, or the name of the column in the `colData` `dataFrame` of the `SummarizedExperiment` storing this information.

In general, functions accepting both SummarizedExperiment and matrix objects as input data and returning a data matrix object, will give as output the same kind of object received. That is, if we apply function `translate_data` to a SummarizedExperiment object, we will obtain a SummarizedExperiment, while applying the same function to a matrix object will result in a matrix object as output.

Hipathia is a free open-source software implementing the result of a research work. If you use it, please support the research project by citing:

Hidalgo, M. R., Cubuk, C., Amadoz, A., Salavert, F., Carbonell-Caballero, J., & Dopazo, J. (2017). High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget, 8(3), 5160–5178. http://doi.org/10.18632/oncotarget.14107

Hipathia accepts as input data a gene expression matrix. Expression may have been measured with any available sequencing technique. However, hipathia assumes that data has been already normalized for correcting any possible sequencing bias (which includes also batch effect correction).

The gene expression matrix must include samples as columns and genes as rows, as shown in the `brca` dataset example. Rownames must be the Entrez IDs of the genes in the rows. In order to transform other gene IDs to Entrez IDs, function `translate_data` can be used. Accepted IDs to be transformed to Entrez IDs include:

Human Mouse Rat

The parameters needed by this function are the data matrix and the species of the experiment.

    data(brca_data)
    trans_data <- translate_data(brca_data, "hsa")

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    ## translated ids = 3184 (1)
    ## untranslated ids = 3 (0.00094)
    ## multihit ids = 0 (0)

Apart from the necessary bias corrections, the expression data matrix must be scaled between 0 and 1 before computing the subpaths activation values. Function `normalize_data` is designed to this purpouse.

    exp_data <- normalize_data(trans_data)

    boxplot(trans_data)

BRCA data before scaling

    boxplot(exp_data)

BRCA data after scaling

Function `normalize_data` includes different parameters for normalization. If option `by_quantiles` is `TRUE`, a previous normalization by quantiles is performed.

    exp_data <- normalize_data(trans_data, by_quantiles = TRUE)
    boxplot(exp_data)

BRCA data after a Quantiles normalization

Other parameters of this function affect the way in which scaling to the interval [0,1] is performed. Parameter `by_gene` indicates whether to perform the scaling to [0,1] to each row of the matrix. If the option `by_gene` is set to `TRUE`, the normalization between 0 and 1 is done for each row of the matrix, meaning that the expression of each gene will have a range between 0 and 1. If it is set to `FALSE`, the normalization is done for the whole matrix, meaning that only the genes with the maximum value of the matrix will have a normalized value of 1. It is recommended to keep it set to `FALSE`, as the default value.

Parameter `percentil` indicates whether to use the percentil to compute the normalized value between 0 and 1. If it is set to `TRUE`, the function takes as a value for the position `(i,j)` of the matrix the percentil of sample `j` in the ditribution of gene `i`. If it is set to `FALSE`, the function applies a direct transformation from the original interval to [0,1]. It is recommended to keep it set to `FALSE` except for heavy-tailed distributions of the genes.

    exp_data <- normalize_data(trans_data, percentil = TRUE)
    boxplot(exp_data)

BRCA data after normalizing by percentil

Parameter `truncation_percentil` gives the value of percentil `p` from which all further values are truncated to percentil `p`. Symmetrically, values beyond percentil `1-p` are also truncated to `1-p`.

    exp_data <- normalize_data(trans_data, truncation_percentil = 0.95)
    boxplot(exp_data)

BRCA data after truncating by percentil 0.95

Hipathia aims to compute the level of activation of each subpathway in a pathway for each of the samples from the experiment. This is done by function `hipathia`, which takes as inputs the matrix of gene expression, the pathways object and some additional parameters.

In this section we will see how to load the pathways object, how the hipathia method works and how to apply function `hipathia` to the computation of the values of activation of the loaded pathways.

Hipathia package is currently implemented to use preprocessed KEGG pathways. The pathways have been processed and stored in a pathways object. This object includes all the information that the different functions in the package need. In order to load this object, use function `load_pathways` and select the species to be analyzed. Available species include human ( hsa ), mouse ( mmu ) and rat ( rno ).

    pathways <- load_pathways(species = "hsa")

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    ## Loaded 145 pathways

Parameter `pathways_list` allows the user to specify the pathways to be loaded. The different functions of the package will use all the pathways in the pathways object for its computations. In order to restrict the analysis to a particular set of pathways, load only the required pathways to the pathway object. By default, all pathways available for the specified species are loaded.

    pathways_only2 <- load_pathways(species = "hsa", pathways_list = c("hsa03320",
                                                                       "hsa04014"))

    ## Loaded 2 pathways

In order to know which pathways are included in each pathways object, function `get_pathways_list` can be used.

    length(get_pathways_list(pathways))

    ## [1] 145

    get_pathways_list(pathways)[1:10]

    ##  [1] "hsa03320" "hsa03460" "hsa04010" "hsa04012" "hsa04014" "hsa04015"
    ##  [7] "hsa04020" "hsa04022" "hsa04024" "hsa04062"

    length(get_pathways_list(pathways_only2))

    ## [1] 2

    get_pathways_list(pathways_only2)

    ## [1] "hsa03320" "hsa04014"

In order for a protein to pass the signal, there are two important factors: first, the protein must be present, and second, some other protein must activate it. Therefore, hipathia is a method to compute signal transduction based on two steps. First, it quantifies the presence of a particular gene as a normalized value between 0 and 1. Then, it computes the signal value passing through a node taking into account the level of expression of each gene inside the node and the intensity of the signal arriving to it. The signal value of the pathway is the signal value through the last node of the pathway.

Pathways are represented by directed graphs, which include different input and output nodes. The signal arrives to an initial node and is transmited along the pathway following the direction of the interactions up to an output node. Thus, the signal may follow many different paths along the pathway. Hipathia computes the intensity of this signal up to each output node of a pathway separately.

Genes in the output nodes are also called effector proteins, since they are the ones responsibles for performing the action the signal is seeking. We define the effector subpathway ending in node G as the subgraph including any node in a path leading to G. When applied to effector subpathways, hipathia returns the intensity of the signal arriving to the effector protein G.

Effector subpathways may have many different input nodes. In order to analyze in detail which of the possible paths leading to node G is responsible for the observed change, effector subpathays can be decomposed into several subpathways including only one input node. We define the decomposed subpathway from H to G as the subgraph including any node in a path from H to G.

Pathways are represented by graphs and composed by nodes and relations among them. Some nodes may contain multiple genes representing different isoforms of the protein or members of the same gene familiy, among others. Since each gene has its own level of expression, the first step of the method is to summarize this information into a score representing the expression of the node as a whole.

The computation of the signal intensity across the pathway is performed by means of an iterative algorithm beginning in the input nodes of the subpathway. In order to initialize the pathway signal we assume an incoming signal value of 1 in the input nodes of the subpathway. Then, for each node \(n\) of the network, the signal value \(S_n\) is propagated along the nodes according to the following rule \[\begin{equation} \label{formula} S_n = v_n\cdot(1-\prod_{s_i \in A_n}(1-s_i))\cdot\prod_{s_j\in I_n}(1-s_j) \end{equation}\]

where \(A_n\) is the set of signals arriving to the current node from an activation edge, \(I_n\) is the set of signals arriving to the current node from an inhibition edge, and \(v_n\) is the normalized value of expression of the current node.

Function `hipathia` computes the level of activation of the subpathways, taking as inputs the matrix of gene expression, the pathways object and some additional parameters.

Parameter `decompose` indicates whether to use effector subpathways or decomposed subpathways. Option `decompose=FALSE` uses effector subpathways while option `decompose=TRUE` uses decomposed subpathways. For further information on this, see Section @ref(sec:subs). For further information on the method used to compute the level of signal activity in the pathways, see Section @ref(sec:signal) or refer to Hidalgo et al..

    results <- hipathia(exp_data, pathways, decompose = FALSE, verbose=FALSE)

    ## Added missing genes: 144 (4.33%)

The genes which are needed by `hipathia` to compute the signal and are not present in the provided matrix are added by the function, assigning to each sample the median of the matrix. The number and percentage of added genes is shown by the function. A high level of added missing genes may indicate that the results are not representative of the actual analysis.

The resulting object is a MultiArrayExperiment object, which includes two different SummarizedExperiment objects: `paths` and `nodes`.

    results

    ## A MultiAssayExperiment object of 2 listed
    ##  experiments with user-defined names and respective classes.
    ##  Containing an ExperimentList class object of length 2:
    ##  [1] nodes: SummarizedExperiment with 6754 rows and 40 columns
    ##  [2] paths: SummarizedExperiment with 1869 rows and 40 columns
    ## Functionality:
    ##  experiments() - obtain the ExperimentList instance
    ##  colData() - the primary/phenotype DataFrame
    ##  sampleMap() - the sample coordination DataFrame
    ##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
    ##  *Format() - convert into a long or wide DataFrame
    ##  assays() - convert ExperimentList to a SimpleList of matrices
    ##  exportClass() - save data to flat files

The `paths` object includes as assay a matrix with the level of activity of the signal in each subpathway. In order to extract the object of signal activity values from this object use function `get_paths_data`. By default, this function returns a SummarizedExperiment object, but it can return just the matrix of subpaths values if parameter `matrix` is set to `TRUE`.

    path_vals <- get_paths_data(results, matrix = TRUE)
    path_vals <- get_paths_data(results)
    hhead(path_vals, 4)

    ##               TCGA.BH.A1FM.11B.23R.A13Q.07 TCGA.E2.A1LB.11A.22R.A144.07
    ## P-hsa03320-37                   0.42896593                   0.37775624
    ## P-hsa03320-61                   0.19398922                   0.19805778
    ## P-hsa03320-46                   0.09120405                   0.08985045
    ## P-hsa03320-57                   0.10329939                   0.10176627
    ##               TCGA.BH.A208.11A.51R.A157.07 TCGA.BH.A18K.11A.13R.A12D.07
    ## P-hsa03320-37                    0.4024025                   0.41396505
    ## P-hsa03320-61                    0.1655702                   0.18774026
    ## P-hsa03320-46                    0.0905164                   0.09280719
    ## P-hsa03320-57                    0.1025205                   0.13953972

Rownames of the matrix of pathway results are the IDs of the subpathways. The object `results` stores also the comprehensive names of the subpathways as `rowData` of the assay `paths`. However, in case we need to transform subpath IDs to comprehensive subpath names, we can use `get_path_names` function, see Section @ref(sec:gpn) for further information on this. However, it is not recommended to change the row names of the matrix of subpath values.

Notice that the matrix of subpathway activity values will include a value of activity for each sample and for each possible subpathway of the pathways in the pathway object. Depending on whether parameter `decompose` is set to `TRUE` or `FALSE`, and on the number of pathways included in the object of pathways given as attribute, the number of analyzed subpathways may vary. Currently hipathia includes up to the following number of pathways, effector subpathways and decomposed subpathways per species:

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| Pathways | Effector subpathways | Decomposed subpathways
--- | --- | --- | ---
hsa | 145 | 1869 | 8410
mmu | 141 | 1850 | 8410
rno | 141 | 1846 | 8221

It is recommended to perform an initial hipathia analysis with effector subpathways, and use decomposed subpathways only for specific pathways in which the user is highly interested.

Each effector protein of a pathway is responsible for performing a particular function. Thus, from the matrix of effector subpathways we can infer the functions matrix with the function `quantify_terms`, which computes an intensity value for each molecular function and for each sample.

Different effector subpathways of different pathways may end in the same effector protein, and also different effector proteins may have the same molecular function. Therefore, for a particular function \(f\), `quantify_terms` summarizes the values of all the subpathways ending in an effector protein related to \(f\) with a mean value.

Different function activity matrices can be computed depending on the functional annotation given to the effector nodes. Function `quantify_terms`, through parameter `dbannot`, accepts any annotation defined by the user and it has also two default annotations: Gene Ontology functions and Uniprot keywords. For further information on the differences between Gene Ontology and Uniprot keywords annotations please refer to this page.

    uniprot_vals <- quantify_terms(results, pathways, dbannot = "uniprot")

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    ## Quantified Uniprot terms: 142

    go_vals <- quantify_terms(results, pathways, dbannot = "GO")

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    ## Quantified GO terms: 1654

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The result of this function is a data object with the level of activity of each annotated function for each sample. As before, the returned object is a SummarizedExperiment, unless parameter `matrix` is set to `TRUE` in which case a matrix is returned.

Notice that functions annotated to genes which are not included in any effector node will be not computed.

Once the object data of desired features has been computed, either subpath values or function values, any kind of analysis may be performed on it, in the same way as if it were the matrix of gene expression. Specifically, comparison of the features across different groups of samples is one of the keys. We can perform a comparison of two groups applying the Wilcoxon test using function `do_wilcoxon`.

    data(brca_design)
    sample_group <- brca_design[colnames(path_vals),"group"]
    comp <- do_wilcoxon(path_vals, sample_group, g1 = "Tumor", g2 = "Normal")
    hhead(comp)

    ##               UP/DOWN  statistic     p.value FDRp.value
    ## P-hsa03320-37    DOWN -2.2451574 0.024467723 0.04647375
    ## P-hsa03320-61    DOWN -0.8926529 0.383413282 0.46623255
    ## P-hsa03320-46    DOWN -2.5427084 0.010314038 0.02156257
    ## P-hsa03320-57    DOWN -2.5697585 0.009483607 0.02011903
    ## P-hsa03320-64    DOWN -0.3246011 0.758350956 0.81223951

Function `get_pathways_summary` returns a summary by pathway of the results from the Wilcoxon test, summaryzing the number of significant up- or down-activated features.

    pathways_summary <- get_pathways_summary(comp, pathways)
    head(pathways_summary, 4)

    ##                                                          id_pathways
    ## Notch signaling pathway                                     hsa04330
    ## Signaling pathways regulating pluripotency of stem cells    hsa04550
    ## Fc epsilon RI signaling pathway                             hsa04664
    ## Circadian rhythm                                            hsa04710
    ##                                                          num_total_paths
    ## Notch signaling pathway                                                3
    ## Signaling pathways regulating pluripotency of stem cells              14
    ## Fc epsilon RI signaling pathway                                        6
    ## Circadian rhythm                                                       4
    ##                                                          num_significant_paths
    ## Notch signaling pathway                                                      3
    ## Signaling pathways regulating pluripotency of stem cells                    14
    ## Fc epsilon RI signaling pathway                                              6
    ## Circadian rhythm                                                             4
    ##                                                          percent_significant_paths
    ## Notch signaling pathway                                                        100
    ## Signaling pathways regulating pluripotency of stem cells                       100
    ## Fc epsilon RI signaling pathway                                                100
    ## Circadian rhythm                                                               100
    ##                                                          num_up_paths
    ## Notch signaling pathway                                             1
    ## Signaling pathways regulating pluripotency of stem cells            2
    ## Fc epsilon RI signaling pathway                                     0
    ## Circadian rhythm                                                    2
    ##                                                          percent_up_paths
    ## Notch signaling pathway                                             33.33
    ## Signaling pathways regulating pluripotency of stem cells            14.29
    ## Fc epsilon RI signaling pathway                                      0.00
    ## Circadian rhythm                                                    50.00
    ##                                                          num_down_paths
    ## Notch signaling pathway                                               2
    ## Signaling pathways regulating pluripotency of stem cells             12
    ## Fc epsilon RI signaling pathway                                       6
    ## Circadian rhythm                                                      2
    ##                                                          percent_down_paths
    ## Notch signaling pathway                                               66.67
    ## Signaling pathways regulating pluripotency of stem cells              85.71
    ## Fc epsilon RI signaling pathway                                      100.00
    ## Circadian rhythm                                                      50.00

In order to visualize the results of the comparison, see Section @ref(sec:visual).

Principal Components Analysis can be also performed by using function `do_pca`. Notice that the number of rows must not exceed the number of columns of the input matrix.

    ranked_path_vals <- path_vals[order(comp$p.value, decreasing = FALSE),]
    pca_model <- do_pca(ranked_path_vals[1:ncol(ranked_path_vals),])

PCA models can be visualized with a specific function called `pca_plot`. See Section @ref(sec:visual) for further information.

Function `heatmap_plot` plots a heatmap with the values of the given data object. This object may be a SummarizedExperiment object or a matrix. The experimental design can be provided to assign a class to each sample by means of the parameter `group`. Notice that the classes must be in the same order as the columns of the provided matrix. One can select whether to cluster samples or variables setting parameters `variable_clust` and `sample_clust` to `TRUE`.

The colors of the different classes of samples can be selected through parameter `sample_colors` with a vector of colors named after the classes. The colors inside the heatmap can be also selected with parameter `colors`. Personalized colors can be provided as a vector, or preselected color schemes classic (default), hipathia or redgreen may be chosen.

    heatmap_plot(path_vals, group = sample_group)

Heatmap plot

    heatmap_plot(uniprot_vals, group = sample_group, colors="hipathia",
              variable_clust = TRUE)

Heatmap plots with variable clustering

    heatmap_plot(go_vals, group = sample_group, colors="redgreen",
              variable_clust = TRUE)

Different colors of heatmaps: `redgreen`

Function `pca_plot` plots two components of a PCA model computed with function `do_pca` (see Section @ref(sec:pf-ana)). The experimental design can be provided to assign a class to each sample by means of the parameter `group`. Notice that the classes must be in the same order as the columns of the matrix provided to the PCA model. The colors of the different classes of samples can be selected through parameter `sample_colors` with a vector of colors named after the classes. If no such parameter is provided, a predefined set of colors will be assigned. A main title may be given to the plot through parameter `main`. The components to be plotted can be selected through parameters `cp1` and `cp2` giving integer number. If parameter `legend` is set to TRUE, the legend will be plotted.

    pca_plot(pca_model, sample_group, legend = TRUE)

PCA plot

    pca_plot(pca_model, group = rep(1:5, 8), main = "Random types",
          legend = TRUE)

PCA plot with 5 random colors

Function `multiple_pca_plot` plots \(n\) PCA components given by parameter `comps=n` as an integer vector. By default, \(n=3\). As before, the experimental design can be provided to assign a class to each sample by means of the parameter `group`. Notice that the classes must be in the same order as the columns of the matrix provided to the PCA model. The colors of the different classes of samples can be selected through parameter `sample_colors` with a vector of colors named after the classes. If no such parameter is provided, a predefined set of colors will be assigned. The cumulative explained variance can be represented by setting `plot_variance` parameter to TRUE. If parameter `legend` is set to TRUE, the legend will be plotted. A main title may be given to the plot through parameter `main`.

    multiple_pca_plot(pca_model, sample_group, cex=3, plot_variance = TRUE)

Multiple PCA plot with acumulated explained variance

The results of a comparison are sometimes difficult to summarize. An easy way to understand these results is to visualize them as an image. Function `pathway_comparison_plot` creates an image of a pathway, with the same layout from KEGG, including a color code representing the significant up- and down-activated subpathways, and, if desired, the significant up- and down-regulated nodes.

    pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320")

Pathway comparison plot without node colors

In these plots, colored edges represent significant subpathways. Edges belonging to subpathways which are significantly down-activated will be depicted in blue and those belonging to subpathways which are significantly up-activated will be depicted in red (as default). The up and down colors may be changed by the user through the parameter `colors` by giving a vector with three colors (representing down-activation, non-significance and up-activation respectively) or a color scheme (either classic or hipathia).

In order to visualize the effect of the nodes expression differences in the pathways, nodes can be colored by its differential expression. The color of each node with respect to its differential expression must be previously computed using function `node_color_per_de`. Note that this fucntion computes differential expression on the nodes, not on the genes. It uses function `eBayes` from package limma, see the package vignette for further information.

When computed, the resulting object must be provided to the `pathway_comparison_plot` function as parameter `node_colors`.

    colors_de <- node_color_per_de(results, pathways, sample_group, "Tumor",
                                "Normal")
    pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320",
                         node_colors = colors_de)

Pathway comparison plot with node colors: `classic`

    colors_de_hipathia <- node_color_per_de(results, pathways, sample_group,
                                         "Tumor", "Normal", colors = "hipathia")
    pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320",
                         node_colors = colors_de_hipathia, colors = "hipathia")

Pathway comparison plot with node colors: `hipathia`

Hipathia results can be viewed on a web browser interactivelly. In order to save the files for their visualization, use function `create_report`. To visualize the created report, use function `visualize_report`. For the interpretation of the results in this visualization, see Section @ref(sec:interpret).

The parameters to be provided to function `create_report` are the object of results, the Wilcoxon comparison, the pathways object and the path to the folder in which the files must be saved. Optionally, the colors of the nodes showing their differential expression can be also provided using an object returned by function `node_color_per_de` or a similar data structure.

    report <- create_report(comp, pathways, "save_noColors")

    ## Creating report folders...

    ## Creating pathways folder...

    ## Creating HTML index...

    report_colors <- create_report(comp, pathways, "save_colors",
                                   node_colors = colors_de)

    ## Creating report folders...

    ## Creating pathways folder...

    ## Creating HTML index...

Due to cross-origin security restrictions (CORS), a web server is needed to serve the result files correctly. The easiest way to run a web server to show the result files is with the hipathia function `visualize_report`. The user must specify the folder where the report has been stored by function `create_report`. A web server developed in R will be executed, serving the result files to the default URL http://127.0.0.1:4000. Port 4000 may be changed through parameter `port`.

    visualize_report(report_colors)

    ## Serving the directory /tmp/Rtmpvcns6I/save_colors/pathway-viewer at http://127.0.0.1:4000

    ## Open a web browser and go to URL http://127.0.0.1:4000

    visualize_report(report, port = 4001)

    ## Serving the directory /tmp/Rtmpvcns6I/save_noColors/pathway-viewer at http://127.0.0.1:4001

    ## Open a web browser and go to URL http://127.0.0.1:4001

The function `visualize_report` uses the servr package, please refer to the package documentation for further information.

The servers will be active until function `daemon_stop` from package `servr` is executed. Information about how to stop each server individually is given as an output of each `visualize_report` function. To stop all servers at a time, use

    servr::daemon_stop()

Alternatively, if you have already a web server installed in your computer, just link or move the output folder to your web server http document root and then open it on your web browser.

Effector subpathway results are shown by default grouped by the pathway to which they belong. However, if our interest is to see the comparison of all the subpathways arriving to a particular function, we can group the subpathways by Uniprot or GO functions. Moreover, if we want to see the results of all the subpathways containing a particular gene, we can group the subpathways by genes.

In order to do that, we must use the `group_by` parameter of functions `node_color_per_de` and `create_report`. Available `group_by` parameter values include: `uniprot`, to group subpathways arriving to the same Uniprot functions, `GO`, to group subpathways arriving to the same GO terms, and `genes`, to group subpathways containing each particular gene.

    colors_de_uni <- node_color_per_de(results, pathways, sample_group, "Tumor",
                                    "Normal", group_by = "uniprot")
    create_report(comp, pathways, "save_colors_uniprot",
               node_colors = colors_de_uni, group_by = "uniprot")
    visualize_report("save_colors_uniprot", port = 4002)

As before, to stop the server and free the port, use the information about how to stop each server individually in the output of each `visualize_report` function or stop all servers at a time, using

    servr::daemon_stop()

The interactive visualization of hipathia results includes three panels and a legend. The legend is on top of the page resuming the main information depicted in the images. The left panel is the pathways panel, where the currently selected pathway is shown. The layout of the pathway is similar to the layout shown in KEGG.

As before, edges belonging to significant down-activated pathways are depicted in blue, those belonging to significant up-activated subpathways are depicted in red, and those belonging to non-significant subpathways are depicted in grey. Similarly, when nodes are colored by their differential expression, down-regulated nodes are colored in blue, up-regulated nodes are colored in red and non-significant nodes are colored in white. Different shades of the colors indicate different levels of significance with respect to the p-value of the differential expression.

The selected pathway to be shown can be modified through the pathway list in the top right panel. Arrows pointing up and down to the left of the names of the pathways indicates that the pathways contain up- or down-activated subpathways, respectively. When the arrows are colored in red or blue, it means that there are significant up- or down-regulated subpathways, respectively. The pathways list can be filtered through the Filter… box, or ordered by means of the buttons in the top right part of the panel.

All computed subpathways of the currently selected pathway are listed in the subpathways list in the bottom right panel. Arrows pointing up and down by the names of the subpathways indicates that they are up- or down-activated, respectively. When the arrows are colored in red or blue, it means that they are significantly up- or down-regulated, respectively. When a subpathway is selected from the list, only the arrows and nodes belonging to this subpathway will be highlighted. Clicking again on this subpathway will deselect it.

Hipathia is able to read and analyze custom graphs from SIF files with attributes. No species restriction is applied in this case. See Section @ref(sec:spec) for further details on file specifications.

The function used for that purpouse is `mgi_from_sif`, which takes as parameter `sif.folder` the path to the folder where the pathway files are stored, and as `spe` parameter the modeled species. Optionally, the function can add the name of the functions to which the effector nodes are related to increase the readability of the output infromation. For that, the user must include as `entrez_symbol` parameter a data.frame with two columns, first column with the EntrezGene ID, second column with the gene Symbol of the included genes, and as parameter `dbannot` the functional annotation of the included genes.

    newmgi <- mgi_from_sif(system.file("extdata/SIF_ATT_example",
                                       package = "hipathia"),
                           spe = "hsa")

    ## Loading graphs...

    ## Creating MGI...

    ## Created MGI with 1 pathway(s)

Hipathia is able to read and include graphs from SIF files with attributes with the following features:

The SIF file must fulfill the following requirements:

An example of SIF file as described above is shown here (hashtags must not be included in the file):

    ##
    ##       N-hsa00-A activation   N-hsa00-C
    ##       N-hsa00-B inhibition   N-hsa00-C
    ##       N-hsa00-C activation N-hsa00-D E
    ##     N-hsa00-D E activation   N-hsa00-F

The ATT file must fulfill the following requirements:

An example of ATT file as described above is shown here (hashtags must not be included in the file):

    ##               ID label   X  Y color     shape type label.cex label.color width
    ##        N-hsa00-A     A   0 40 white rectangle gene       0.5       black    46
    ##        N-hsa00-B     B   0  0 white rectangle gene       0.5       black    46
    ##        N-hsa00-C     C  50 20 white rectangle gene       0.5       black    46
    ##      N-hsa00-D E   D E 100 20 white rectangle gene       0.5       black    46
    ##        N-hsa00-F     F 150 20 white rectangle gene       0.5       black    46
    ##      height        genesList tooltip
    ##          17              998       A
    ##          17             5530       B
    ##          17    1432,5880,842       C
    ##          17 5747,/,9047,5335     D E
    ##          17              572       F

The file including the real names of the patwhays must fulfill the following requirements:

An example of ATT file as described above is shown here (hashtags must not be included in the file):

    ##
    ##  0 New pathway

We have developed some simple functions to ease the use of data in hipathia.

Function `hhead` has been conceived as a generalization of function `head` to matrices, dataframes and SummarizedExperiment objects. It returns the values of the \(n\) first rows and columns of the matrix. In case the object is a SummarizedExperiment, it returns the values of the \(n\) first rows and columns of the (first) assay included in it. In case the object is not a matrix, dataframe or SummarizedExperiment object, it returns the result of applying function `head` to the object.

    class(brca)

    ## [1] "SummarizedExperiment"
    ## attr(,"package")
    ## [1] "SummarizedExperiment"

    hhead(brca, 4)

    ##      TCGA.BH.A1FM.11B.23R.A13Q.07 TCGA.E2.A1LB.11A.22R.A144.07
    ## 2                      10.5317320                     9.732938
    ## 8647                   -3.3266788                    -3.457515
    ## 5244                   -0.3600828                    -1.139309
    ## 1244                    2.2876961                     1.724625
    ##      TCGA.BH.A208.11A.51R.A157.07 TCGA.BH.A18K.11A.13R.A12D.07
    ## 2                       9.7958036                    10.868669
    ## 8647                   -2.5261155                    -3.584934
    ## 5244                   -0.7368491                    -1.257797
    ## 1244                    1.0217356                     1.467979

    class(assay(brca))

    ## [1] "matrix" "array"

    hhead(assay(brca), 4)

    ##      TCGA.BH.A1FM.11B.23R.A13Q.07 TCGA.E2.A1LB.11A.22R.A144.07
    ## 2                      10.5317320                     9.732938
    ## 8647                   -3.3266788                    -3.457515
    ## 5244                   -0.3600828                    -1.139309
    ## 1244                    2.2876961                     1.724625
    ##      TCGA.BH.A208.11A.51R.A157.07 TCGA.BH.A18K.11A.13R.A12D.07
    ## 2                       9.7958036                    10.868669
    ## 8647                   -2.5261155                    -3.584934
    ## 5244                   -0.7368491                    -1.257797
    ## 1244                    1.0217356                     1.467979

The results object returned by function `hipathia` includes the names of the subpathways, and they are shown in the table returned by function `do_wilcoxon`. However, in case we need to transform subpath IDs to comprehensive subpath names, we can use `get_path_names` function:

    get_path_names(pathways, c("P-hsa03320-37", "P-hsa04010-15"))

    ## [1] "PPAR signaling pathway: HMGCS2" "MAPK signaling pathway: NFKB1"