--- title: "*padma* package: Quick-start guide" author: - name: Andrea Rau affiliation: INRAE email: andrea.rau@inrae.fr package: padma date: "`r Sys.Date()`" output: BiocStyle::html_document: toc_float: true BiocStyle::pdf_document: default vignette: > %\VignetteIndexEntry{padma package:Quick-start guide} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ```{r, fig.align = "center", out.width = "50%", echo=FALSE} knitr::include_graphics("hex_padma_v2.png") ``` *padma* is a package that uses multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). In particular, *padma* characterizes individuals with aberrant multi-omic profiles for a given pathway of interest and quantifies this deviation with respect to the sampled population using a multi-omic consensus representation. Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. The method implemented in this package is described in greater detail in the following pre-print: > Rau, A., Manansala, R., Flister, M. J., Rui, H., Jaffrézic, F., Laloë, D.*, and Auer, P. L.* (2019) Individualized multi-omic pathway deviation scores using multiple factor analysis bioRxiv, https://doi.org/10.1101/827022. *These authors contributed equally to this work. Below, we provide a quick-start guide using a subset of the TCGA LUAD multi-omic data (included with the *padma* package) to illustrate the functionalities and output of the method. # Quick start (tl;dr) As with any R package, after installation the *padma* package is loaded as follows: ```{r, message = FALSE} library(padma) ``` Multi-omic data must be formatted as either a `MultiAssayExperiment` object (preferred) or as a named list of `matrix` objects of matched multi-omic data from a set of individuals, which is internally converted into a `MultiAssayExperiment` object. A small example dataset in list format is provided in `LUAD_subset` for multi-omic data (clinical, RNA-seq, CNA, methylation, miRNA-seq) on 13 genes from the D4-GDI signaling pathway in individuals with lung adenocarcinoma from TCGA. In practice, multi-omic data do not need to be subsetted prior to running *padma*; this reduced dataset is simply used as a minimal illustrative example. A standard *padma* analysis for the D4-GDI signalling pathway takes as input these pre-formatted `MultiAssayExperiment` data (called \code{mae} below) and the name of the desired pathway. Note that data should be normalized, batch-corrected, and if appropriate, log-transformed prior to using *padma*. Built-in pathway names and gene lists from the MSigDB curated pathway set are provided in the `msigdb` data provided with *padma*. ```{r, eval = FALSE} run_padma <- padma(mae, pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY") ``` Individualized pathway deviation scores can then be accessed via the `assay` accessor function, and per-gene contributions to these scores can be accessed via the `gene_deviation_scores()` accessor. In addition, a variety of plots can be obtained, including a factor map or partial factor map of individuals, and a plot of the percentage contribution to each component by each considered omics. ```{r, eval = FALSE} assay(run_padma) factorMap(run_padma) factorMap(run_padma, partial_id = "TCGA-78-7536") omicsContrib(run_padma) ``` Additional numerical outputs included in the `run_padma` S4 object of class `padmaResults` include the per-gene pathway deviation scores and full results from the MFA analysis. See the following section for a more detailed description of these outputs. # Description of *padma* A portion of the following text is taken from [Rau et al. (2019)](https://doi.org/10.1101/827022). ## Overview of Multiple Factor Analysis Multiple factor analysis (MFA) represents an extension of principal component analysis for the case where multiple quantitative data tables are to be simultaneously analyzed. In particular, the MFA approach weights each table individually to ensure that tables with more features or those on a different scale do not dominate the analysis; all features within a given table are given the same weight. These weights are chosen such that the first eigenvalue of a PCA performed on each weighted table is equal to 1, ensuring that all tables play an equal role in the global multi-table analysis. According to the desired focus of the analysis, data can be structured either with molecular assays (e.g., RNA-seq, methylation, miRNA-seq, copy number alterations) as tables (and genes as features within omics), or with genes as tables (and molecular assays as features within genes). The MFA weights balance the contributions of each omic or of each gene, respectively. In *padma*, we focus on the latter strategy in order to allow different omics to contribute to a varying degree depending on the chosen pathway. ![Figure 1 from [Rau et al. (2019)](https://doi.org/10.1101/827022), illustrating the *padma* approach.](padma_illustration.png) More precisely, consider a pathway or gene set composed of $p$ genes (Figure 1A), each of which is measured using up to $k$ molecular assays (e.g., RNA-seq, methylation, miRNA-seq, copy number alterations), contained in the set of gene-specific matrices $X_1,\ldots, X_p$ that have the same $n$ matched individuals (rows) and $j_1,\ldots, j_p$ potentially unmatched variables (columns) in each, where $j_g \in \lbrace 1, \ldots, k\rbrace$ for each gene $g = 1,\ldots, p$. Because only the observations and not the variables are matched across data tables, genes may be represented by potentially different subset of omics data (e.g., only expression data for one gene, and expression and methylation data for another). In the first step, these data tables are generally standardized (i.e., centered and scaled). Next, an individual PCA is performed using singular value decomposition for each gene table $X_g$, and its largest singular value $\lambda_g^1$ is calculated (Figure 1B). Then, all features in each gene table $X_g$ are weighted by $1/\lambda_g^1$, and a global PCA is performed using a singular value decomposition on the concatenated set of weighted standardized tables, $X^\ast = \left[ \frac{X_1}{\lambda_1^1}, \ldots, \frac{X_p}{\lambda_p^1}\right]$ (Figure 1C). This yields a matrix of components (i.e., latent variables) in the observation and variable space. The MFA thus provides a consensus across-gene representation of the individuals for a given pathway, and the global PCA performed on the weighted gene tables decomposes the consensus variance into orthogonal variables (i.e., principal components) that are ordered by the proportion of variance explained by each. The coordinates of each individual on these components, also referred to as factor scores, can be used to produce factor maps to represent individuals in this consensus space such that smaller distances reflect greater similarities among individuals. In addition, partial factor scores, which represent the position of individuals in the consensus for a given gene, can also be represented in the consensus factor map; the average of partial factor scores across all dimensions and genes for a given individual corresponds to the factor score (Figure 1D). ## Calculation of individualized pathway deviation scores In the consensus space obtained from the MFA, the origin represents the ``average" pathway behavior across genes, omics, and individuals; individuals that are projected to increasingly distant points in the factor map represent those with increasingly aberrant values, with respect to this average, for one or more of the omics measures for one or more genes in the pathway. To quantify these aberrant individuals, we propose an individualized pathway deviation score $d_i$ based on the multidimensional Euclidean distance of the MFA component loadings for each individual to the origin: \begin{equation*} d_i^2 = \sum_{\ell = 1}^L f_{i,\ell}^2, \end{equation*} where $f_{i,\ell}$ corresponds to the MFA factor score of individual $i$ in component $\ell$, and $L$ corresponds to the rank of $X^\ast$. Note that this corresponds to the weighted Euclidean distance of the scaled multi-omic data (for the genes in a given pathway) of each individual to the origin. These individualized pathway deviation scores are thus nonnegative, where smaller values represent individuals for whom the average multi-omic pathway variation is close to the average, while larger scores represent individuals with increasingly aberrant multi-omic pathway variation with respect to the average. An individual with a large pathway deviation score is thus characterized by one or more genes, with one or more omic measures, that explain a large proportion of the global correlated information across the full pathway. ## Calculation of individualized per-gene deviation scores In order to quantify the role played by each gene for each individual, we decompose the individualized pathway deviation scores into gene-level contributions. Recall that the average of partial factor scores across all MFA dimensions corresponds to each individual’s factor score. We define the gene-level deviation for a given individual as follows: \begin{equation*} d_{i,g}=\frac{\sum_{\ell = 1}^L f_{i,\ell}\left(f_{i,\ell,g}-f_{i,\ell}\right)} {\sum_{\ell = 1}^L f_{i,\ell}^2}, \end{equation*} where as before $f_{i,\ell}$ corresponds to the MFA factor score of individual $i$ in component $\ell$, $L$ corresponds to the rank of $X^\ast$, and $f_{i,\ell,g}$ corresponds to the MFA partial factor score of individual $i$ in gene $g$ in component $\ell$. Note that by construction, the contributions of all pathway genes to the overall deviation score sum to 0. In particular, per-gene contributions can take on both negative and positive values according to the extent to which the gene influences the deviation of the overall pathway score from the origin (i.e., the global center of gravity across individuals); large positive values correspond to tables with a large influence on the overall deviation of an individual, while large negative values correspond to genes that tend to be most similar to the global average. # Description of built-in datasets ## `LUAD_subset`: D4-GDI signaling pathway in the TCGA-LUAD multi-omic data In this vignette, we focus on the example of multi-omic (RNA-seq, methylation, copy number alterations, and miRNA-seq) data for the D4-GDI signaling pathway from individuals with lung adenocarcinoma (LUAD) from The Cancer Genome Atlas (TCGA) database. The multi-omic TCGA data were downloaded and processed as [previously described](https://doi.org/10.5281/zenodo.3524080), including batch correction for the plate effect. The total number of individuals considered here is $n=144$. See [Rau et al. (2019)](https://doi.org/10.1101/827022) for additional details about data processing and mapping of miRNAs to genes. The D4-GDP dissociation inhibitor (GDI) signaling pathway is made up of 13 genes; it was chosen for follow-up here as it was identified as having individualized pathway deviation scores that are significantly positively correlated with smaller progression-free intervals. RNA-seq, methylation, and CNA measures are available for all 13 genes, with the exception of CYCS and PARP1, for which no methylation probes were measured the promoter region. In addition, miRNA-seq data were included for one predicted target pair: hsa-mir-421 $\rightarrow$ CASP3. The available multi-omic data for the D4-GDI signaling pathway in TCGA LUAD patients is included within the *padma* package as the `LUAD_subset` object, which is a named list of `data.frames` each containing the relevant data (clinical parameters, RNA-seq, methylation, miRNA-seq, CNA) for the $p=13$ genes of the D4-GDI pathway in the $n=144$ individuals. ```{r, explore_LUAD_subset} names(LUAD_subset) lapply(LUAD_subset, class) lapply(LUAD_subset, dim) ``` There are some important data formatting issues that are worth mentioning here: - The clinical data (`LUAD_subset$clinical`) have individuals as rows and clinical variables as columns; all other datasets contain assay data with biological entities (e.g., genes, miRNAs) as rows and individuals as columns, with appropriate row and column names. Sample (column) names for assay data correspond to the patient barcodes that are provided in the `bcr_patient_barcode` column of `LUAD_subset$clinical`. For `LUAD_subset$mirna`, the first two columns represent the miRNA ID (`miRNA_lc`) and corresponding targeted gene symbol (`gene`). - A `gene_map` data.frame can be optionally included to map biological entities (e.g. miRNAs) to gene symbols as needed. By default, a set of 10,754 predicted miRNA gene targets with "Functional MTI" support type from miRTarBase (version 7.0) are used for the `gene_map`. - All omics data have already been appropriately pre-processed before subsetting to this pathway (e.g., RNA-seq and miRNA-seq data have been normalized and log-transformed, all datasets were batch-corrected for the plate effect). - Although *padma* does require that all datasets have the same number of individuals, and that all datasets are sorted so that individuals are in the same order, each dataset does not need to represent every considered gene. Note that in these data, a single gene (CASP3) is predicted to be targeted by a miRNA (hsa-mir-421). - Finally, these toy data have been subsetted to include only values relevant to the D4-GDI signaling pathway solely in the interest of space. In practice, users do not need to subset their data prior to input in *padma* or match miRNAs to genes, as both of these steps are performed automatically for a ser-provided pathway name or set of genes. ## `msigdb`: the MSigDB canonical pathway gene sets In our work, we considered the pathways included in the MSigDB canonical pathways curated gene set catalog, which includes genes whose products are involved in metabolic and signaling pathways reported in curated public databases. We specifically used the 1322 “C2 curated gene sets” catalog from 601MSigDB v5.2 available at http://bioinf.wehi.edu.au/software/MSigDB/as described in the [*limma*](https://bioconductor.org/packages/release/bioc/html/limma.html) Bioconductor package. For convenience, the gene sets corresponding to each pathway have been included in the `msigdb` data frame: ```{r, msigdb} head(msigdb) ``` In practice, to use *padma* a user can either directly provide a vector of gene symbols, or the geneset name as defined in this data frame (e.g., `c2_cp_BIOCARTA_41BB_PATHWAY`). ## `mirtarbase`: predicted gene targets of miRNAs *padma* integrates multi-omic data by mapping omics measures to genes in a given pathway. Although this assignment of values to genes is relatively straightforward for RNA-seq, CNA, and methylation data, a definitive mapping of miRNA-to-gene relationships does not exist, as miRNAs can each potentially target multiple genes. Many methods and databases based on text-mining or bioinformatics-driven approaches exist to predict miRNA-target pairs. Here, we make use of the curated miR-target interaction (MTI) predictions in [miRTarBase](http://mirtarbase.mbc.nctu.edu.tw/php/index.php) (version 7.0), using only exact matches for miRNA IDs and target gene symbols and predictions with the “Functional MTI” support type. As a reference, we have included these predictions in the built-in `mirtarbase` data frame: ```{r} head(mirtarbase) ``` In practice, unless an alternative data frame is provided for the `gene_map` argument of *padma*, the `mirtarbase` data frame is used to match miRNA IDs to gene symbols. # Running *padma* To run *padma* for the D4-GDI pathway, we start by formatting the `LUAD_subset` data as a `MultiAssayExperiment` object. ```{r} library(MultiAssayExperiment) LUAD_subset <- padma::LUAD_subset omics_data <- list(rnaseq = as.matrix(LUAD_subset$rnaseq), methyl = as.matrix(LUAD_subset$methyl), mirna = as.matrix(LUAD_subset$mirna), cna = as.matrix(LUAD_subset$cna)) pheno_data <- data.frame(LUAD_subset$clinical, row.names = LUAD_subset$clinical$bcr_patient_barcode) mae <- suppressMessages( MultiAssayExperiment::MultiAssayExperiment( experiments = omics_data, colData = pheno_data)) ``` Next, the following command is used: ```{r, runpadma} D4GDI <- msigdb[grep("D4GDI", msigdb$geneset), "geneset"] run_padma <- padma(mae, pathway_name = D4GDI, verbose = FALSE) ``` Alternatively, *padma* could be run directly on the list of matrices contained in `LUAD_subset` (ensuring that the clinical data are included as `colData`): ```{r, runpadmalist} clinical_data <- data.frame(LUAD_subset$clinical) rownames(clinical_data) <- clinical_data$bcr_patient_barcode run_padma_list <- padma(omics_data, colData = clinical_data, pathway_name = D4GDI, verbose = FALSE) ``` Note that we have used the `misgdb` built-in database to find the full name of the D4-GDI pathway, `"c2_cp_BIOCARTA_D4GDI_PATHWAY"`. Alternatively, a vector of gene symbols for the pathway of interest could instead be provided to the `pathway_name` argument: ```{r, runpadma2} D4GDI_genes <- unlist(strsplit( msigdb[grep("D4GDI", msigdb$geneset), "symbol"], ", ")) D4GDI_genes run_padma_again <- padma(mae, pathway_name = D4GDI_genes, verbose = FALSE) ``` As noted above, it is required to provide either a recognized pathway name (see `msigdb$geneset`) or a vector of gene symbols. In most cases, users would provide the full multi-omics data in the `omics_data` argument of `padma`, rather than a pre-subsetted dataset as is the case for the built-in `LUAD_subset` data. In the following, we detail the various numerical and graphical outputs that an be accessed after running `padma`; note that the results are an S4 object of class `"padmaResults"`, an extension of the `RangedSummarizedExperiment` class. ## Numerical outputs The numerical outputs of `padma` include the following (which can all be accessed using the matching accessor functions): There are two primary numerical outputs from `padma`: - **pathway deviation scores**: accessed via `assay(...)`, a `data.frame` providing the individualized pathway deviation scores for each individual. The `group` column indicates whether an individual was included as part of the reference population (`"Base"`) or as a supplementary individual projected onto the reference (`"Supp"`), as indicated by the user in the `base_ids` and `supp_ids` arguments of `padma`. - **pathway gene-specific deviation scores**: accessed via `pathway_gene_deviation(...)`, a matrix of dimension $n$ (nuumber of individuals) x $p$ (number of genes in the pathway) containing the individualized per-gene deviation scores There are also several outputs related to the MFA itself, and obtained via the [*FactorMineR*](http://factominer.free.fr/) R package implementation of the MFA: - `MFA_results`: accessed via the `MFA_results(...)`, a named list containing detailed MFA output. When `padma` is run with `full_results = TRUE`, these include eigenvalues (`eig`), the percent contributions of each individual, gene, and omic to the MFA components (`ind_contrib_MFA`, `gene_contrib_MFA`, `omics_contrib_MFA`), the formatted gene tables used as input for the MFA (`gene_tables`), the pairwise Lg coefficients between genes (`gene_Lf_MFA`), and the full *FactoMineR* MFA output (`total_MFA`), which is an object of class `"MFA"`. See the *FactoMineR* [reference manual](https://cran.r-project.org/web/packages/FactoMineR/index.html) for a full description of this full MFA output. When `padma` is run with `full_results = FALSE`, these include eigenvalues (`eig`), and a summary of the cumulative contributions of each individual, gene, and omic to the first ten and full set of MFA components (`ind_contrib_MFA_summary`, `gene_contrib_MFA_summary`, `omics_contrib_MFA_summary`). In both cases, `eig` is a matrix of dimension $c$ (number of MFA components) x 3 providing the respective eigenvalues, percentage of variance explained, and cumulative percentage of variance explained by each MFA component. Finally, there are additional outputs related to the pathway itself that can be accessed via appropriate accessor functions: - `pathway_name`: accessed via `pathway_name(...)`, the user-provided name of pathway analyzed by `padma`. If a vector of gene symbols was provided rather than a built-in name, `"custom"` is returned. - `ngenes`: accessed via `ngenes(...)`, number of genes with data available in the pathway - `imputed_genes`: accessed via `imputed_genes(...)`, names of entities for which values were automatically imputed by `padma` - `removed_genes`: accessed via `removed_genes(...)`, names of entities which were automatically removed by `padma` due to small variance (< 10e-5) or all missing values across samples. ## Graphical outputs *padma* includes three primary plotting functions, all of which can be produced using *ggplot2* (default) or base graphics (using argument `ggplot = FALSE`). Note that when *ggplot2* is used, *ggrepel* is additionally used to better position sample labels. - `factorMap`: plot the MFA components for each individual for a selected pair of components. This would typically be done for the first few components, and serves as a visualization of the structuring of indviduals for the pathway; for example, below the factor map suggests that individual TCGA-78-7536 is likely to have a larger overall pathway deviation score given that it is positioned quite far from the origin (for these two axes). ```{r, factorMAP} factorMap(run_padma, dim_x = 1, dim_y = 2) ``` The base version of this plot may be produced as follows. ```{r, factorMAP2} factorMap(run_padma, dim_x = 1, dim_y = 2, ggplot = FALSE) ``` - `factorMap` (partial mode): plot the MFA partial components for one specific individual for a pair of components. This function would be used to zero in on one or several indivduals (for example, TCGA-78-7536) to visualize which genes appear to be pulling the overall score away from the origin. Here we note the seeming large influence of CASP1 and CASP3. Note that the individual's overall pathway score, which is represented as the large black point, is the same as that from the previous plot. ```{r, factorMappartial} factorMap(run_padma, partial_id = "TCGA-78-7536", dim_x = 1, dim_y = 2) ``` As before, *ggplot2* plotting can be optionally turned off, if desired. ```{r, factorMappartial2} factorMap(run_padma, partial_id = "TCGA-78-7536", dim_x = 1, dim_y = 2, ggplot = FALSE) ``` - `omicsContrib`: visualize the contribution of each omics to the variation of a subset of MFA components, and summarize this contribution as a weighted overall average. In this plot, transparency of the bars reflects the percentage variance explained for each component. Note that if *ggplot2* is used, the *cowplot* package is used to combine graphics. ```{r, omicscontrib} omicsContrib(run_padma, max_dim = 10) ``` Once again, *ggplot2* plotting can be turned off if desired. ```{r, omicscontrib2} omicsContrib(run_padma, max_dim = 10, ggplot = FALSE) ``` ## Additional options ### Projecting supplementary individuals onto a reference consensus Unless otherwise specified, *padma* will make use of all individuals to calculate pathway deviation scores. However, in some cases users may wish to perform the MFA on a set of reference individuals and subsequently project a set a supplementary individuals onto the reference consensus (e.g., in a case where healthy individuals are to be used as a reference, and pathway deviation scores of diseased individuals are to be calculated relative to them). In such a case, the `base_ids` and `supp_ids` arguments of `padma` can be used to provide the sampel names for individuals in each group, respectively. For instance, if the first ten individuals are in the reference group, and the user wishes to project individuals 15 to 20 as supplementary, the following code could be used: ```{r} run_padma_supp <- padma(mae, pathway_name = D4GDI, verbose = FALSE, base_ids = sampleMap(mae)$primary[1:10], supp_ids = sampleMap(mae)$primary[15:20]) ``` Note that there must not be any overlap in the indices provided for `base_ids` and `supp_ids`. ### Missing data imputation When missing values are included in the `omics_data` argument, by default *padma* uses mean imputation to replace these values prior to calculating individualized pathway scores. Alternatively, these values can be imputed via a preliminary MFA as implemented in the [*missMDA*}(https://cran.r-project.org/web/packages/missMDA/index.html) package, which can in some cases be slightly more time consuming. To do, the `impute` argument must be activated: ```{r} run_padma_impute <- padma(mae, pathway_name = D4GDI, impute = TRUE, verbose = FALSE) ``` ### Providing miRNA target predictions As noted above, if miRNA-seq data (or other data in which biological entities are not genes) are included in the `omics_data` argument, a mapping to genes must be provided to *padma* in the `gene_map` argument. By default, the mapping in `mirtarbase` taken from [miRTarBase](http://mirtarbase.mbc.nctu.edu.tw/php/index.php) is used within *padma*. If another mapping is desired by the user, this should be provided as a 2-column data frame (entity name and corresponding gene) in the `gene_map` argument. ### Requesting concise results from *padma* Because *padma* stores the full set of MFA numerical results above (including factor components and partial factor components for all individuals), the full output can become quite large if run for very large pathways and/or looped over a large number of pathways. By default, *padma* returns this full set of results, but users can request a space-saving concise output via the `full_results` argument: ```{r, eval = FALSE} run_padma_concise <- padma(mae, pathway_name = D4GDI, full_results = FALSE) ``` # FAQ **1. Can *padma* be used with non-continuous data, like binary per-gene** **somatic mutation calls?** At the current time, *padma* is only formatted to correctly handle continuous data. However, the MFA approach (as implemented in the [*FactoMineR*](http://factominer.free.fr/) package, which is called by *padma*) is technically able to handle continuous, categorical, and binary data, so this is merely a question of extending the existing *padma* code to handle more general situations. **2. Can *padma* integrate omics data that cannot be summarized to the ** **gene-level? (e.g., SNP genotyping or metabolic data)** As it is currently written, *padma* is intended to provide inference at the pathway-level, where members of each pathway correspond to genes. In theory the MFA could be performed using each omic as a table (rather than each gene), in which case no gene-level summarization would be required. *padma* is not currently equipped to handle this more general setting. # To-do list and ideas for work in progress The following items are on my to-do list for improvements: - Incorporation of known hierarchical structure among genes in a given pathway - Addition of interactivity for exploration of *padma* results - Extensions for highly structured groups of individuals - Extension of *padma* to cases where non-continuous data (e.g., binary or categorical) can be handled - Add in the possibility to remove certain MFA components (e.g., those correlated with a batch effect) - Add in the possibility to quantify contribution of a given grouping (e.g., omics) across all MFA components - Add in a full working example using [*curatedTCGAData*](https://bioconductor.org/packages/curatedTCGAData/). # Session Info ```{r} sessionInfo() ```