--- title: "Using Phantasus application" author: - "Daria Zenkova" - "Vladislav Kamenev" - "Maxim N. Artyomov" - "Alexey A. Sergushichev" date: "`r Sys.Date()`" output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Using phantasus application} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- This tutorial describes Phantasus -- a web-application for visual and interactive gene expression analysis. Phantasus is based on [Morpheus](https://software.broadinstitute.org/morpheus/) -- a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via [OpenCPU API](https://www.opencpu.org/). The main object in Phantasus is a gene expression matrix. It can either be uploaded from a local text or Excel file or loaded from Gene Expression Omnibus (GEO) database by the series identifier. Aside from basic visualization and filtering methods as implemented in Morpheus, R-based methods such as k-means clustering, principal component analysis, differential expression analysis with limma package are supported. In this vignette we show example usage of Phantasus for analysis of public gene expression data from GEO database. It starts from loading data, normalization and filtering outliers, to doing differential gene expression analysis and downstream analysis. # Example workfow for analysing gene expression changes in macrophage activation To illustrate the usage of Phantasus let us consider public dataset from Gene Expression Omnibus (GEO) database [GSE53986](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53986). This dataset contains data from experiments, where bone marrow derived macrophages were treated with three stimuli: LPS, IFNg and combined LPS+INFg. ## Starting application The simplest way to try Phantasus application is to go to web-site https://genome.ifmo.ru/phantasus or its mirror https://artyomovlab.wustl.edu/phantasus where the latest version is deployed. Alternatively, Phantaus can be start locally using the corresponding R package: ```{r,eval=FALSE} library(phantasus) servePhantasus() ``` This command runs the application with the default parameters, opens it in the default browser (from `browser` option) with address http://0.0.0.0:8000. The starting screen should appear: ```{r, out.width = "750px", echo = FALSE} knitr::include_graphics("images/start_screen.jpg") ``` ## Preparing the dataset for analysis **Opening the dataset** Let us open the dataset. To do this, select _GEO Datasets_ option in _Choose a file..._ dropdown menu. There, a text field will appear where `GSE53986` should be entered. Clicking the _Load_ button (or pressing _Enter_ on the keyboard) will start the loading. After a few seconds, the corresponding heatmap should appear. ```{r, out.width = "750px", echo = FALSE} knitr::include_graphics("images/dataset_loaded.jpg") ``` On the heatmap, the rows correspond to genes (or microarray probes). The rows are annotated with _Gene symbol_ and _Gene ID_ annotaions (as loaded from GEO database). Columns correspond to samples. They are annotated with titles, GEO sample accession identifiers and treatment field. The annotations, such as treatment, are loaded from user-submitted GEO annotations (they can be seen, for example, in _Charateristics_ secion at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1304836). We note that not for all of the datasets in GEO such proper annotations are supplied. **Adjusting expression values** By hovering at heatmap cell, gene expression values can be viewed. The large values there indicate that the data is not log-scaled, which is important for most types of gene expression analysis. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/huge_value.jpg") ``` For the proper further analysis it is recommended to normalize the matrix. To normalize values go to _Tools/Adjust_ menu and check _Log 2_ and _Quantile normalize_ adjustments. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/adjust_tool.jpg") ``` The new tab with adjusted values will appear. All operations that modify gene expression matrix (such as adjustment, subsetting and several others) create a new tab. This allows to revert the operation by going back to one of the previous tabs. **Removing duplicate genes** Since the dataset is obtained with a mircroarray, a single gene can be represented by several probes. This can be seen, for example, by sorting rows by _Gene symbol_ column (one click on column header), entering `Actb` in the search field and going to the first match by clicking down-arrow next to the field. There are five probes corresponding to Actb gene in the considered microarray. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/duplicates.jpg") ``` To simplify the analysis it is better to have one row per gene in the gene expression matrix. One of the easiest ways is to chose only one row that has the maximal median level of expression across all samples. Such method removes the noise of lowly-expressed probes. Go to _Tools/Collapse_ and choose _Maximum Median Probe_ as the method and _Gene ID_ as the collapse field. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/collapse_tool.jpg") ``` The result will be shown in a new tab. **Filtering lowly-expressed genes** Additionally, lowly-epxressed genes can be filtered explicitly. It helps to reduce noise and increase power of downstream analysis methods. First, we calculate mean expression of each gene using _Tools/Create Calculated Annotation_ menu. Enter `mean_expression` as the annotation name and `MEAN()` in the _Formula_ field and click _OK_. The result will appear as an additional column in row annotations. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/calculated_annotation_tool.jpg") ``` ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/calculated_annotation_loaded.jpg") ``` Now this annotation can be used to filter genes. Open _Tools/Filter_ menu. Click _Add_ to add a new filter. Choose `mean_expression` as a _Field_ for filtering. Then press _Switch to top filter_ button and input the number of genes to keep. A good choice for a typical mammalian dataset is to keep around 10--12 thousand most expressed genes. Filter is applied automatically, so after closing the dialog with _Close_ button only the genes passing the filter will be displayed. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/filter_tool.jpg") ``` After filtering 12001 genes should be shown. The number is not equal to required 12000 genes due to some of the genes having the same *mean_expression* value. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/filter_tool_result.jpg") ``` It is more convenient to extract these genes into a new tab. For this, select all genes (click on any gene and press _Ctrl+A_) and use _Tools/New Heat Map_ menu (or press _Ctrl+X_). Now you have the tab with a fully prepared dataset for the further analysis. To easily distinguish it from other tabs, you can rename it by right click on the tab and choosing _Rename_ option. Let us rename it to `GSE53986_norm`. It is also useful to save the current result to be able to return to it later. In order to save it use _File/Save Dataset_ menu. Enter an appropriate file name (e.g. `GSE53986_norm`) and press `OK`. A file in text [GCT format](https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#GCT:_Gene_Cluster_Text_file_format_.28.2A.gct.29) will be downloaded. ## Exploring the dataset **PCA Plot** One of the ways to asses quality of the dataset is to use principal component analysis (PCA) method. This can be done using _Tools/PCA Plot_ menu. ```{r, out.width = "550px", echo = FALSE} knitr::include_graphics("images/pcaplot_tool_clean.png") ``` You can customize color, size and labels of points on the chart using values from annotation. Here we set color to come from _treatment_ annotation. ```{r, out.width = "550px", echo = FALSE} knitr::include_graphics("images/pcaplot_tool_finished.png") ``` It can be seen that in this dataset the first replicates in each condition are outliers. **K-means clustering** Another useful dataset exploration tool is k-means clustering. Use _Tools/k-means_ to cluster genes into 16 clusters. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/kmeans_tool.jpg") ``` Afterwards, rows can be sorted by _clusters_ column. By using menu _View/Fit to window_ one can get a "bird's-eye view" on the dataset. Here also one can clearly see outlying samples. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/kmeans_result.jpg") ``` **Hierarchical clustering** _Tool/Hierarchical clustering_ menu can be used to cluster samples and highlight outliers (and concordance of other samples) even further. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/hierarchical_tool.jpg") ``` **Filtering outliers** Now, when outliers are confirmed and easily viewed with the dendrogram from the previous step, you can select the good samples and extract them into another heatmap (_Tools/New Heat Map_ of pressing _Ctrl+X_). ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/good_samples.jpg") ``` ## Differential gene expression **Apllying _limma_ tool** Differential gene expression analysis can be carried out with _Tool/limma_ menu. Choose _treatment_ as a _Field_, with _Untreated_ and _LPS_ as classes. Clicking _OK_ will call differential gene expression analysis method with [*limma*](https://bioconductor.org/packages/limma) R package. ```{r, out.width = "500px", echo = FALSE} knitr::include_graphics("images/limma_tool.jpg") ``` The rows can be ordered by decreasing *t*-statistic column to see which genes are the most up-regulated upon LPS treatment. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/limma_results.png") ``` **Pahway analysis with Enrichr** The results of differential gene expression can be used, for example, for pathway enrichment analysis with online tools such as [MSigDB](software.broadinstitute.org/gsea/msigdb/compute_overlaps.jsp) or [Enrichr](http://amp.pharm.mssm.edu/Enrichr/). For ease of use Enrichr is integrated into Phantasus. Open _Tools/Submit to Enrichr_ menu and select about top 200 genes up-regulated on LPS. Also select _Gene symbol_ as the column with gene symbols. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/enrichr.png") ``` Clicking _OK_ will result in a new browser tab with Enrichr being opened with results of pathway enrichment analysis. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/enrichr_result.png") ``` **Metabolic network analysis with Shiny GAM** Another analysis integrated into Phantasus is metabolic network analysis with [Shiny GAM](https://artyomovlab.wustl.edu/shiny/gam/). After differential expression you can submit the table with _limma_ results using _Tools/Sumbit to Shiny GAM_ menu. ```{r, out.width = "300px", echo = FALSE} knitr::include_graphics("images/shiny_gam.jpg") ``` After successful submission, a new browser tab will be opened with _Shiny GAM_ interface and the submitted data. ```{r, out.width = "600px", echo = FALSE} knitr::include_graphics("images/shiny_gam_result.jpg") ``` # Advanced usage ## Options for `servePhantasus` You can customise serving of the application by specifying following parameters: - `host` and `port` (by default: `"0.0.0.0"` and `8000`); - `cacheDir` (by default: `tempdir()`) -- directory where downloaded datasets will be saved and reused in later sessions; - `preloadedDir` (by default: `NULL`) -- directory with `ExpressionSet` objects encoded in rda-files, that can be quickly loaded to application by name (see section \@ref(preloaded-datasets)); - `openInBrowser` (by default `TRUE`). ## Loading dataset options There are three ways to upload a dataset into application: - As a file from: - computer; - URL; - Dropbox. - By GEO identifier: - with _Open file_ interface; - with adding parameter `geo` to the link (_e.g._, http://localhost:8000/?geo=GSE27112). - By an identifier of a dataset saved on the server (see section \@ref(preloaded-datasets)) - with _Saved on server datasets_ - with adding parameter `preloaded` to the link (_e.g._, http://localhost:8000/?preloaded=fileName) You can either open the dataset from the main page, or if you are already looking at some datasets and don't want to lose your progress you can use _File/Open_ (_Ctrl+O_), choose _Open dataset in new tab_ and then select the open option. ```{r, out.width="500px", echo = FALSE} knitr::include_graphics("images/open_file_tool.jpg") ``` ## Preloaded datasets Preloaded datasets is a feature that allows quick access to frequently-accessed datasets or to share them inside the research group. To store dataset on a server, on nead to save list `ess` of `ExpressionSet` objects into an RData file with `.rda` extension into a directory as specified in `servePhantasus`. Let us preprocess and save `GSE14308` dataset: ```{r, message=FALSE, cache=TRUE} library(GEOquery) library(limma) gse14308 <- getGEO("GSE14308", AnnotGPL = TRUE)[[1]] gse14308$condition <- sub("-.*$", "", gse14308$title) pData(gse14308) <- pData(gse14308)[, c("title", "geo_accession", "condition")] gse14308 <- gse14308[, order(gse14308$condition)] fData(gse14308) <- fData(gse14308)[, c("Gene ID", "Gene symbol")] exprs(gse14308) <- normalizeBetweenArrays(log2(exprs(gse14308)+1), method="quantile") ess <- list(GSE14308_norm=gse14308) preloadedDir <- tempdir() save(ess, file=file.path(preloadedDir, "GSE14308_norm.rda")) ``` Next we can serve Phantasus with set `preloadedDir` option: ```{r, eval=F} servePhantasus(preloadedDir=preloadedDir) ``` There you can either put `GSE14308_norm` name when using open option _Saved on server datasets_ or just open by specifying the name in URL: http://localhost:8000/?preloaded=GSE14308_norm. ```{r, out.width="650px", echo = FALSE} knitr::include_graphics("images/gse14308_norm.png") ``` ## Support for RNA-seq datasets Phantasus supports loading RNA-seq datasets from GEO using gene expression counts as computed by [ARCHS4 project](http://amp.pharm.mssm.edu/archs4/index.html). To make it work one need to download gene level expression from the [Download](http://amp.pharm.mssm.edu/archs4/download.html) section. The downloaded files `human_matrix.h5` and `mouse_matrix.h5` should be placed into Phantasus cache folder. # Feedback You can see known issues and submit yours at GitHub: (https://github.com/ctlab/phantasus/issues) # Acknowledgments The authors are very thankful to Joshua Gould for developing Morpheus.