--- title: "Visualization of imaging cytometry data in R" date: "`r BiocStyle::doc_date()`" package: "`r BiocStyle::pkg_ver('cytomapper')`" author: - name: Nils Eling affiliation: Department for Quantitative Biomedicine, University of Zurich email: nils.eling@dqbm.uzh.ch - name: Nicolas Damond affiliation: Department for Quantitative Biomedicine, University of Zurich email: nicolas.damond@dqbm.uzh.ch output: BiocStyle::html_document: toc_float: yes bibliography: library.bib abstract: | Highly multiplexed imaging cytometry acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualized across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualized on segmented cell areas. This package contains functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging cytometry. The main functions of this package allow 1. the visualization of pixel-level information across multiple channels and 2. the display of cell-level information (expression and/or metadata) on segmentation masks. vignette: | %\VignetteIndexEntry{"Visualization of imaging cytometry data in R} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo=FALSE, results="hide"} knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE, fig.retina = 0.75, crop = NULL) library(BiocStyle) ``` ```{r library, echo=FALSE} library(cytomapper) ``` # Introduction This vignette gives an introduction to displaying highly-multiplexed imaging cytometry data with the `cytomapper` package. As an example, these instructions display imaging mass cytometry (IMC) data. However, other imaging cytometry approaches including multiplexed ion beam imaging (MIBI) [@angelo2014mibi], tissue-based cyclic immunofluorescence (t-CyCIF) [@lin2018cycif] and iterative indirect immunofluorescence imaging (4i) [@gut20184i], which produce pixel-level intensities and optionally segmentation masks can be displayed using `cytomapper`. IMC [@giesen2014imc] is a multiplexed imaging cytometry approach to measure spatial protein abundance. In IMC, tissue sections are stained with a mix of around 40 metal-conjugated antibodies prior to laser ablation with $1\mu{}m$ resolution. The ablated material is transferred to a mass cytometer for time-of-flight detection of the metal ions [@giesen2014imc][@mavropoulosimc]. In that way, hundreds of images (usually with an image size of around 1mm x 1mm) can be generated in a reasonable amount of time [@damond2019pancreas]. Raw IMC data are computationally processed using a segmentation pipeline (available at [https://github.com/BodenmillerGroup/ImcSegmentationPipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline)). This pipeline produces image stacks containing the raw pixel values for up to 40 channels, segmentation masks containing the segmented cells, cell-level expression and metadata information as well as a number of image-level meta information. Cell-level expression and metadata can be processed and read into a `SingleCellExperiment` class object. For more information on the `SingleCellExperiment` object and how to create it, please see the `r Biocpkg("SingleCellExperiment")` package and the [Orchestrating Single-Cell Analysis with Bioconductor](https://osca.bioconductor.org/data-infrastructure.html#the-singlecellexperiment-class) workflow. The `cytomapper` package provides a new `CytoImageList` class as a container for multiplexed images or segmentation masks. For more information on this class, refer to the [CytoImageList](#CytoImageList) section. The main functions of this package include `plotCells` and `plotPixels`. The `plotCells` function requires the following object inputs to display cell-level information (expression and metadata): 1. a `SingleCellExperiment` object, which contains the cells' counts and metadata 2. a `CytoImageList` object containing the segmentation masks The `plotPixels` function requires the following object inputs to display pixel-level expression information: 1. a `CytoImageList` object containing the pixel-level information per channel 2. (optionally) a `SingleCellExperiment` object, which contains the cells' counts and metadata 3. (optionally) a `CytoImageList` object containing the segmentation masks # Quick start {#QuickStart} The following section provides a quick example highlighting the functionality of `cytomapper`. For detailed information on reading in the data, refer to the [Reading in data](#ReadingData) section. More information on the required data format is provided in the [Data formats](#DataFormats) section. In the first step, we will read in the provided [toy dataset](#ToyData) ```{r quickstart-load-data} data(pancreasSCE) data(pancreasImages) data(pancreasMasks) ``` The `CytoImageList` object containing pixel-level intensities representing the ion counts for five proteins can be displayed using the `plotPixels` function: ```{r quickstart-plotPixels} plotPixels(image = pancreasImages, colour_by = c("H3", "CD99", "CDH")) ``` For more details on image normalization, cell outlining, and other pixel-level manipulations, refer to the [Plotting pixel information](#PixelInfo) section. The `CytoImageList` object containing segmentation masks, which represent cell areas on the image can be displayed using the `plotCells` function. Only the segmentation masks are plotted when no other parameters are specified. To colour and/or outline segmentation masks, a `SingleCellExperiment`, an `img_id` and `cell_id` entry need to be specified: ```{r quickstart-plotCells-2} plotCells(mask = pancreasMasks, object = pancreasSCE, cell_id = "CellNb", img_id = "ImageNb", colour_by = "CD99", outline_by = "CellType") plotCells(mask = pancreasMasks, object = pancreasSCE, cell_id = "CellNb", img_id = "ImageNb", colour_by = "CellType") ``` For more information on the data formats and requirements, refer to the following section. More details on the `plotCells` function are provided in the [Plotting cell information](#CellInfo) section. # Data formats {#DataFormats} The `cytomapper` package combines objects of the `r Biocpkg("SingleCellExperiment")` class and the `CytoImageList` class (provided in `cytomapper`) to visualize cell- and pixel-level information. In the main functions of the package, `image` refers to a `CytoImageList` object containing one or multiple multi-channel images where each channel represents the pixel-intensity of one selected marker (proteins in the case of IMC). The entry `mask` refers to a `CytoImageList` object containing one or multiple segmentation masks. Segmentation masks are defined as one-channel images containing integer values, which represent the cells' ids or 0 (background). Finally, the `object` entry refers to a `SingleCellExperiment` class object that contains cell-specific expression values (in the `assay` slots) and cell-specific metadata in the `colData` slot. To link information between the `SingleCellExperiment` and `CytoImageList` objects, two slots need to be specified: * `img_id`: a single character indicating the `colData` (in the `SingleCellExperiment` object) and `elementMetadata` (in the `CytoImageList` object) entry that contains the image identifiers. These image ids have to match between the `SingleCellExperiment` object and the `CytoImageList` object. * `cell_id`: a single character indicating the `colData` entry that contains the cell identifiers. These should be integer values corresponding to pixel-values in the segmentation masks. The `img_id` and `cell_id` entry in the `SingleCellExperiment` object need to be accessible via: ```{r image-cell-id-sce} head(colData(pancreasSCE)[,"ImageNb"]) head(colData(pancreasSCE)[,"CellNb"]) ``` The `img_id` entry in the `CytoImageList` object need to be accessible via: ```{r image-cell-id-cil} mcols(pancreasImages)[,"ImageNb"] mcols(pancreasMasks)[,"ImageNb"] ``` For more information on the `CytoImageList` class, please refer to the section [The CytoImageList object](#CytoImageList). For more information on the `SingleCellExperiment` object and how to create it, please see the `r Biocpkg("SingleCellExperiment")` package and the [Orchestrating Single-Cell Analysis with Bioconductor](https://osca.bioconductor.org/data-infrastructure.html#the-singlecellexperiment-class) workflow. ## The provided toy dataset {#ToyData} For visualization purposes, the `cytomapper` package provides a toy dataset containing 3 images of $100\mu{m}$ x $100\mu{m}$ dimensions (100 x 100 pixels). The dataset contains 362 segmented cells and the expression values for 5 proteins: H3, CD99, PIN, CD8a, and CDH It represents a small subset of the data presented in [A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry](https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30691-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1550413118306910%3Fshowall%3Dtrue). This dataset was generated using imaging mass cytometry [@giesen2014imc]. Raw output files (in .mcd format) were processed using the [IMC segmentation pipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline), which produces tiff-stacks containing the pixel-level information of all measured markers, segmentation masks that contain the cells' object ids as well as cell- and image-specific measurements. Cell-specific measurements include the mean marker intensity per cell and per marker, the cells' position and size measurements. Pixel-level intensities for all 5 markers (5 channels) are stored in the `pancreasImages` object. Entries to the `CytoImageList` object and the rownames of `elementMetadata` match: E34_imc, G01_imc, and J02_imc. The `elementMetadata` slot (accesible via the `mcols()` function) contains the image identifiers. ```{r pancreasImages} pancreasImages mcols(pancreasImages) channelNames(pancreasImages) imageData(pancreasImages[[1]])[1:15,1:5,1] ``` The corresponding segmentation masks are stored in the `pancreasMasks` object and can be read in from tiff images containing the segmentation masks (see next section). Segmentation masks are defined as one-channel images containing integer values, which represent the cells' ids or 0 (background). ```{r pancreasMasks} pancreasMasks mcols(pancreasMasks) imageData(pancreasMasks[[1]])[1:15,1:5] ``` The IMC segmentation pipeline also generates cell-specific measurements. The `SingleCellExperiment` class offers an ideal container to store cell-specific expression counts together with cell-specific metadata. For the toy dataset, cell-specific mean marker intensities (`counts`) and arcsinh-transformed mean marker intensities (`exprs`) are stored in the `assays(pancreasSCE)` slot. All cell-specific metadata are stored in the `colData` slot of the corresponding `SingleCellExperiment` object: `pancreasSCE`. For more information on the metadata, please refer to the `?pancreasSCE` documentation. Of note: the cell-type labels contained in the `colData(pancreasSCE)$CellType` slot are arbitrary and only partly represent biologically relevant cell-types. ```{r pancreasSCE} pancreasSCE names(colData(pancreasSCE)) ``` The `pancreasSCE` object also contains further information on the measured proteins via the `rowData(pancreasSCE)` slot. Furthermore, the `pancreasSCE` object contains the raw expression counts per cell in the form of mean pixel value per cell and protein (accessible via `counts(pancreasSCE)`). The arcsinh-transformed (using a co-factor of 1) raw expression counts can be obtained via `assay(pancreasSCE, "exprs")`. For more information on how to generate `SingleCellExperiment` objects from count-based data, see [Orchestrating Single-Cell Analysis with Bioconductor](https://osca.bioconductor.org/data-infrastructure.html#the-singlecellexperiment-class). # Reading in data {#ReadingData} The `cytomapper` package provides the `loadImages` function to conveniently read images into a `CytoImageList` object. ## Load images The `loadImages` function returns a `CytoImageList` object containing the multi-channel images or segmentation masks. Refer to the `?loadImages` function to see the full functionality. As an example, we will read in multi-channel images and segmentation masks provided by the `cytomapper` package. ```{r read-in-images} # Read in masks path.to.images <- system.file("extdata", package = "cytomapper") all_masks <- loadImages(path.to.images, pattern = "_mask.tiff") all_masks # Read in images all_stacks <- loadImages(path.to.images, pattern = "_imc.tiff") all_stacks ``` ## Add metadata To link images between the two `CytoImageList` objects and the corresponding `SingleCellExperiment` object, the image ids need to be added to the `elementMetadata` slot of the `CytoImageList` objects. From the experimental setup, we know that the image named 'E34_imc' has image id '1', G01_imc has id '2', J02_imc has id '3'. ```{r add-metadata} unique(pancreasSCE$ImageNb) mcols(all_masks)$ImageNb <- c("1", "2", "3") mcols(all_stacks)$ImageNb <- c("1", "2", "3") ``` ## Scale images We can see that, in some cases, the pixel-values are not correctly scaled by the image encoding. The segmentation masks should only contain integer entries: ```{r image-encoding} head(unique(as.numeric(all_masks[[1]]))) ``` The provided data was processed using CellProfiler [@carpenter2006cellprofiler]. By default, CellProfiler scales all pixel intensities between 0 and 1. This is done by dividing each count by the maximum possible intensity value (see [MeasureObjectIntensity](https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-3.0.0/modules/measurement.html#measureobjectintensity) for more info). In the case of 16-bit encoding (where 0 is a valid intensity), this scaling value is `2^16-1 = 65535`. Therefore, pixel-intensites need to be rescaled by this value. However, this scaling value can change and different images can be scaled by different factors. The user needs make sure to select the correct factors in more complex cases. The `cytomapper` package provides a `?scaleImages` function. The user needs to manually scale images to obtain the correct pixel-values. Here, we scale the segmentation masks by the factor for 16-bit encoding: `2^16-1` ```{r scaleImage-1} all_masks <- scaleImages(all_masks, 2^16-1) head(unique(as.numeric(all_masks[[1]]))) ``` For this toy dataset, the multi-channel images are not affected by this scaling factor. The final `all_masks` object corresponds to the `pancreasMasks` object provided by `cytomapper`. ## Add channel names To access the correct images in the multi-channel `CytoImageList` object, the user needs to set the correct channel names. For this, the `cytomapper` package provides the `?channelNames` getter and setter function: ```{r channelNames-example} channelNames(all_stacks) <- c("H3", "CD99", "PIN", "CD8a", "CDH") ``` The read-in data can now be used for visualization as explained in the [Quick start section](#QuickStart). # The CytoImageList object {#CytoImageList} The `cytomapper` package provides a new `CytoImageList` class, which inherits from the [`SimpleList` class](https://rdrr.io/bioc/S4Vectors/man/SimpleList-class.html). Each entry to the `CytoImageList` object is an `Image` class object defined in the `r BiocStyle::Biocpkg("EBImage")` package. A `CytoImageList` object is restricted to the following entries: * all images need to have the same number of channels * the order/naming of channels need to be the same across all images * entries to the `CytoImageList` object need to be uniquely named * names of `CytoImageList` object can either be `NULL` or should not contain `NA` or empty entries * only grayscale images are supported (see `?Image` for more information) * channels names do not support duplicated entries `CytoImageList` objects that contain masks should only contain a single channel The following paragraphs will explain further details on manipulating `CytoImageList` objects ## Accessors {#accessors} All accessor functions defined for `SimpleList` also work on `CytoImageList` class objects. Element-wise metadata --- in the case of the `CytoImageList` object these are image-specific metadata --- are saved in the `elementMetadata` slot. This slot can be accessed via the `mcols()` function: ```{r mcols} mcols(pancreasImages) mcols(pancreasImages)$PatientID <- c("Patient1", "Patient2", "Patient3") mcols(pancreasImages) ``` Subsetting a `CytoImageList` object works similar to a `SimpleList` object: ```{r subsetting-SimpleList} pancreasImages[1] pancreasImages[[1]] ``` However, to facilitate subsetting and making sure that entry names are transfered between objects, the `cytomapper` package provides a number of getter and setter functions: ### Getting and setting images Individual or multiple entries in a `CytoImageList` object can be obtained or replaced using the `getImages` and `setImages` functions, respectively. ```{r get-set-images} cur_image <- getImages(pancreasImages, "E34_imc") cur_image setImages(pancreasImages, "New_image") <- cur_image pancreasImages mcols(pancreasImages) ``` The `setImages` function ensures that names are transfered from one to the other object along the assignment operator: ```{r set-images} names(cur_image) <- "Replacement" setImages(pancreasImages, 2) <- cur_image pancreasImages mcols(pancreasImages) ``` However, if the image to replace is called by name, only the image and associated metadata is replaced: ```{r set-images-2} setImages(pancreasImages, "J02_imc") <- cur_image pancreasImages mcols(pancreasImages) ``` Images can be deleted by setting the entry to `NULL`: ```{r deleting-images} setImages(pancreasImages, c("Replacement", "New_image")) <- NULL pancreasImages ``` **Of note:** for plotting, the entries in the `img_id` slot in the `CytoImageList` objects have to be unique. ### Getting and setting channels The `cytomapper` package also provides functions to obtain and replace channels. This functionality is provided via the `getChannels` and `setChannels` functions: ```{r get-set-channels} cur_channel <- getChannels(pancreasImages, "H3") cur_channel channelNames(cur_channel) <- "New_H3" setChannels(pancreasImages, 1) <- cur_channel pancreasImages ``` The `setChannels` function does not allow combining and adding new channels. For this task, the `cytomapper` package provides the `mergeChannels` section in the next paragraph. ### Naming and merging channels Channel names can be obtained and replaced using the `channelNames` getter and setter function: ```{r channelnames} channelNames(pancreasImages) channelNames(pancreasImages) <- c("ch1", "ch2", "ch3", "ch4", "ch5") pancreasImages ``` Furthermore, channels can be merged using the `mergeChannels` function: ```{r mergechannels} cur_channels <- getChannels(pancreasImages, 1:2) channelNames(cur_channels) <- c("new_ch1", "new_ch2") pancreasImages <- mergeChannels(pancreasImages, cur_channels) pancreasImages ``` ## Looping To perform custom operations on each individual entry to a `CytoImageList` object, the `r Biocpkg("S4Vectors")` package provides the `endoapply` function. While the `lapply` function returns a `list` object, the `endoapply` function provides an object of the same class of the input object. This allows the user to apply all functions provided by the `r Biocpkg("EBImage")` package to individual entries within the `CytoImageList` object: ```{r endoapply-example} data("pancreasImages") # Performing a gaussian blur pancreasImages <- endoapply(pancreasImages, gblur, sigma = 1) ``` # Plotting pixel information {#PixelInfo} The `cytomapper` package provides the `plotPixels` function to plot pixel-level intensities of marker proteins. The function requires a `CytoImageList` object containing a single or multiple multi-channel images. To colour images based on channel name, the `channelNames` of the object need to be set. Furthermore, to outline cells, a `CytoImageList` object containing segmentation masks and a `SingleCellExperiment` object containing cell-specific metadata need to be provided. By default, pixel values are coloured internally and scaled between the minimum and maximum values across all displayed images. However, to manipulate pixel values and to linearly scale values to a certain range, the `cytomapper` package provides a function for image normalization. ## Normalization The `normalize` function provided in the `cytomapper` package internally calls the `normalize` function of the `r Biocpkg("EBImage")` package. The main difference between the two functions is the option to scale per image or globally in the `cytomapper` package (see `?'normalize,CytoImageList-method'`). By default, the `normalize` function linearly scales the images channel-wise across all images and returns values between 0 and 1 (or the chosen `ft` range): ```{r normalize-default} data("pancreasImages") # Default normalization cur_images <- normalize(pancreasImages) ``` A `CytoImageList` object can also be normalized image-wise: ```{r normalize-image-wise} # Image-wise normalization cur_images <- normalize(pancreasImages, separateImages = TRUE) ``` To clip the image range, the user can provide a clipping range. ```{r normalize-clippingrange} # Percentage-based clipping range cur_images <- normalize(pancreasImages) cur_images <- normalize(cur_images, inputRange = c(0, 0.9)) plotPixels(cur_images, colour_by = c("H3", "CD99", "CDH")) ``` For more information on the normalization functionality provided by the `cytomapper` package, please refer to `?'normalize,CytoImageList-method'`. ## Colouring The `cytomapper` package supports the visualization of up to 6 channels and displays a combined image by setting the `colour_by` parameter. See `?plotPixels` for examples. ## Adjusting brightness, contrast and gamma To enhance individual channels, the brightness (b), contrast (c) and gamma (g) can be set channel-wise via the `bcg` parameter. These parameters are set in form of a named `list` object. Entry names need to correspond by channels specified in `colour_by`. Each entry takes a numeric vector of length three where the first entry represents the brightness value, the second the contrast factor and the third the gamma factor. Internally, the brightness value is added to each channel; each channel is multiplied by the contrast factor and each channel is exponentiated by the gamma factor. ```{r bcg-pixels} data("pancreasImages") # Increase contrast for the CD99 and CDH channel plotPixels(pancreasImages, colour_by = c("H3", "CD99", "CDH"), bcg = list(CD99 = c(0,2,1), CDH = c(0,2,1))) ``` ## Outlining {#Outlining} The cells can be outlined when providing a `CytoImageList` object containing the corresponding segmentation masks and a character `img_id` indicating the name of the `elementMetadata` slot that contains the image IDs. The user can furthermore specify the metadata entry to outline cells by. For this, a `SingleCellExperiment` object containing the cell-specific metadata and a `cell_id` indicating the name of the `colData` slot that contains the cell IDs need to be provided: ```{r outline-1-pixels} plotPixels(pancreasImages, mask = pancreasMasks, object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", colour_by = c("H3", "CD99", "CDH"), outline_by = "CellType") ``` ## Subsetting {#subsetting} The user can subset the images before calling the plotting functions: ```{r subsetting-pixels} cur_images <- getImages(pancreasImages, "J02_imc") plotPixels(cur_images, colour_by = c("H3", "CD99", "CDH")) ``` For further information on subsetting functionality, please refer to the [Accessors](#accessors) section. ## Adjusting the colour The user can also customize the colours for selected features. The `colour` parameter takes a named `list` in which names correspond to the entries to `colour_by`. To colour continous features such as expression or continous metadata entries (e.g. cell area, see next section), at least two colours for interpolation need to be provided. These colours are passed to the `colorRampPalette` function for interpolation. For details, please refer to the next [Adjusting the colour](#adjustingColour) section # Plotting cell information {#CellInfo} In the following sections, the `plotCells` function will be introduced. This function displays cell-level information on segmentation masks. It requires a `CytoImageList` object containing segmentation masks in the form of single-channel images. Furthermore, to colour and outline cells, a `SingleCellExperiment` object containing cell-specific expression counts and metadata needs to be provided. By default, cell-specific expression values are coloured internally and scaled marker-specifically between the minimum and maximum values across the full `SingleCellExperiment`. ## Colouring Segmentation masks can be coloured based on the pixel-values averaged across the area of each cell. In the `SingleCellExperiment` object, these values can be obtained from the `counts()` slot. To colour segmentation masks based on expression, the `rownames` of the `SingleCellExperiment` must be correctly named. The `cytomapper` package supports the visualization of up to 6 channels and displays a combined image. However, in the case of displaying expression on segmentation mask, the user should not display too many features. See `?plotCells` for examples. ## Changing the assay slot To visualize differently transformed counts, the `plotCells` function allows setting the `exprs_values` parameter. In the toy dataset, the `assay(pancreasSCE, "exprs")` slot contains the arcsinh-transformed raw expression counts. ```{r exprs_values-cells} plotCells(pancreasMasks, object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", colour_by = c("CD8a", "PIN"), exprs_values = "exprs") ``` ## Outlining The user can furthermore outline cells and specify the metadata entry to outline cells by. See the previous [Outlining](#Outlining) section and `?plotCells` for examples. ## Subsetting Similar to the `plotPixels` function, the user can subset the images before plotting. For an example, please see the previous [Subsetting](#subsetting) section and the [Accessors](#accessors) section. ## Adjusting the colour {#adjustingColour} The user can also customize the colours for selected features and metadata. The `colour` parameter takes a named `list` in which names correspond to the entries to `colour_by` and/or `outline_by`. To colour continous features such as expression or continous metadata entries (e.g. cell area), at least two colours for interpolation need to be provided. These colours are passed to the `colorRampPalette` function for interpolation. To colour discrete entries, one colour per entry needs to be specified in form of a named vector. ```{r colour-cells} plotCells(pancreasMasks, object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", colour_by = c("CD99", "CDH"), outline_by = "CellType", colour = list(CD99 = c("black", "red"), CDH = c("black", "white"), CellType = c(celltype_A = "blue", celltype_B = "green", celltype_C = "yellow"))) ``` # Customisation The next sections explain different ways to customise the visual output of the `cytomapper` package. To find more details on additional parameters that can be set to customise the display, refer to `?'plotting-param'`. ## Subsetting the SingleCellExperiment object The `cytomapper` package matches cells contained in the `SingleCellExperiment` to objects contained in the `CytoImageList` segmentation masks object via cell identifiers. These are integer values, which are unique to each object per image. By matching these IDs, the user can subset the `SingleCellExperiment` object and therefore only visualize the cells retained in the object: ```{r subsetting-SCE} cur_sce <- pancreasSCE[,colData(pancreasSCE)$CellType == "celltype_A"] plotCells(pancreasMasks, object = cur_sce, img_id = "ImageNb", cell_id = "CellNb", colour_by = "CellType", colour = list(CellType = c(celltype_A = "red"))) ``` ## Background and missing colour The background of a segemntation mask is defined by the value `0`. To change the background colour, the `background_colour` parameter can be set. Furthermore, cells that are not contained in the `SingleCellExperiment` object can be coloured by setting `missing_colour`. For an example, see figure \@ref(fig:customization). ## Scale bar and image title Depending on the cells' and background colour, the scale bar and image title are not visible. To change the visual display of the scale bar, a named list can be passed to the `scale_bar` parameter. The list should contain one or multiple of the following entries: `length`, `label`, `cex`, `lwidth`, `colour`, `position`, `margin`, `frame`. For a detailed explanation on the individual entries, please refer to the `scale_bar` section in `?'plotting-param'`. **Of note:** By default, the length of the scale bar is defined in number of pixels. Therefore, the user needs to know the length (e.g. in $\mu{m}$) to label the scale bar correctly. The image titles can be set using the `image_title` parameter. Also here, the user needs to provide a named list with one or multiple of follwing entries: `text`, `position`, `colour`, `margin`, `font`, `cex`. The entry to `text` needs to be a character vector of the same length as the `CytoImageList` object. Plotting of the scale bar and image title can be suppressed by setting the `scale_bar` and `image_title` parameters to `NULL`. For an example, see figure \@ref(fig:customization). ## Legend By default, the legend all all its contents are adjusted to the size of the largest image in the `CytoImageList` object. However, legend features can be altered by setting the `legend` parameter. It takes a named list containing one or multiple of the follwoing entries: `colour_by.title.font`, `colour_by.title.cex`, `colour_by.labels.cex`, `colour_by.legend.cex`, `outline_by.title.font`, `outline_by.title.cex`, `outline_by.labels.cex`, `outline_by.legend.cex`, `margin`. For detailed explanation on the individual entries, please refer to the `legend` parameter in `?'plotting-param'`. For an example, see figure \@ref(fig:customization). ## Setting the margin between images To enhance the display of individual images, the `cytomapper` package provides the `margin` parameter. The `margin` parameter takes a single numeric indicating the gap (in pixels) between individual images. For an example, see figure \@ref(fig:customization). ## Scale the feature counts By default, features are scaled to the minimum and maximum per channel. This behaviour facilitates visualization but does not allow the user to visually compare absolute expression counts across channels. The default behaviour can be suppressed by setting `scale = FALSE`. In this case, counts are linearly scaled to the minimum and maximum across all channels and across all displayed images. For an example, see figure \@ref(fig:customization). ## Image interpolation By default, colours are interpolated between pixels (see `?rasterImage` for details). To suppress this default behaviour, the user can set `interpolate = FALSE`. ```{r customization, fig.cap = "Plot customization example."} plotCells(pancreasMasks, object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", colour_by = "CD99", outline_by = "CellType", background_colour = "white", missing_colour = "black", scale_bar = list(length = 30, label = expression("30 " ~ mu * "m"), cex = 2, lwidth = 10, colour = "cyan", position = "bottomleft", margin = c(5,5), frame = 3), image_title = list(text = c("image_1", "image_2", "image_3"), position = "topleft", colour = "cyan", margin = c(2,10), font = 3, cex = 2), legend = list(colour_by.title.font = 2, colour_by.title.cex = 1.2, colour_by.labels.cex = 0.7, outline_by.legend.cex = 0.3, margin = 10), margin = 2) ``` # Returning plots and images The user has the option to save the generated plots (see next section) or to get the plots and/or coloured images returned. If `return_plot` and/or `return_images` is set to `TRUE`, `cytomapper` returns a list object with one or two entries: `plot` and/or `images`. The `display` parameter supports the entries `display = "all"` (default), which displays images in a grid-like fashion and `display = "single"`, which display images individually. If the `return_plot` parameter is set to `TRUE`, `cytomapper` internally calls the `recordPlot` function and returns a plot object. The user can additionally set `display = "single"` to get a list of plots returned. If the `return_images` parameter is set to `TRUE`, `cytomapper` returns a `SimpleList` object containing three-colour (red, green, blue) `Image` objects. ```{r return_plot} cur_out <- plotPixels(pancreasImages, colour_by = c("H3", "CD99", "CDH"), return_plot = TRUE, return_images = TRUE, display = "single") ``` The returned plot objects now allows the plotting of individual images: ```{r return_plot-2} cur_out$plot$E34_imc ``` Furthermore, the user can directly plot the coloured images from the returned `SimpleList` object: ```{r return_images} plot(cur_out$images$G01_imc) ``` However, when plotting solely the coloured images, the image title and scale bar will be lost. # Saving images Finally, the user can save the plot by specifying `save_plot`. The `save_plot` entry takes a list of two entries: `filename` and `scale`. The `filename` should be a character representing a valid file name ending with `.png`, `.tiff` or `.jpeg`. The `scale` entry controls the resolution of the image (see `?"plotting-param"` for help). Increasing the scale parameter will increase the resolution of the final image. When setting `display = "single"`, the `cytomapper` package will save individual images in individual files. The filename will be altered to the form `filename_x.png` where `x` is the position of the image in the `CytoImageList` object or `legend`. # Acknowledgements We want to thank the [Bodenmiller laboratory](http://www.bodenmillerlab.org) for feedback on the package and its functionality. Special thanks goes to Daniel Schulz and Jana Fischer for testing the package. # Contributions Nicolas created the first version of `cytomapper` (named `IMCMapper`). Nils and Nicolas implemented and maintain the package. # Session info {.unnumbered} ```{r sessionInfo, echo=FALSE} sessionInfo() ``` # References