--- title: "Spatial Data Quality Control with SpaceTrooper Package" author: "Dario Righelli" date: "`r BiocStyle::doc_date()`" output: BiocStyle::html_document: toc: true toc_float: true vignette: > %\VignetteIndexEntry{Spatial Data Quality Control with SpaceTrooper Package} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console --- ```{r setup, include=FALSE} # Set chunk options: suppress echo, messages, and warnings in code output knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE) ``` # Introduction This vignette introduces the `SpaceTrooper` package for preprocessing and quality control of imaging-based spatial omics, from platforms like **Xenium**, **Merfish**, and **CosMx**. We cover data loading, quality control, and results visualization. # Installation To install `SpaceTrooper`, use the following commands: ```{r install, eval=FALSE} # Install BiocManager if not already installed, then install SpaceTrooper if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("SpaceTrooper") ``` # Data Loading In this section, we load data from various platforms using the package's functions. The goal is to provide a uniform `SpatialExperiment` object across all technologies, allowing for consistent QC analysis. The functions in `SpaceTrooper` compute missing metrics as needed and allow for the inclusion of polygons with the `keep_polygons` argument. This stores polygons in the `colData` of the `SpatialExperiment`. We suggest to use the `keep_polygons` argument for technologies like **Xenium** and **Merfish/Merscope** because we already load the polygons to compute missing metrics in these cases. Note that when loading data with `readXeniumSPE` in HDF5 format, this creates a `DelayedArray` count assay in the `SpatialExperiment`, which could lead to some issues in downstream analysis, in case this data structure is not supported by the used package. ```{r load-data} library(SpatialExperiment) # Load the SpaceTrooper library library(SpaceTrooper) # Load Xenium data into a Spatial Experiment object (SPE) xeniumFolder <- system.file( "extdata", "Xenium_small", package="SpaceTrooper") (xen <- readXeniumSPE(xeniumFolder, computeMissingMetrics=TRUE, keepPolygons=TRUE)) colData(xen) # Load Merfish data into an SPE with parquet boundaries merscopeFolder <- system.file("extdata", "Merfish_Tiny", package="SpaceTrooper") (mer <- readMerfishSPE(merscopeFolder, boundariesType="parquet", computeMissingMetrics=TRUE, keepPolygons=TRUE)) colData(mer) ``` Despite main differences across the technologies provided data, we can see that all the technologies have the same mandatory `cell_id` and `AspectRatio` columns. # Data Analysis based on CosMx The package offers several functions for spatial data analysis, including quality control and visualization. This tutorial focuses on CosMx data, which provides Fields of View (FoVs) with cell identifiers. Note that FoVs are unique to CosMx and are not available for other technologies like Xenium and Merfish/Merscope. For CosMx data, you don't need to `keep_polygons` initially because the metrics can be computed without them. Polygons can be loaded later if needed. ```{r cosmx-analysis} # Reload CosMx data with sample name and without polygons cosmxFolder <- system.file(file.path("extdata", "CosMx_DBKero_Tiny"), package="SpaceTrooper") spe <- readCosmxSPE(cosmxFolder, sampleName="DBKero_CosMx") ## a custom function for the labels of this dataset source(system.file(file.path("scripts", "labelsfunct.R"), package="SpaceTrooper")) spe <- addLabelsDBKero(spe) spe ``` # Field of Views (FOVs) Visualization The `plotCellsFovs` function shows a map of the FoVs within an experiment. This plot is specific to CosMx data and uses cell centroids. ```{r plot-fovs} # Plot the cells within their respective Field of Views (FOVs) plotCellsFovs(spe) ``` Because the dataset is a subset of just one Field of View of the original experiment, we are able to see the identifier of the FoV in black and the centroids of the cells in purple. When an experiment has multiple FoVs, you can see the map and the topological organization of the FoVs, together with their identifiers. # Visualizing cell types To focus on cell centroids, use the `plotCentroidsSPE` function. It provides a `colour_by` argument, similar to `scater`, to colour the centroids by the values present in the indicated column. Additionally, if you have a column with a color palette it automatically maps it to the values passed in the `colour_by` argument. ```{r plot-centroids-labels} # Plot the centroids of the cells in the SPE plotCentroids(spe, colourBy="labels") # Plot the centroids of the cells in the SPE with personalized labels plotCentroids(spe, colourBy="labels", palette="labels_colors") ``` This allow us to see the cell centroids coloured by their cell types giving us an idea of the cell population in the entire experiment. Additionally, the `palette` argument allows to use a user-defined palette for the associated centroids. The `palette` value must by a column in the object `colData`. Notice that with this plot the FoV identifier is not shown. # Quality control The `spatialPerCellQC` function, inspired by `scater::addPerCellQC`, computes additional metrics for each cell in the `SpatialExperiment`. It also allows for the detection of negative control probes, which is crucial for QC. By default, it automatically removes 0 counts cells, but this can be handled with the `rmZeros` argument. ```{r cosmx-analysis-qc} # Perform per-cell quality control checks spe <- spatialPerCellQC(spe, rmZeros=TRUE, negProbList=c("NegPrb", "Negative", "SystemControl")) names(colData(spe)) ``` # Metrics Histograms You can investigate individual metrics by viewing their histograms. For outliers, use the `use_fences` argument to display the fences computed by `computeSpatialOutlier` (see next chunk). ```{r plot-metrics} # Plot a histogram of counts (sum) plotMetricHist(spe, metric="sum") # Plot a histogram of cell areas (Area_um) plotMetricHist(spe, metric="Area_um") # Plot a histogram of proportion of counts respect to the cell area in micron plotMetricHist(spe, metric="log2CountArea") ``` These plots show, respectively, the distributions of the total counts (`sum`), of the Area in micron (`Area_um`) and the relationship between the counts and the Area of each cell (`log2CountArea`). # Spatial Outlier Detection Spatial outlier detection is another critical step in QC. While the flag score addresses some metrics, other outlier detection methods may be needed. The `computeSpatialOutlier` function allows the computation of the medcouple statistics on a specified metric (`compute_by` argument). The medcouple is specifically designed for asymmetric distributions, indeed the function stamps a warning message when this requisite is not satisfied. It can also use `scuttle::isOutlier` for symmetric distributions. The `method` argument supports `mc`, `scuttle`, or `both`. This outlier detection approach can be used to decide if and which cells can be discarded on a singular metric. ```{r cosmx-analysis-outlier, warning=FALSE} # Identify spatial outliers based on cell area (Area_um) spe <- computeSpatialOutlier(spe, computeBy="Area_um", method="both") # Identify spatial outliers based on mean DAPI intensity spe <- computeSpatialOutlier(spe, computeBy="Mean.DAPI", method="both") names(colData(spe)) ``` If we computed outliers with the `computeSpatialOutlier` function, we can also visualize which fences have been used to create the filter on the cells. ```{r plot-metrics-2} # Plot a histogram with fences to identify outliers using the medcouple plotMetricHist(spe, metric="Area_um", useFences="Area_um_outlier_mc") # Plot a histogram with fences to identify outliers using scuttle plotMetricHist(spe, metric="Area_um", useFences="Area_um_outlier_sc") # Plot a histogram with fences to identify outliers using the medcouple plotMetricHist(spe, metric="Mean.DAPI", useFences="Mean.DAPI_outlier_mc") # Plot a histogram with fences to identify outliers using scuttle plotMetricHist(spe, metric="Mean.DAPI", useFences="Mean.DAPI_outlier_sc") ``` We visualize the fences computed with medcouple and scuttle outlier detection approaches, to directly inspect differences and the amount of detected outlier each method detected. If we want, we can already use these fences to remove the computed outliers. # The Quality Score Next, we use `computeQCScore` to calculate a flag score based on previously computed metrics. The quality score combines **transcript counts** related to **cell area**, the **aspect ratio** of each cell, and its **distance from the FoV border** (only for CosMx, this last one is not used otherwise). See the `help(computeQCScore)` details section for additional details. ```{r cosmx-analysis-score} # Calculate quality scores for each cell spe <- computeQCScore(spe) names(colData(spe)) ``` Logical filters can then be computed using `computeQCScoreFlags`, which requires thresholds for various metrics. Currently, the function considers: * **Quality Score (`qsThreshold`)**: Cells with scores below this threshold (default 0.5) are flagged for exclusion. This value can be used to indicate the quantile for the filtering when setting the `useQSQuantiles` argument to `TRUE`. * **Quality Score Quantiles (`useQSQuantiles`)**: Option to filter based on quantiles (default `FALSE`). ```{r cosmx-analysis-score2} # Compute flags to identify cells for filtering spe <- computeQCScoreFlags(spe, qsThreshold=0.5) names(colData(spe)) table(spe$low_qcscore) ``` We detected 88 cells to be removed. # Additional metrics to filter out cells While for other metrics such as the total counts and the negative prob ratio, the function `computeThresholdFlags` considers: * **Total Counts (`totalThreshold`)**: Minimum count threshold (default 0). * **Negative Probe Ratio (`ctrlTotRatioThreshold`)**: Minimum ratio of negative probes to total counts (default 0.1). ```{r threshold-outlier} spe <- computeThresholdFlags(spe, totalThreshold=0, ctrlTotRatioThreshold=0.1) table(spe$threshold_flags) ``` In this example, we don't find any cell to be removed. # Adding Polygon and Visualization To better understand the quality score values we start to load the polygons, giving us a better overview of the cells characteristics. We can load and add polygons to the SPE object using the following functions. Each technology has its own `readPolygons` function to standardize the loaded `sf` object and handle different file types. ```{r plot-polygons} # Read polygon data associated with cells in the SPE # the polygon file path is stored in the spe metadata pols <- readPolygonsCosmx(metadata(spe)$polygons) # Add the polygon data to the SPE object spe <- addPolygonsToSPE(spe, pols) ``` Once the polygons are stored in an `sf` object within `colData`, they can be visualized using functions based on the `ggplot2` library. ```{r plot-polygons-fov-1} # Plot the polygons of the selected cells plotPolygons(spe, bgColor="white") ``` Showing the cells on a white background for better visualization. ```{r plot-polygons-fov-2} # Plot polygons colored by cell area plotPolygons(spe, colourBy="log2AspectRatio") plotPolygons(spe, colourBy="Area_um") ``` We can see in `yellow` and `darkviolet` that there are few cells with extreme values of `log2AspectRatio` and `Area_um` in micron. ```{r plot-polygons-fov-3} plotPolygons(spe, colourBy="QC_score") plotPolygons(spe, colourBy="low_qcscore") ``` We can see that the quality score is able to detect both these aspects and highlight the cells that are mostly isolated on the FoV border or showing a weird confomation. We always recommend to be aware of the cell populations in the under-study context, before proceeding to remove the detected cells. # Fov Zoom and Map The `plotZoomFovsMap` function allows you to visualize a map of the FoVs with a zoom-in of selected FoVs, colored by the `colour_by` argument. ```{r plot-zoom-fov} plotZoomFovsMap(spe, fovs=16, colourBy="QC_score") plotZoomFovsMap(spe, fovs=16, colourBy="low_qcscore") ``` We see on the left side the map of all the FoVs (only the FoV 16 in this case), together with the poligons on the right, coloured by the quality score. Allowing us to have a better view of a specific tissue area in the whole experiment. # Conclusion In this vignette, we explored the main functionalities of the `SpaceTrooper` package for spatial data analysis. Main steps shown are: * data loading: Xenium, Merfish/Merscope, CosMx * polygons loading: Xenium, Merfish/Merscope, CosMx * quality control: + outlier detection: medcouple and scuttle MAD + flag score: a score combining **transcript counts**, **cell area**, **aspect ratio** and **distance from the FoV border** * visualization: + centroids: with ggplot2 + polygons: sf + ggplot2 # Session Information ```{r sessionInfo} sessionInfo() ```