Add neighborhood analysis module functions to cheatsheet

components/dictionary.txt

@@ -84,6 +84,7 @@ codebook
 codeword
 codewords
 colData
+colocalize
 colorectal
 colour
 ComplexHeatmap
@@ -304,6 +305,7 @@ Menyhárt
 mer
 merchantability
 MERFISH
+MERSCOPE
 mesenchymal
 mesoderm
 metacell
@@ -344,6 +346,7 @@ myocytes
 myoepithelial
 myofibroblast
 myogenesis
+NanoString
 natively
 NCI
 nd
@@ -494,6 +497,7 @@ Srivastava
 SRP
 SRR
 StackOverflow
+STARmap
 StatQuest
 Stoeckius
 str
@@ -566,6 +570,7 @@ versicolor
 virginica
 Visium
 vitro
+Vizgen
 VST
 Wattenberg
 WCSS

module-cheatsheets/spatial-cheatsheet.md

@@ -20,12 +20,15 @@ If you have a different version of R or other R packages, the documentation may
 - [`SpotSweeper`](#spotsweeper)
 - [`Banksy`](#banksy)
 - [`spacexr`](#spacexr)
+- [`SpatialExperimentIO`](#spatialexperimentio)
+- [`hoodscanR`](#hoodscanr)
 - [`scuttle`, `scran`, and `scater`](#scuttle-scran-and-scater)
 - [`bluster`](#bluster)
 - [`patchwork`](#patchwork)
 - [`pals`](#pals)
 - [`pheatmap`](#pheatmap)
 - [`purrr`](#purrr)
+- [`ComplexHeatmap`](#complexheatmap)
 
 <!-- END doctoc generated TOC please keep comment here to allow auto update -->
 
@@ -125,6 +128,40 @@ Read the [`spacexr` package documentation](https://www.bioconductor.org/packages
 
 <div style="page-break-after: always;"></div>
 
+### `SpatialExperimentIO`
+
+<!-- TODO: This package has not yet made it to rdrr -->
+Read the [`SpatialExperimentIO` package documentation](https://www.bioconductor.org/packages/release/bioc/html/SpatialExperimentIO.html) and a [vignette on its usage](https://www.bioconductor.org/packages/release/bioc/vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.html).
+
+This package provides reader functions for imaging-based spatial transcriptomics platforms, including Xenium (10x Genomics), CosMx (NanoString), MERSCOPE (Vizgen), and STARmap PLUS.
+
+
+| Library/Package      | Piece of Code      | What it's called    | What it does  |
+|----------------------|----------------------------|--------------------------------------------|--------------------------------------------------------------|
+| `SpatialExperimentIO` | [`readXeniumSXE()`](https://www.bioconductor.org/packages/release/bioc/vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.html#xenium) | Read Xenium into a Spatial Experiment | Loads an unzipped 10x Genomics Xenium output bundle directory into a `SpatialExperiment` object |
+
+<div style="page-break-after: always;"></div>
+
+### `hoodscanR`
+
+<!-- TODO: This package has not yet made it to rdrr -->
+Read the [`hoodscanR` package documentation](https://www.bioconductor.org/packages/release/bioc/html/hoodscanR.html) and a [vignette on its usage](https://www.bioconductor.org/packages/release/bioc/vignettes/hoodscanR/inst/doc/Quick_start.html).
+
+Note that the functions in this package are generally run in sequence, where the output of one function is the input to the next.
+
+
+| Library/Package      | Piece of Code      | What it's called    | What it does  |
+|----------------------|----------------------------|--------------------------------------------|--------------------------------------------------------------|
+| `hoodscanR` | [`findNearCells()`](https://davislaboratory.github.io/hoodscanR/reference/findNearCells.html) | Find the k-the nearest cells for each | Find the `k` nearest cells to each cell in a `SpatialExperiment` object, returning a list with a matrix of distances and a matrix of the annotations (e.g., cell types) of those neighboring cells |
+| `hoodscanR` | [`scanHoods()`](https://davislaboratory.github.io/hoodscanR/reference/scanHoods.html) | Scan cellular neighborhoods | Apply a modified softmax algorithm to the distance matrix from `hoodscanR::findNearCells()` to calculate the probability that each cell is associated with each of its nearest neighboring cells |
+| `hoodscanR` | [`mergeByGroup()`](https://davislaboratory.github.io/hoodscanR/reference/mergeByGroup.html) | Merge probability matrix based on annotations | Collapse the probability matrix from `hoodscanR::scanHoods()` by annotation, so that each column is a group (e.g., a cell type) rather than an individual neighboring cell |
+| `hoodscanR` | [`mergeHoodSpe()`](https://davislaboratory.github.io/hoodscanR/reference/mergeHoodSpe.html) | Merge probability matrix into Spatial Experiment object | Add the merged probability matrix into the `colData` of a `SpatialExperiment` object |
+| `hoodscanR` | [`calcMetrics()`](https://davislaboratory.github.io/hoodscanR/reference/calcMetrics.html) | Calculate metrics for probability matrix | Calculate the entropy and perplexity of the probability matrix for each cell, which summarize how mixed or how distinct that cell's neighborhood is, and store them in `colData` |
+| `hoodscanR` | [`plotColocal()`](https://davislaboratory.github.io/hoodscanR/reference/plotColocal.html) | Plot heatmap for neighborhood analysis | Plot a heatmap of the Pearson correlations between cells' neighborhood probability distributions to show which groups tend to colocalize |
+| `hoodscanR` | [`clustByHood()`](https://davislaboratory.github.io/hoodscanR/reference/clustByHood.html) | Cluster the probability matrix with K-means | Perform k-means clustering on the probability matrix to assign each cell to a neighborhood cluster |
+| `hoodscanR` | [`plotProbDist()`](https://davislaboratory.github.io/hoodscanR/reference/plotProbDist.html) | Plot probability distribution | Plot the distribution of neighborhood probabilities, optionally split by a grouping variable such as the neighborhood clusters from `hoodscanR::clustByHood()` |
+
+<div style="page-break-after: always;"></div>
 
 ### `scuttle`, `scran`, and `scater`
 
@@ -141,14 +178,14 @@ Read the [`scater` package documentation](https://rdrr.io/bioc/scater/), and a [
 | `scuttle`| [`addPerCellQC()`](https://rdrr.io/bioc/scuttle/man/addPerCellQC.html)| Add per cell quality control | For a `SingleCellExperiment` object, calculate and add quality control per cell and store in `colData`  |
 | `scuttle` | [`computeLibraryFactors()`](https://rdrr.io/bioc/scuttle/man/librarySizeFactors.html)  | Compute Library Factors | Returns a numeric vector of computed size factors for each spot (or cell) stored in a `SpatialExperiment` (or `SingleCellExperiment`) object. The size factor is computed as the library size of each spot/cell after scaling them to have a mean of 1 across all spots/cells |
 | `scuttle`| [`logNormCounts()`](https://rdrr.io/bioc/scuttle/man/logNormCounts.html)| Normalize log counts | Returns the `SpatialExperiment` (or `SingleCellExperiment`) object with normalized expression values for each spot (cell), using the size factors stored in the object |
+| `scuttle`| [`isOutlier()`](https://rdrr.io/bioc/scuttle/man/isOutlier.html)| Identify outliers | Convenience function to determine which values in a numeric vector are outliers based on the median absolute deviation (MAD) |
 | `scran`| [`getTopHVGs()`](https://rdrr.io/bioc/scran/man/getTopHVGs.html)| Get top highly variable genes | Identify variable genes in a `SingleCellExperiment` object, based on variance |
 | `scran`| [`modelGeneVar()`](https://rdrr.io/bioc/scran/man/modelGeneVar.html)| model per gene variance | Model the per gene variance of a `SingleCellExperiment` object |
 | `scran`| [`clusterCells()`](https://rdrr.io/github/MarioniLab/scran/man/clusterCells.html)| Cluster cells | Perform clustering on an SCE object using the `bluster` package |
 | `scater`| [`runPCA()`](https://rdrr.io/bioc/scater/man/runPCA.html)| Run PCA | Calculates principal components analysis on a `SingleCellExperiment` object, returning an SCE object with a PCA reduced dimension |
 | `scater`| [`runUMAP()`](https://rdrr.io/bioc/scater/man/runUMAP.html)| Run UMAP | Calculates uniform manifold approximate projection on a `SingleCellExperiment` object, returning an SCE object with a UMAP reduced dimension |
 | `scater`| [`plotUMAP()`](https://rdrr.io/bioc/scater/man/plot_reddim.html)| Plot UMAP | Plot the "UMAP"-named reduced dimension slot from a `SingleCellExperiment` object |
 
-
 <div style="page-break-after: always;"></div>
 
 
@@ -216,4 +253,14 @@ Read the [`purrr` package documentation](https://purrr.tidyverse.org/) and a [vi
 |----------------|--------------|-----------------|--------------|
 | `purrr`| [`map()`](https://purrr.tidyverse.org/reference/map.html)| map | Apply a function across each element of list; return a list |
 | `purrr`| [`imap()`](https://purrr.tidyverse.org/reference/imap.html)| imap |  Apply a function across each element of list and its index/names; return a list |
-| `purrr`| [`reduce()`](https://purrr.tidyverse.org/reference/reduce.html)| Reduce |  Reduce a list to a single value by repeatedly applying a given function. Can also be used to iteratively modify a single object. |
+| `purrr`| [`reduce()`](https://purrr.tidyverse.org/reference/reduce.html)| Reduce |  Reduce a list to a single value by repeatedly applying a given function. Can also be used to iteratively modify a single object. |
+
+
+### `ComplexHeatmap`
+
+Read the [`ComplexHeatmap` package documentation](https://rdrr.io/bioc/ComplexHeatmap/) and the [`ComplexHeatmap` Complete Reference e-book](https://jokergoo.github.io/ComplexHeatmap-reference/book/).
+
+
+| Library/Package      | Piece of Code      | What it's called    | What it does  |
+|----------------------|--------------------|---------------------|---------------|
+| `ComplexHeatmap`| [`Heatmap()`](https://rdrr.io/bioc/ComplexHeatmap/man/Heatmap.html)| Heatmap | Create a clustered heatmap from a matrix, with options to split, annotate, and combine it with other heatmaps |

spatial/05-xenium_processing.Rmd

@@ -12,7 +12,7 @@ output:
 
 - Read Xenium data into R
 - Calculate quality control measures on imaging spatial transcriptomic data
-- Remove likely low-quality spots with `SpaceTrooper()`
+- Remove likely low-quality spots
 - Normalize spatial expression data
 
 ## Introduction
@@ -398,7 +398,7 @@ p1 + p2
 ```
 
 To flag these cells, we summarize each cell by its counts-per-area ratio and look for cells that sit far below the bulk of the distribution.
-Specifically, we use `scater::isOutlier()` to flag cells whose ratio falls more than three median absolute deviations (MADs) below the median.
+Specifically, we use `scuttle::isOutlier()` to flag cells whose ratio falls more than three median absolute deviations (MADs) below the median.
 
 * The MAD is a robust measure of spread and is less sensitive to extreme values than the standard deviation. 
 * We will use the common cutoff of 3 * MAD. 
@@ -412,7 +412,7 @@ count_area_ratio <- spe$total_counts / spe$cell_area
 
 # flag cells that are outliers on the low side
 # (3 median absolute deviations below the median, on the log2 scale since the metric is a ratio)
-outliers <- scater::isOutlier(count_area_ratio, log = TRUE, type = "lower", nmads = 3)
+outliers <- scuttle::isOutlier(count_area_ratio, log = TRUE, type = "lower", nmads = 3)
 
 # print the structure which tells us where to find the identified threshold
 str(outliers)