# spoon
<img src="vignettes/hex.png" width="50"/>
## Introduction
`spoon` is a method to address the mean-variance relationship in spatially resolved transcriptomics data. Current approaches rank spatially variable genes based on either p-values or some effect size, such as the proportion of spatially variable genes. However, previous work in RNA-sequencing has shown that a technical bias, referred to as the "mean-variance relationship", exists in these data in that the gene-level variance is correlated with mean RNA expression. We found that there is a "mean-variance relationship" in spatial transcriptomics data, and so we propose `spoon`, a statistical framework to prioritize spatially variable genes that is not confounded by this relationship. We fit a spline curve to estimate the mean-variance relationship. Then, similar to using weights in a weighted least squares model, we used weights that we plugged into a Gaussian Process Regression model fit with a nearest-neighbor Gaussian process model to the preprocessed expression measurements for each gene, i.e. one model per gene. `spoon` removes the bias and leads to a more informative set of spatially variable genes.
The `generate_weights()` function calculates individual observation weights, where an individual observation is a UMI (unique molecular identifier) count value for a specific gene and sample. If the desired SVG detection method accepts weights, then the individual observation weights can be used as inputs. If the desired SVG detection method does not accept weights, then the Delta method is leveraged to rescale the data and covariates by the weights. These scaled data and covariates are used as inputs into the desired SVG detection function.
Bioconductor houses the infrastructure to store and analyze spatially resolved transcriptomics data for R users, including many SVG detection methods. This method addresses the mean-variance relationship confounding SVG detection, which is related to these other Bioconductor packages. Additionally, `spoon` is inspired by `limma::voom()` , which is a popular Bioconductor package.
## Installation
The following code will install the latest release version of the `spoon` package from Bioconductor. Additional details are shown on the [Bioconductor](https://bioconductor.org/packages/spoon) page.
```{r, eval=FALSE}
install.packages("BiocManager")
BiocManager::install("spoon")
```
The latest development version can also be installed from the `devel` version of Bioconductor or from [GitHub](https://github.com/kinnaryshah/spoon).
## Input data format
We recommend the input data be provided as a [SpatialExperiment](https://bioconductor.org/packages/SpatialExperiment) Bioconductor object. The outputs are stored in the `rowData` of the `SpatialExperiment` object. The examples below use this input data format.
The inputs can also be provided as a numeric matrix of raw counts and a numeric matrix of spatial coordinates.
## Tutorial
**Load packages and data**
```{r}
library(nnSVG)
library(STexampleData)
library(SpatialExperiment)
library(BRISC)
library(BiocParallel)
library(scuttle)
library(Matrix)
library(spoon)
spe <- Visium_mouseCoronal()
```
**Preprocessing**
```{r}
# keep spots over tissue
spe <- spe[, colData(spe)$in_tissue == 1]
# filter out low quality genes
spe <- filter_genes(spe)
# calculate logcounts (log-transformed normalized counts) using scran package
spe <- computeLibraryFactors(spe)
spe <- logNormCounts(spe)
# choose a small number of genes for this example to run quickly
set.seed(3)
ix_random <- sample(seq_len(nrow(spe)), 10)
spe <- spe[ix_random, ]
# remove spots with zero counts
spe <- spe[, colSums(logcounts(spe)) > 0]
```
**Step 1: generate weights**
```{r}
weights <- generate_weights(input = spe,
stabilize = TRUE,
BPPARAM = MulticoreParam(workers = 1,
RNGseed = 4))
```
**Step 2: weighted SVG detection**
```{r}
spe <- weighted_nnSVG(input = spe,
w = weights,
BPPARAM = MulticoreParam(workers = 1, RNGseed = 5))
```
**Show results**
```{r}
# display results
rowData(spe)
```
**Other SVG detection tools**
We provided a function to use the weights with [nnSVG](https://www.nature.com/articles/s41467-023-39748-z) for more accurate spatially variable gene detection. The weights can also be used with other spatially variable gene detection tools using the following procedure:
```{r, eval=FALSE}
assay_name <- "logcounts"
weighted_logcounts <- t(weights)*assays(spe)[[assay_name]]
assay(spe, "weighted_logcounts") <- weighted_logcounts
```
`weighted_logcounts` can be accessed from `assay(spe, "weighted_logcounts")`. Then, `weighted_logcounts` should be used as the input counts matrix and `weights` as the input covariate matrix in a spatially variable detection tool.
## Session information
```{r}
sessionInfo()
```
