GitHub - lmweber/nnSVG: nnSVG: scalable method to identify spatially variable genes (SVGs) in spatially-resolved transcriptomics data (original) (raw)
nnSVG
Overview
nnSVG is a method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data.
The nnSVG method is based on nearest-neighbor Gaussian processes (Datta et al., 2016, Finley et al., 2019) and uses the BRISC algorithm (Saha and Datta, 2018) for model fitting and parameter estimation. nnSVG allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. The method scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.
nnSVG is implemented as an R package within the Bioconductor framework, and is available from Bioconductor.
Our paper describing the method is available from Nature Communications.
Installation
The package can be installed from Bioconductor as follows, using R version 4.2 or above:
install.packages("BiocManager") BiocManager::install("nnSVG")
Alternatively, the latest development version of the package can also be installed from GitHub:
remotes::install_github("lmweber/nnSVG")
If you are installing from GitHub, the following dependency packages may need to be installed manually from Bioconductor and CRAN (these are installed automatically if you install from Bioconductor instead):
install.packages("BiocManager") BiocManager::install("SpatialExperiment") BiocManager::install("STexampleData") install.packages("BRISC")
Tutorial
A detailed tutorial is available in the package vignette from Bioconductor. A direct link to the tutorial / package vignette is available here.
Input data format
In the examples below, we assume the input data are provided as a SpatialExperiment Bioconductor object. In this case, the outputs are stored in the rowData of the SpatialExperiment object.
Alternatively, the inputs can also be provided as a numeric matrix of normalized and transformed counts (e.g. log-transformed normalized counts, also known as logcounts) and a numeric matrix of spatial coordinates.
Example workflow
A short example workflow is shown below. This is a modified version of the full tutorial available in the package vignette from Bioconductor. A direct link to the tutorial / package vignette is available here).
Load packages
library(nnSVG) library(STexampleData) library(scran) library(ggplot2)
Load example dataset
load example dataset from STexampleData package
spe <- Visium_humanDLPFC() dim(spe)
Preprocessing
keep spots over tissue
spe <- spe[, colData(spe)$in_tissue == 1] dim(spe)
spot-level quality control: already performed on this example dataset
filter low-expressed and mitochondrial genes
using function from nnSVG package with default filtering parameters
spe <- filter_genes(spe)
Gene filtering: removing mitochondrial genes
removed 13 mitochondrial genes
Gene filtering: retaining genes with at least 3 counts in at least 0.5% (n = 19) of spatial locations
removed 30216 out of 33525 genes due to low expression
calculate logcounts (log-transformed normalized counts) using scran package
using library size factors
spe <- computeLibraryFactors(spe) spe <- logNormCounts(spe) assayNames(spe)
[1] "counts" "logcounts"
Subset data for this example
select small set of random genes and several known SVGs for faster runtime in this example workflow
set.seed(123) ix_random <- sample(seq_len(nrow(spe)), 10) known_genes <- c("MOBP", "PCP4", "SNAP25", "HBB", "IGKC", "NPY") ix_known <- which(rowData(spe)$gene_name %in% known_genes) ix <- c(ix_known, ix_random)
spe <- spe[ix, ] dim(spe)
Run nnSVG
set seed for reproducibility
run nnSVG using a single thread for this example workflow
set.seed(123) spe <- nnSVG(spe, n_threads = 1)
show results
rowData(spe)
DataFrame with 16 rows and 17 columns
[...]
Investigate results
The results are stored in the rowData of the SpatialExperiment object.
The main results of interest are:
LR_stat: likelihood ratio (LR) statistics used to rank SVGsrank: rank of top SVGs according to LR statisticspval: approximate p-valuespadj: approximate p-values adjusted for multiple testingprop_sv: effect size defined as proportion of spatial variance
number of significant SVGs
table(rowData(spe)$padj <= 0.05)
show results for top n SVGs
n <- 10 rowData(spe)[order(rowData(spe)$rank)[1:n], ]
DataFrame with 10 rows and 17 columns
gene_id gene_name feature_type sigma.sq tau.sq
ENSG00000168314 ENSG00000168314 MOBP Gene Expression 1.38739383 0.364188
ENSG00000132639 ENSG00000132639 SNAP25 Gene Expression 0.43003959 0.430106
ENSG00000211592 ENSG00000211592 IGKC Gene Expression 0.56564845 0.455042
ENSG00000244734 ENSG00000244734 HBB Gene Expression 0.32942113 0.353754
ENSG00000183036 ENSG00000183036 PCP4 Gene Expression 0.23102220 0.452735
ENSG00000122585 ENSG00000122585 NPY Gene Expression 0.28567359 0.280173
ENSG00000129562 ENSG00000129562 DAD1 Gene Expression 0.02389607 0.464723
ENSG00000114923 ENSG00000114923 SLC4A3 Gene Expression 0.01147170 0.237260
ENSG00000133606 ENSG00000133606 MKRN1 Gene Expression 0.00632248 0.272432
ENSG00000143543 ENSG00000143543 JTB Gene Expression 0.07541566 0.463623
phi loglik runtime mean var spcov
ENSG00000168314 1.102018 -3663.60 0.631 0.805525 1.205673 1.462248
ENSG00000132639 3.033847 -3912.70 0.450 3.451926 0.857922 0.189973
ENSG00000211592 20.107022 -4531.64 1.054 0.622937 1.007454 1.207340
ENSG00000244734 27.814098 -4044.96 1.559 0.411262 0.697673 1.395587
ENSG00000183036 8.272278 -4026.22 0.419 0.687961 0.684598 0.698656
ENSG00000122585 71.653290 -3995.23 0.843 0.393975 0.567383 1.356646
ENSG00000129562 10.141894 -3842.24 0.590 0.549318 0.489167 0.281410
ENSG00000114923 12.765645 -2617.36 0.658 0.250768 0.248816 0.427112
ENSG00000133606 0.082764 -2831.51 0.612 0.295404 0.278806 0.269171
ENSG00000143543 119.721419 -4036.28 0.731 0.654919 0.539172 0.419318
prop_sv loglik_lm LR_stat rank pval padj
ENSG00000168314 0.7920804 -5503.33 3679.46397 1 0.00000e+00 0.00000e+00
ENSG00000132639 0.4999614 -4884.19 1942.98556 2 0.00000e+00 0.00000e+00
ENSG00000211592 0.5541822 -5176.53 1289.77508 3 0.00000e+00 0.00000e+00
ENSG00000244734 0.4821910 -4507.99 926.04573 4 0.00000e+00 0.00000e+00
ENSG00000183036 0.3378716 -4473.57 894.68884 5 0.00000e+00 0.00000e+00
ENSG00000122585 0.5048609 -4131.87 273.27818 6 0.00000e+00 0.00000e+00
ENSG00000129562 0.0489053 -3861.98 39.49098 7 2.65854e-09 6.07667e-09
ENSG00000114923 0.0461207 -2632.02 29.31376 8 4.31119e-07 8.62238e-07
ENSG00000133606 0.0226812 -2839.08 15.15227 9 5.12539e-04 9.11181e-04
ENSG00000143543 0.1399077 -4039.07 5.59664 10 6.09124e-02 9.74599e-02
Plot expression of top SVG
Plot expression of the top-ranked SVG.
plot spatial expression of top-ranked SVG
ix <- which(rowData(spe)$rank == 1) ix_name <- rowData(spe)$gene_name[ix] ix_name
df <- as.data.frame(cbind(spatialCoords(spe), expr = counts(spe)[ix, ]))
ggplot(df, aes(x = pxl_col_in_fullres, y = pxl_row_in_fullres, color = expr)) + geom_point(size = 0.8) + coord_fixed() + scale_y_reverse() + scale_color_gradient(low = "gray90", high = "blue", trans = "sqrt", breaks = range(df$expr), name = "counts") + ggtitle(ix_name) + theme_bw() + theme(plot.title = element_text(face = "italic"), panel.grid = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank())
Citation
Our paper describing nnSVG is available from Nature Communications: