,
Boyi Guo [aut] ,
Stephanie C. Hicks [aut] | Title: | Address the Mean-variance Relationship in Spatial Transcriptomics Data |
|---|---|
| Description: | This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools. |
| Authors: | Kinnary Shah [aut, cre] ,
Boyi Guo [aut] ,
Stephanie C. Hicks [aut] |
| Maintainer: | Kinnary Shah <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.1 |
| Built: | 2024-11-19 03:36:18 UTC |
| Source: | https://github.com/bioc/spoon |
Generate weights on the observation level for each gene
generate_weights( input, spatial_coords = NULL, assay_name = "counts", stabilize = TRUE, n_threads = 1, BPPARAM = NULL )generate_weights( input, spatial_coords = NULL, assay_name = "counts", stabilize = TRUE, n_threads = 1, BPPARAM = NULL )
input |
either a SpatialExperiment object which contains a counts matrix, or a counts matrix |
spatial_coords |
matrix containing columns of spatial coordinates, needed if input is a matrix |
assay_name |
if using a SpatialExperiment object, name of the assay in which the counts matrix is stored |
stabilize |
when TRUE, stabilize weights to avoid extrapolation (highly recommended) |
n_threads |
default = 1, number of threads for parallelization |
BPPARAM |
optional additional argument for parallelization to use BiocParallel |
This function generates weights for each observation, which are used as input to scale the data and covariates
weights matrix
library(nnSVG) library(STexampleData) library(SpatialExperiment) library(BRISC) library(BiocParallel) library(scuttle) spe <- Visium_humanDLPFC() # keep spots over tissue spe <- spe[, colData(spe)$in_tissue == 1] # filter low-expressed and mitochondrial genes spe <- filter_genes(spe) # calculate logcounts (log-transformed normalized counts) using scran package spe <- computeLibraryFactors(spe) spe <- logNormCounts(spe) known_genes <- c("MOBP", "PCP4", "SNAP25", "HBB", "IGKC", "NPY") ix_known <- which(rowData(spe)$gene_name %in% known_genes) ix <- c(ix_known) spe <- spe[ix, ] spe <- spe[, colSums(logcounts(spe)) > 0] #EXAMPLE 1 USING SPATIAL EXPERIMENT set.seed(1) weights_1 <- generate_weights(input = spe, stabilize = TRUE) #EXAMPLE 2 USING MATRIX counts_mat <- counts(spe) logcounts_mat <- logcounts(spe) coords_mat <- spatialCoords(spe) set.seed(1) weights_2 <- generate_weights(input = counts_mat, spatial_coords = coords_mat, stabilize = TRUE)library(nnSVG) library(STexampleData) library(SpatialExperiment) library(BRISC) library(BiocParallel) library(scuttle) spe <- Visium_humanDLPFC() # keep spots over tissue spe <- spe[, colData(spe)$in_tissue == 1] # filter low-expressed and mitochondrial genes spe <- filter_genes(spe) # calculate logcounts (log-transformed normalized counts) using scran package spe <- computeLibraryFactors(spe) spe <- logNormCounts(spe) known_genes <- c("MOBP", "PCP4", "SNAP25", "HBB", "IGKC", "NPY") ix_known <- which(rowData(spe)$gene_name %in% known_genes) ix <- c(ix_known) spe <- spe[ix, ] spe <- spe[, colSums(logcounts(spe)) > 0] #EXAMPLE 1 USING SPATIAL EXPERIMENT set.seed(1) weights_1 <- generate_weights(input = spe, stabilize = TRUE) #EXAMPLE 2 USING MATRIX counts_mat <- counts(spe) logcounts_mat <- logcounts(spe) coords_mat <- spatialCoords(spe) set.seed(1) weights_2 <- generate_weights(input = counts_mat, spatial_coords = coords_mat, stabilize = TRUE)
Run nnSVG for SVG detection using the weights
weighted_nnSVG( input, spatial_coords = NULL, assay_name = "logcounts", w, n_threads = 1, BPPARAM = MulticoreParam(workers = 1) )weighted_nnSVG( input, spatial_coords = NULL, assay_name = "logcounts", w, n_threads = 1, BPPARAM = MulticoreParam(workers = 1) )
input |
either a SpatialExperiment object which contains a logcounts matrix, or a logcounts matrix |
spatial_coords |
matrix containing columns of spatial coordinates, needed if input is a matrix |
assay_name |
if using a SpatialExperiment object, name of the assay in which the logcounts matrix is stored |
w |
weights matrix |
n_threads |
default = 1, number of threads for parallelization |
BPPARAM |
optional additional argument for parallelization to use BiocParallel |
This function incorporates weights for each observation to run nnSVG
either spe with weighted nnSVG statistics, or matrix with weighted nnSVG statistics
library(nnSVG) library(STexampleData) library(SpatialExperiment) library(BRISC) library(BiocParallel) library(scuttle) library(Matrix) spe <- Visium_humanDLPFC() # keep spots over tissue spe <- spe[, colData(spe)$in_tissue == 1] # filter low-expressed and mitochondrial genes spe <- filter_genes(spe) # calculate logcounts (log-transformed normalized counts) using scran package spe <- computeLibraryFactors(spe) spe <- logNormCounts(spe) known_genes <- c("MOBP", "PCP4", "SNAP25", "HBB", "IGKC", "NPY") ix_known <- which(rowData(spe)$gene_name %in% known_genes) ix <- c(ix_known) spe <- spe[ix, ] spe <- spe[, colSums(logcounts(spe)) > 0] #EXAMPLE 1 USING SPATIAL EXPERIMENT set.seed(1) weights_1 <- generate_weights(input = spe, stabilize = TRUE) spe_results <- weighted_nnSVG(input = spe, w = weights_1, BPPARAM = MulticoreParam(workers = 1, RNGseed = 4)) # display results rowData(spe_results) #EXAMPLE 2 USING MATRIX counts_mat <- counts(spe) logcounts_mat <- logcounts(spe) coords_mat <- spatialCoords(spe) set.seed(1) weights_2 <- generate_weights(input = counts_mat, spatial_coords = coords_mat, stabilize = TRUE) results <- weighted_nnSVG(input = logcounts_mat, spatial_coords = coords_mat, w = weights_2, BPPARAM = MulticoreParam(workers = 1, RNGseed = 4)) # display results print(results)library(nnSVG) library(STexampleData) library(SpatialExperiment) library(BRISC) library(BiocParallel) library(scuttle) library(Matrix) spe <- Visium_humanDLPFC() # keep spots over tissue spe <- spe[, colData(spe)$in_tissue == 1] # filter low-expressed and mitochondrial genes spe <- filter_genes(spe) # calculate logcounts (log-transformed normalized counts) using scran package spe <- computeLibraryFactors(spe) spe <- logNormCounts(spe) known_genes <- c("MOBP", "PCP4", "SNAP25", "HBB", "IGKC", "NPY") ix_known <- which(rowData(spe)$gene_name %in% known_genes) ix <- c(ix_known) spe <- spe[ix, ] spe <- spe[, colSums(logcounts(spe)) > 0] #EXAMPLE 1 USING SPATIAL EXPERIMENT set.seed(1) weights_1 <- generate_weights(input = spe, stabilize = TRUE) spe_results <- weighted_nnSVG(input = spe, w = weights_1, BPPARAM = MulticoreParam(workers = 1, RNGseed = 4)) # display results rowData(spe_results) #EXAMPLE 2 USING MATRIX counts_mat <- counts(spe) logcounts_mat <- logcounts(spe) coords_mat <- spatialCoords(spe) set.seed(1) weights_2 <- generate_weights(input = counts_mat, spatial_coords = coords_mat, stabilize = TRUE) results <- weighted_nnSVG(input = logcounts_mat, spatial_coords = coords_mat, w = weights_2, BPPARAM = MulticoreParam(workers = 1, RNGseed = 4)) # display results print(results)