Package 'spatialDE'

Title: R wrapper for SpatialDE
Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.
Authors: Davide Corso [aut] , Milan Malfait [aut] , Lambda Moses [aut] , Gabriele Sales [cre]
Maintainer: Gabriele Sales <[email protected]>
License: MIT + file LICENSE
Version: 1.13.0
Built: 2024-10-31 05:29:00 UTC
Source: https://github.com/bioc/spatialDE

Help Index

Plot Fraction Spatial Variance vs Q-value

Description

Plot Fraction Spatial Variance vs Q-value

Usage

FSV_sig(
  results,
  ms_results = NULL,
  certain_only = FALSE,
  log_x = FALSE,
  do_label = TRUE,
  covariate_names = NULL
)

Arguments

results

results from SpatialDE.
ms_results

model selection results, should be a data frame with columns g for gene names and model for the model selected.
certain_only

only plot results with narrow 95% confidence interval.
log_x

Whether to display x axis in log scale.
do_label

display gene names for statistically significant genes, default TRUE.
covariate_names

names of covariates as a reference, default to NULL.

Value

A ggplot2 object.

Author(s)

Davide Corso, Milan Malfait, Lambda Moses

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

SpatialDE 1.1.3: the version of the Python package used under the hood.

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

## Run spatialDE with S4 integration
results <- spatialDE(spe)

## Run model search
msearch <- modelSearch(spe, de_results = results, qval_thresh = NULL,
  verbose = FALSE)

plot <- FSV_sig(results, msearch)

Mouse Olfactory Bulb sample metadata

Description

Coordinates and total counts for the samples from the Mouse Olfactory Bulb data generated by Stahl et al. (2016). This data was originally downloaded from https://github.com/Teichlab/SpatialDE/blob/master/Analysis/MouseOB/MOB_sample_info.csv.

Usage

data(MOB_sample_info)

Format

A data.frame with 262 rows and 3 variables as columns: the x and y coordinates and total_counts corresponding to each spot.

References

Ståhl, P. L. et al. (2016) 'Visualization and analysis of gene expression in tissue sections by spatial transcriptomics', Science, 353(6294), p. 78. doi: 10.1126/science.aaf2403.

Generate count matrix for spatially variable genes.

Description

Generate count matrix for spatially variable genes.

Usage

mockSVG(size, tot_genes, de_genes, return_SPE = FALSE)

Arguments

size

An integer scalar. Cells will be spatially arranged on a ⁠size x size⁠ grid. Default: 10, corresponding to 100 cells.
tot_genes

An integer scalar. Total number of genes. Default: 1000.
de_genes

An integer scaler. The number of spatially variable genes. Default: 100.
return_SPE

A logical, whether to return result as a SpatialExperiment. Default: FALSE.

Value

If return_SPE = TRUE, returns a SpatialExperiment object.

If not, a list containing:

  • coordinates: data.frame with x and y columns;

  • counts: matrix with generated gene counts.

Examples

spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)
spe

Compare model fits with different models

Description

Classify DE genes to interpretable fitting classes.

Usage

model_search(x, coordinates, de_results, qval_thresh = 0.05, verbose = FALSE)

Arguments

x

matrix-like object of normalized counts. E.g. resulting from regress_out().
coordinates

data.frame with sample coordinates. Each row is a sample, the columns with coordinates must be named 'x' and 'y'.
de_results

data.frame resulting from run().
qval_thresh

numeric scalar, specifying the q-value significance threshold to filter de_results. Only rows in de_results with qval < qval_thresh will be kept. To disable, set qval_thresh = NULL.
verbose

logical controlling the display of the progress bar.

Value

data.frame of model_search results.

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
mock <- mockSVG(size = 20, tot_genes = 3, de_genes = 1)

stabilized <- stabilize(mock$counts)
sample_info <- mock$coordinates
sample_info$total_counts <- colSums(mock$counts)
regressed <- regress_out(counts = stabilized, sample_info = sample_info)

## Run SpatialDE
de_results <- run(regressed, coordinates = mock$coordinates)

## Run model search
ms_results <- model_search(
    x = regressed,
    coordinates = mock$coordinates,
    de_results = de_results,
    qval_thresh = NULL
)

Classify Spatially Variable Genes to interpretable fitting classes

Description

Compare model fits with different models, using the SpatialDE Python package.

Usage

modelSearch(x, de_results, ...)

## S4 method for signature 'matrix'
modelSearch(x, de_results, coordinates, qval_thresh = 0.05, verbose = FALSE)

## S4 method for signature 'SpatialExperiment'
modelSearch(
  x,
  de_results,
  assay_type = "counts",
  qval_thresh = 0.05,
  verbose = FALSE
)

Arguments

x

A numeric matrix of counts where genes are rows and cells are columns.

Alternatively, a SpatialExperiment object.
de_results

data.frame resulting from run() or spatialDE().
...

For the generic, arguments to pass to specific methods.
coordinates

A data.frame with sample coordinates. Each row is a sample, the columns with coordinates should be named 'x' and 'y'.

For the SpatialExperiment method, coordinates are taken from spatialCoords(x).
qval_thresh

numeric scalar, specifying the q-value significance threshold to filter de_results. Only rows in de_results with qval < qval_thresh will be kept. To disable, set qval_thresh = NULL.
verbose

A logical controlling the display of a progress bar from the Python package.
assay_type

A character string specifying the assay from x to use as input. Defaults to "counts".

Value

data.frame of model_search results.

Author(s)

Davide Corso, Milan Malfait, Lambda Moses

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

SpatialDE 1.1.3: the version of the Python package used under the hood.

See Also

The individual steps performed by this function: stabilize(), regress_out() and model_search().

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

## Run spatialDE with S4 integration
de_results <- spatialDE(spe)

## Run model search
model_search <- modelSearch(spe, de_results = de_results,
    qval_thresh = NULL, verbose = FALSE
)

Plot Spatial Patterns of Multiple Genes

Description

Plot Spatial Patterns of Multiple Genes

Usage

multiGenePlots(x, ...)

## S4 method for signature 'matrix'
multiGenePlots(
  x,
  coordinates,
  genes_plot,
  viridis_option = "D",
  ncol = 2,
  point_size = 1,
  dark_theme = TRUE
)

## S4 method for signature 'SpatialExperiment'
multiGenePlots(
  x,
  assay_type = "counts",
  genes_plot,
  viridis_option = "D",
  ncol = 2,
  point_size = 1,
  dark_theme = TRUE
)

Arguments

x

A numeric matrix of stabilized counts (e.g. resulting from stabilize()) where genes are rows and cells are columns.

Alternatively, a SpatialExperiment object.
...

For the generic, arguments to pass to specific methods.
coordinates

A data.frame with sample coordinates. Each row is a sample, the columns with coordinates should be named 'x' and 'y'.

For the SpatialExperiment method, coordinates are taken from spatialCoords(x).
genes_plot

character vector specifying which genes are to be plotted.
viridis_option

This function uses the viridis palette to color cells for gene expression. Four options are available: "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"), "viridis" (or "D", the default option) and "cividis" (or "E").
ncol

Number of columns to arrange the plots.
point_size

Point size of each plot.
dark_theme

Whether dark background should be used; this is helpful to highlight cells with high expression when using the viridis palette.
assay_type

A character string specifying the assay from x to use as input. Defaults to "counts".

Value

This function draws a plot for each specified genes

Author(s)

Davide Corso, Milan Malfait, Lambda Moses

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

SpatialDE 1.1.3: the version of the Python package used under the hood.

See Also

The individual steps performed by this function: stabilize(), spatialDE().

For further analysis of the DE results: model_search() and spatial_patterns().

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

## Run spatialDE
results <- spatialDE(spe)

ordered_spe_results <- results[order(results$qval), ]
head(ordered_spe_results)

plots <- multiGenePlots(spe,
    assay_type = "counts",
    ordered_spe_results$g,
    point_size = 4,
    viridis_option = "D"
)

Regress out library size effect

Description

Regresses out the effect of library size. This function is a wrapper for regress_out from the NaiveDE Python package.

Usage

regress_out(counts, sample_info)

Arguments

counts

matrix of variance stabilized counts, e.g. resulting from stabilize().
sample_info

data.frame with samples as rows and at least a column with total_counts.

Value

matrix of normalized counts.

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
mock <- mockSVG(20, 3, 1)

stabilized <- stabilize(mock$counts)
sample_info <- mock$coordinates
sample_info$total_counts <- colSums(mock$counts)

regressed <- regress_out(counts = stabilized, sample_info = sample_info)

Mouse Olfactory Bulb spatial gene expression data

Description

Replicate 11 from the spatially dependent gene expression data from the mouse olfactory bulb generated by Stahl et al. (2016). This data was originally downloaded from https://github.com/Teichlab/SpatialDE/blob/master/Analysis/MouseOB/data/Rep11_MOB_0.csv.

Usage

data(Rep11_MOB_0)

Format

A matrix with 16218 genes as rows and 262 spots as columns.

References

Ståhl, P. L. et al. (2016) 'Visualization and analysis of gene expression in tissue sections by spatial transcriptomics', Science, 353(6294), p. 78. doi: 10.1126/science.aaf2403.

Perform SpatialDE test

Description

Wraps the run function from the SpatialDE Python package.

Usage

run(x, coordinates, verbose = FALSE)

Arguments

x

matrix-like object of normalized counts. E.g. resulting from regress_out().
coordinates

data.frame with sample coordinates. Each row is a sample, the columns with coordinates must be named 'x' and 'y'.
verbose

logical controlling the display of the progress bar.

Value

A data.frame with DE results where each row is a gene and columns contain relevant statistics.

The most important columns are:

  • g: the name of the gene

  • pval: the p-value for spatial differential expression

  • qval: the q-value, indicating significance after correcting for multiple testing

  • l: A parameter indicating the distance scale a gene changes expression over

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
mock <- mockSVG(size = 20, tot_genes = 3, de_genes = 1)

stabilized <- stabilize(mock$counts)
sample_info <- mock$coordinates
sample_info$total_counts <- colSums(mock$counts)
regressed <- regress_out(counts = stabilized, sample_info = sample_info)

## Run SpatialDE
de_results <- run(regressed, coordinates = mock$coordinates)

Group spatially variable genes into spatial patterns using automatic expression histology (AEH)

Description

Group spatially variable genes into spatial patterns using automatic expression histology (AEH)

Usage

spatial_patterns(
  x,
  coordinates,
  de_results,
  qval_thresh = 0.05,
  n_patterns,
  length,
  verbose = FALSE
)

Arguments

x

matrix-like object of normalized counts. E.g. resulting from regress_out().
coordinates

data.frame with sample coordinates. Each row is a sample, the columns with coordinates must be named 'x' and 'y'.
de_results

data.frame resulting from run().
qval_thresh

numeric scalar, specifying the q-value significance threshold to filter de_results. Only rows in de_results with qval < qval_thresh will be kept. To disable, set qval_thresh = NULL.
n_patterns

integer The number of spatial patterns
length

numeric The characteristic length scale of the clusters
verbose

logical controlling the display of the progress bar.

Value

list of two dataframe (pattern_results, patterns): pattern_results dataframe with pattern membership information for each gene. patterns the posterior mean underlying expression fro genes in given spatial patterns.

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
mock <- mockSVG(size = 20, tot_genes = 3, de_genes = 1)

stabilized <- stabilize(mock$counts)
sample_info <- mock$coordinates
sample_info$total_counts <- colSums(mock$counts)
regressed <- regress_out(counts = stabilized, sample_info = sample_info)

## Run SpatialDE
de_results <- run(x = regressed, coordinates = mock$coordinates)

## Run Spatial_patterns
sp <- spatial_patterns(
    x = regressed,
    coordinates = mock$coordinates,
    de_results = de_results,
    qval_thresh = NULL,
    n_patterns = 5, length = 1.5
)

sp$pattern_results
sp$patterns

Find spatially variable genes with SpatialDE

Description

Identify genes that significantly depend on spatial coordinates with the SpatialDE Python package.

Usage

spatialDE(x, ...)

## S4 method for signature 'matrix'
spatialDE(x, coordinates, verbose = FALSE)

## S4 method for signature 'SpatialExperiment'
spatialDE(x, assay_type = "counts", verbose = FALSE)

Arguments

x

A numeric matrix of counts where genes are rows and cells are columns.

Alternatively, a SpatialExperiment object.
...

For the generic, arguments to pass to specific methods.
coordinates

A data.frame with sample coordinates. Each row is a sample, the columns with coordinates should be named 'x' and 'y'.

For the SpatialExperiment method, coordinates are taken from spatialCoords(x).
verbose

A logical controlling the display of a progress bar from the Python package.
assay_type

A character string specifying the assay from x to use as input. Defaults to "counts".

Value

A data.frame with DE results where each row is a gene and columns contain relevant statistics.

The most important columns are:

  • g: the name of the gene

  • pval: the p-value for spatial differential expression

  • qval: the q-value, indicating significance after correcting for multiple testing

  • l: A parameter indicating the distance scale a gene changes expression over

Author(s)

Davide Corso, Milan Malfait, Lambda Moses

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

SpatialDE 1.1.3: the version of the Python package used under the hood.

See Also

The individual steps performed by this function: stabilize(), regress_out() and run().

For further analysis of the DE results: model_search() and spatial_patterns().

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

## Run spatialDE
de_results <- spatialDE(spe)

head(de_results)

Automatic expression histology in SpatialDE

Description

Group spatially variable genes into spatial patterns using Automatic Expression Histology, using the SpatialDE Python package.

Usage

spatialPatterns(x, de_results, ...)

## S4 method for signature 'matrix'
spatialPatterns(
  x,
  de_results,
  coordinates,
  qval_thresh = 0.05,
  n_patterns,
  length,
  verbose = FALSE
)

## S4 method for signature 'SpatialExperiment'
spatialPatterns(
  x,
  de_results,
  qval_thresh = 0.05,
  n_patterns,
  length,
  assay_type = "counts",
  verbose = FALSE
)

Arguments

x

A numeric matrix of counts where genes are rows and cells are columns.

Alternatively, a SpatialExperiment object.
de_results

data.frame resulting from run() or spatialDE().
...

For the generic, arguments to pass to specific methods.
coordinates

A data.frame with sample coordinates. Each row is a sample, the columns with coordinates should be named 'x' and 'y'.

For the SpatialExperiment method, coordinates are taken from spatialCoords(x).
qval_thresh

numeric scalar, specifying the q-value significance threshold to filter de_results. Only rows in de_results with qval < qval_thresh will be kept. To disable, set qval_thresh = NULL.
n_patterns

integer The number of spatial patterns
length

numeric The characteristic length scale of the clusters
verbose

A logical controlling the display of a progress bar from the Python package.
assay_type

A character string specifying the assay from x to use as input. Defaults to "counts".

Value

A list of two data.frames (pattern_results, patterns):

  • pattern_results: data.frame with pattern membership information for each gene.

  • patterns the posterior mean underlying expression from genes in given spatial patterns.

Author(s)

Davide Corso, Milan Malfait, Lambda Moses

References

Svensson, V., Teichmann, S. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat Methods 15, 343–346 (2018). https://doi.org/10.1038/nmeth.4636

SpatialDE 1.1.3: the version of the Python package used under the hood.

See Also

The individual steps performed by this function: stabilize(), regress_out() and spatial_patterns().

Examples

## Mock up a SpatialExperiment object wit 100 cells and 3 genes
set.seed(42)
spe <- mockSVG(size = 10, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

## Run spatialDE
de_results <- spatialDE(spe)

spatial_patterns <- spatialPatterns(spe, de_results = de_results,
    qval_thresh = NULL, n_patterns = 4L, length = 1.5,
    verbose = FALSE
)

head(spatial_patterns$pattern_results)
head(spatial_patterns$patterns)

Stabilize variance of counts

Description

Stabilize variance of negative binomial data using Anscombe's approximation. This function is a wrapper for stabilize from the NaiveDE Python package.

Usage

stabilize(counts)

Arguments

counts

matrix with expression values for samples in columns and genes in rows.

Value

matrix of variance stabilized counts.

Examples

## Mock up a SpatialExperiment object wit 400 cells and 3 genes
set.seed(42)
mock <- mockSVG(20, 3, 1)

stabilized <- stabilize(mock$counts)