Package 'standR' reference manual

Title:	Spatial transcriptome analyses of Nanostring's DSP data in R
Description:	standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.
Authors:	Ning Liu [aut, cre] , Dharmesh D Bhuva [aut] , Ahmed Mohamed [aut]
Maintainer:	Ning Liu <[email protected]>
License:	MIT + file LICENSE
Version:	1.9.0
Built:	2024-07-25 03:00:59 UTC
Source:	https://github.com/bioc/standR

Add QC statistics to the Spatial Experiment object

Description

Add QC statistics to the Spatial Experiment object

Usage

addPerROIQC(
  spe_object,
  sample_fraction = 0.9,
  rm_genes = TRUE,
  min_count = 5,
  design = NULL
)
addPerROIQC(
  spe_object,
  sample_fraction = 0.9,
  rm_genes = TRUE,
  min_count = 5,
  design = NULL
)

Arguments

`spe_object`	A SpatialExperiment object
`sample_fraction`	Double. Genes with low count in more than this threshold of the samples will be removed. Default is 0.9
`rm_genes`	Logical. Decide whether genes with low count in more than sample_fraction of the samples are removed from the dataset. Default is TRUE.
`min_count`	Integer. Minimum read count to calculate count threshold. Default is 5.
`design`	Generate using `model.matrix`, if this is specify, `edgeR::filterByExpr` will be used to filter genes.

Value

A SpatialExperiment object

Examples

data("dkd_spe_subset")
spe_filtered <- addPerROIQC(dkd_spe_subset)
spe_filtered

data("dkd_spe_subset")
spe_filtered <- addPerROIQC(dkd_spe_subset)
spe_filtered

Calculate statistics for evaluating batch correction

Description

Calculate statistics for evaluating batch correction

Usage

computeClusterEvalStats(
  spe_object,
  foiColumn,
  precomputed = NULL,
  n_dimension = c(1, 2),
  assay = 2
)
computeClusterEvalStats(
  spe_object,
  foiColumn,
  precomputed = NULL,
  n_dimension = c(1, 2),
  assay = 2
)

Arguments

`spe_object`	A Spatial Experiment object.
`foiColumn`	A column name indicating the factor of interest to be tested, can be biological factor or batch factor.
`precomputed`	a dimensional reduction results from `stats::prcomp`. result in `reducedDims(object)` to plot. Default is NULL, we will compute for you.
`n_dimension`	The top n dimensions to be plotted
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).

Value

A dataframe object containing the clustering evaluating statistics.

Examples

library(scater)
data("dkd_spe_subset")
computeClusterEvalStats(dkd_spe_subset, "SlideName")

library(scater)
data("dkd_spe_subset")
computeClusterEvalStats(dkd_spe_subset, "SlideName")

Compute and plot the results of a PCA analysis on gene expression data

Description

Compute and plot the results of a PCA analysis on gene expression data

Usage

drawPCA(object, dims = c(1, 2), ...)

## S4 method for signature 'ExpressionSet'
drawPCA(object, dims = c(1, 2), precomputed = NULL, textScale = 1, ...)

## S4 method for signature 'SummarizedExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)

## S4 method for signature 'SingleCellExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)

## S4 method for signature 'SpatialExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)
drawPCA(object, dims = c(1, 2), ...)

## S4 method for signature 'ExpressionSet'
drawPCA(object, dims = c(1, 2), precomputed = NULL, textScale = 1, ...)

## S4 method for signature 'SummarizedExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)

## S4 method for signature 'SingleCellExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)

## S4 method for signature 'SpatialExperiment'
drawPCA(
  object,
  dims = c(1, 2),
  assay = 1,
  precomputed = NULL,
  textScale = 1,
  ...
)

Arguments

`object`	a DGEList, SummarizedExperiment or ExpressionSet object containing gene expression data.
`dims`	a numeric, containing 2 values specifying the dimensions to plot.
`...`	aesthetic mappings to pass to `ggplot2::aes_string()`.
`precomputed`	a dimensional reduction results from `stats::prcomp`. result in `reducedDims(object)` to plot.
`textScale`	a numeric, specifying the relative scale factor to apply to text on the plot.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).

Value

a ggplot2 object

Examples

data("dkd_spe_subset")
drawPCA(dkd_spe_subset)

data("dkd_spe_subset")
drawPCA(dkd_spe_subset)

Testing multiple K for RUV4 batch correction to find the best K.

Description

Testing multiple K for RUV4 batch correction to find the best K.

Usage

findBestK(
  spe,
  maxK = 10,
  factor_of_int,
  factor_batch,
  NCGs,
  point_size = 3,
  line_col = "black",
  point_col = "black",
  text_size = 13
)
findBestK(
  spe,
  maxK = 10,
  factor_of_int,
  factor_batch,
  NCGs,
  point_size = 3,
  line_col = "black",
  point_col = "black",
  text_size = 13
)

Arguments

`spe`	A Spatial Experiment object.
`maxK`	Integer. The max k to test, will test k from 1 to maxK, by default is 10.
`factor_of_int`	Column name(s) to indicate the factors of interest. This is required for the RUV4 method.
`factor_batch`	Column name to indicate the batch.
`NCGs`	Negative control genes. This is required for the RUV4 method.
`point_size`	Numeric. Plotting parameter.
`line_col`	Character. Plotting parameter.
`point_col`	Character. Plotting parameter.
`text_size`	Numeric. Plotting parameter.

Value

A ggplot object.

Examples

data("dkd_spe_subset")
spe <- findNCGs(dkd_spe_subset, top_n = 100)
findBestK(spe,
  factor_of_int = c("disease_status"),
  factor_batch = "SlideName", NCGs = S4Vectors::metadata(spe)$NCGs
)

data("dkd_spe_subset")
spe <- findNCGs(dkd_spe_subset, top_n = 100)
findBestK(spe,
  factor_of_int = c("disease_status"),
  factor_batch = "SlideName", NCGs = S4Vectors::metadata(spe)$NCGs
)

Get negative control genes from each batch of the data

Description

Get negative control genes from each batch of the data

Usage

findNCGs(spe, n_assay = 2, batch_name = "SlideName", top_n = 200)
findNCGs(spe, n_assay = 2, batch_name = "SlideName", top_n = 200)

Arguments

`spe`	A Spatial Experiment object.
`n_assay`	Integer to indicate the nth count table in the assay(spe) to be used.
`batch_name`	Column name indicating batches.
`top_n`	Integer indicate how many genes to be included as negative control genes.

Value

A Spatial Experiment object, conatining negative control genes in the metadata.

Examples

data("dkd_spe_subset")

spe <- findNCGs(dkd_spe_subset, top_n = 100)
S4Vectors::metadata(spe)$NCGs

data("dkd_spe_subset")

spe <- findNCGs(dkd_spe_subset, top_n = 100)
S4Vectors::metadata(spe)$NCGs

Batch correction for GeoMX data

Description

Batch correction for GeoMX data

Usage

geomxBatchCorrection(
  spe,
  k,
  factors,
  NCGs,
  n_assay = 2,
  batch = NULL,
  batch2 = NULL,
  covariates = NULL,
  design = matrix(1, ncol(spe), 1),
  method = c("RUV4", "Limma", "RUVg"),
  isLog = TRUE
)
geomxBatchCorrection(
  spe,
  k,
  factors,
  NCGs,
  n_assay = 2,
  batch = NULL,
  batch2 = NULL,
  covariates = NULL,
  design = matrix(1, ncol(spe), 1),
  method = c("RUV4", "Limma", "RUVg"),
  isLog = TRUE
)

Arguments

`spe`	A Spatial Experiment object.
`k`	The number of unwanted factors to use. Can be 0. This is required for the RUV4 method.
`factors`	Column name(s) to indicate the factors of interest. This is required for the RUV4 method.
`NCGs`	Negative control genes. This is required for the RUV4 method.
`n_assay`	Integer to indicate the nth count table in the assay(spe) to be used.
`batch`	A vector indicating batches. This is required for the Limma method.
`batch2`	A vector indicating the second series of batches. This is specific for the Limma method.
`covariates`	A matrix or vector of numeric covariates to be adjusted for.
`design`	A design matrix relating to treatment conditions to be preserved, can be generated using `stats::model.matrix` function with all biological factors included.
`method`	Can be either RUV4 or Limma or RUVg, by default is RUV4.
`isLog`	Logical vector, indicating if the count table is log or not.

Value

A Spatial Experiment object, containing the normalized count and normalization factor. For method RUV4 and RUVg, the W matrices will be saved in the colData of the object.

Note

The normalised count is not intended to be used directly for linear modelling. For linear modelling, it is better to include the batch factors/W matrices in the linear model.

References

Gagnon-Bartsch, J. A., Jacob, L., & Speed, T. P. (2013). Removing unwanted variation from high dimensional data with negative controls. Berkeley: Tech Reports from Dep Stat Univ California, 1-112.

Ritchie, M. E., Phipson, B., Wu, D. I., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research, 43(7), e47-e47.

Examples

data("dkd_spe_subset")
spe <- findNCGs(dkd_spe_subset, top_n = 100)
spe_ruv <- geomxBatchCorrection(spe,
  k = 3,
  factors = c("disease_status", "region"),
  NCGs = S4Vectors::metadata(spe)$NCGs
)

data("dkd_spe_subset")
spe <- findNCGs(dkd_spe_subset, top_n = 100)
spe_ruv <- geomxBatchCorrection(spe,
  k = 3,
  factors = c("disease_status", "region"),
  NCGs = S4Vectors::metadata(spe)$NCGs
)

Perform normalization to GeoMX data

Description

Perform normalization to GeoMX data

Usage

geomxNorm(
  spe_object,
  method = c("TMM", "RPKM", "TPM", "CPM", "upperquartile", "sizefactor"),
  log = TRUE
)
geomxNorm(
  spe_object,
  method = c("TMM", "RPKM", "TPM", "CPM", "upperquartile", "sizefactor"),
  log = TRUE
)

Arguments

`spe_object`	A SpatialExperiment object.
`method`	Normalization method to use. Options: TMM, RPKM, TPM, CPM, upperquartile, sizefactor. RPKM and TPM require gene length information, which should be added into rowData(spe). Note that TMM here is TMM + CPM.
`log`	Log-transformed or not.

Value

A SpatialExperiment object, with the second assay being the normalized count matrix. The normalised count is stored in the assay slot called "logcounts" by default. With method TMM and sizefactor, the norm.factor will be saved in the metadata of the SpatialExperiment object.

Note

The normalised count is not intended to be used directly for linear modelling. For linear modelling, it is better to include the normalized factors in the "norm.factors" column of the DGEList object.

References

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.

Love, M., Anders, S., & Huber, W. (2014). Differential analysis of count data–the DESeq2 package. Genome Biol, 15(550), 10-1186.

Examples

data("dkd_spe_subset")

spe_tmm <- geomxNorm(dkd_spe_subset, method = "TMM")
spe_upq <- geomxNorm(dkd_spe_subset, method = "upperquartile")
spe_deseqnorm <- geomxNorm(dkd_spe_subset, method = "sizefactor")

data("dkd_spe_subset")

spe_tmm <- geomxNorm(dkd_spe_subset, method = "TMM")
spe_upq <- geomxNorm(dkd_spe_subset, method = "upperquartile")
spe_deseqnorm <- geomxNorm(dkd_spe_subset, method = "sizefactor")

Compare and evaluate different batch corrected data with plotting.

Description

Compare and evaluate different batch corrected data with plotting.

Usage

plotClusterEvalStats(
  spe_list,
  bio_feature_name,
  batch_feature_name,
  data_names,
  colors = NA
)
plotClusterEvalStats(
  spe_list,
  bio_feature_name,
  batch_feature_name,
  data_names,
  colors = NA
)

Arguments

`spe_list`	A list of Spatial Experiment object.
`bio_feature_name`	The common biological variation name.
`batch_feature_name`	The common batch variation name.
`data_names`	Data names.
`colors`	Color values of filing the bars.

Value

A ggplot object.

Examples

library(scater)
data("dkd_spe_subset")
spe <- dkd_spe_subset
spe2 <- spe
spe3 <- spe
plotClusterEvalStats(list(spe, spe2, spe3),
  bio_feature_name = "region",
  batch_feature_name = "SlideName", c("test1", "test2", "test3")
)
library(scater)
data("dkd_spe_subset")
spe <- dkd_spe_subset
spe2 <- spe
spe3 <- spe
plotClusterEvalStats(list(spe, spe2, spe3),
  bio_feature_name = "region",
  batch_feature_name = "SlideName", c("test1", "test2", "test3")
)

Compute and plot the results of any dimension reduction methods on gene expression data

Description

Compute and plot the results of any dimension reduction methods on gene expression data

Usage

plotDR(object, dims = c(1, 2), ...)

## S4 method for signature 'SingleCellExperiment'
plotDR(object, dims, dimred = "PCA", textScale = 1, ...)

## S4 method for signature 'SpatialExperiment'
plotDR(object, dims, dimred = "PCA", textScale = 1, ...)
plotDR(object, dims = c(1, 2), ...)

## S4 method for signature 'SingleCellExperiment'
plotDR(object, dims, dimred = "PCA", textScale = 1, ...)

## S4 method for signature 'SpatialExperiment'
plotDR(object, dims, dimred = "PCA", textScale = 1, ...)

Arguments

`object`	a DGEList, SummarizedExperiment or ExpressionSet object containing gene expression data.
`dims`	a numeric, containing 2 values specifying the dimensions to plot.
`...`	aesthetic mappings to pass to `ggplot2::aes_string()`.
`dimred`	a string or integer scalar indicating the reduced dimension result in `reducedDims(object)` to plot.
`textScale`	a numeric, specifying the relative scale factor to apply to text on the plot.

Value

a ggplot2 object

Examples

library(scater)
data("dkd_spe_subset")
spe <- scater::runPCA(dkd_spe_subset)
plotDR(spe, dimred = "PCA")

library(scater)
data("dkd_spe_subset")
spe <- scater::runPCA(dkd_spe_subset)
plotDR(spe, dimred = "PCA")

Plot gene-wise QC plot

Description

Plot gene-wise QC plot

Usage

plotGeneQC(
  spe,
  top_n = 9,
  ordannots = c(),
  point_size = 1,
  line_type = "dashed",
  line_col = "darkred",
  line_cex = 1,
  hist_col = "black",
  hist_fill = "skyblue",
  bin_num = 30,
  text_size = 13,
  layout_ncol = 1,
  layout_nrow = 2,
  layout_height = c(1, 1),
  ...
)
plotGeneQC(
  spe,
  top_n = 9,
  ordannots = c(),
  point_size = 1,
  line_type = "dashed",
  line_col = "darkred",
  line_cex = 1,
  hist_col = "black",
  hist_fill = "skyblue",
  bin_num = 30,
  text_size = 13,
  layout_ncol = 1,
  layout_nrow = 2,
  layout_height = c(1, 1),
  ...
)

Arguments

`spe`	A SpatialExperiment object.
`top_n`	Integer. Indicating the top n genes will be plotted. Default is 9.
`ordannots`	variables or computations to sort samples by (tidy style).
`point_size`	Numeric. Point size.
`line_type`	Character. Line types for ggplot.
`line_col`	Color for line.
`line_cex`	Cex for line.
`hist_col`	Color for histogram.
`hist_fill`	Fill for histogram.
`bin_num`	Bin numbers for histogram.
`text_size`	Text size.
`layout_ncol`	Integer. Column number for layout. Default is 1.
`layout_nrow`	Integer. Row number for layout. Default is 2.
`layout_height`	Vector of numerics with length of 2. Default is c(1, .4).
`...`	aesthetic mappings to pass to `ggplot2::aes()` of the dot plots.

Value

A ggplot object

Examples

data("dkd_spe_subset")
spe <- addPerROIQC(dkd_spe_subset)
plotGeneQC(spe)
data("dkd_spe_subset")
spe <- addPerROIQC(dkd_spe_subset)
plotGeneQC(spe)

Compute and plot the results of a MDS analysis on gene expression data

Description

Compute and plot the results of a MDS analysis on gene expression data

Usage

plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'DGEList'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'ExpressionSet'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SummarizedExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SingleCellExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SpatialExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'DGEList'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'ExpressionSet'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SummarizedExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SingleCellExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

## S4 method for signature 'SpatialExperiment'
plotMDS(
  object,
  dims = c(1, 2),
  precomputed = NULL,
  textScale = 1,
  assay = 1,
  ...
)

Arguments

`object`	a DGEList, SummarizedExperiment or ExpressionSet object containing gene expression data.
`dims`	a numeric, containing 2 values specifying the dimensions to plot.
`precomputed`	a dimensional reduction results from either `limma::plotMDS`.
`textScale`	a numeric, specifying the relative scale factor to apply to text on the plot.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).
`...`	aesthetic mappings to pass to `ggplot2::aes_string()`.

Value

a ggplot2 object

Examples

data("dkd_spe_subset")
standR::plotMDS(dkd_spe_subset)

data("dkd_spe_subset")
standR::plotMDS(dkd_spe_subset)

Plot pair-wise PCA plots for multiple dimensions

Description

Plot pair-wise PCA plots for multiple dimensions

Usage

plotPairPCA(
  spe_object,
  n_dimension = 3,
  precomputed = NULL,
  assay = 2,
  title = NA,
  title.size = 14,
  rmduplabs = FALSE,
  flipcoord = FALSE,
  ...
)
plotPairPCA(
  spe_object,
  n_dimension = 3,
  precomputed = NULL,
  assay = 2,
  title = NA,
  title.size = 14,
  rmduplabs = FALSE,
  flipcoord = FALSE,
  ...
)

Arguments

`spe_object`	A SpatialExperiment object.
`n_dimension`	The top n dimensions to be plotted
`precomputed`	a dimensional reduction results from `stats::prcomp`. result in `reducedDims(object)` to plot. Default is NULL, we will compute for you.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).
`title`	Character vector, title to put at the top.
`title.size`	Numeric vector, size of the title.
`rmduplabs`	Remove duplicated labels from the plot. FALSE by default.
`flipcoord`	Flip the xy coordinates. FALSE by default.
`...`	aesthetic mappings to pass to `ggplot2::aes()`.

Value

A ggplot object.

Examples

data("dkd_spe_subset")
plotPairPCA(dkd_spe_subset)
data("dkd_spe_subset")
plotPairPCA(dkd_spe_subset)

Plot PCA bi plot

Description

Plot PCA bi plot

Usage

plotPCAbiplot(
  spe_object,
  n_loadings = 10,
  dims = c(1, 2),
  precomputed = NULL,
  assay = 1,
  arrow_x = 0,
  arrow_y = 0,
  ...
)
plotPCAbiplot(
  spe_object,
  n_loadings = 10,
  dims = c(1, 2),
  precomputed = NULL,
  assay = 1,
  arrow_x = 0,
  arrow_y = 0,
  ...
)

Arguments

`spe_object`	A SpatialExperiment object.
`n_loadings`	Plot the top n gene loadings
`dims`	The top n dimensions to be plotted
`precomputed`	a dimensional reduction results from `stats::prcomp`. result in `reducedDims(object)` to plot. Default is NULL, we will compute for you.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).
`arrow_x`	a numeric, indicating the x coordinate of the base of the arrow.
`arrow_y`	a numeric, indicating the y coordinate of the base of the arrow.
`...`	aesthetic mappings to pass to `ggplot2::aes()`.

Value

A ggplot object.

Examples

data("dkd_spe_subset")
plotPCAbiplot(dkd_spe_subset)
data("dkd_spe_subset")
plotPCAbiplot(dkd_spe_subset)

Compute and plot relative log expression (RLE) values of gene expression data

Description

Compute and plot relative log expression (RLE) values of gene expression data

Usage

plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'DGEList'
plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'ExpressionSet'
plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'SummarizedExperiment'
plotRLExpr(object, ordannots, assay = 1, ...)
plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'DGEList'
plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'ExpressionSet'
plotRLExpr(object, ordannots = c(), ...)

## S4 method for signature 'SummarizedExperiment'
plotRLExpr(object, ordannots, assay = 1, ...)

Arguments

`object`	a DGEList, SummarizedExperiment or ExpressionSet object containing gene expression data.
`ordannots`	variables or computations to sort samples by (tidy style).
`...`	aesthetic mappings to pass to `ggplot2::aes_string()`.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).

Value

a ggplot2 object, containing the RLE plot.

Examples

data("dkd_spe_subset")
plotRLExpr(dkd_spe_subset)

data("dkd_spe_subset")
plotRLExpr(dkd_spe_subset)

Plot Sample-wise QC plot

Description

Plot Sample-wise QC plot

Usage

plotROIQC(
  spe_object,
  x_axis = "AOINucleiCount",
  y_axis = "lib_size",
  x_lab = "AOINucleiCount",
  y_lab = "Library size",
  x_threshold = NULL,
  y_threshold = NULL,
  regression_col = "purple",
  hist_col = "black",
  hist_fill = "white",
  bin_num = 50,
  threshold_col = "red",
  threshold_linetype = "dashed",
  layout_ncol = 2,
  layout_nrow = 2,
  leyout_height = c(0.8, 2.5),
  layout_width = c(2.5, 0.8),
  ...
)
plotROIQC(
  spe_object,
  x_axis = "AOINucleiCount",
  y_axis = "lib_size",
  x_lab = "AOINucleiCount",
  y_lab = "Library size",
  x_threshold = NULL,
  y_threshold = NULL,
  regression_col = "purple",
  hist_col = "black",
  hist_fill = "white",
  bin_num = 50,
  threshold_col = "red",
  threshold_linetype = "dashed",
  layout_ncol = 2,
  layout_nrow = 2,
  leyout_height = c(0.8, 2.5),
  layout_width = c(2.5, 0.8),
  ...
)

Arguments

`spe_object`	A SpatialExperiment object.
`x_axis`	Numeric feature to plot as x axis.
`y_axis`	Numeric feature to plot as y axis.
`x_lab`	Label name for x axis.
`y_lab`	Label name for y axis.
`x_threshold`	Threshold to draw.
`y_threshold`	Threshold to draw.
`regression_col`	Color for the regression line.
`hist_col`	Color for the histograms.
`hist_fill`	Fill for the histograms.
`bin_num`	Bin numbers for the histograms.
`threshold_col`	Threshold line color.
`threshold_linetype`	Threshold line type.
`layout_ncol`	Column number layout.
`layout_nrow`	Row number layout.
`leyout_height`	Height layout.
`layout_width`	Width layout.
`...`	aesthetic mappings to pass to `ggplot2::aes()` of the dot plots.

Value

A ggplot object.

Examples

library(ggplot2)
library(patchwork)
data("dkd_spe_subset")
spe <- addPerROIQC(dkd_spe_subset)

plotROIQC(spe)

library(ggplot2)
library(patchwork)
data("dkd_spe_subset")
spe <- addPerROIQC(dkd_spe_subset)

plotROIQC(spe)

Plot the user-defined meta data using alluvium plot

Description

Plot the user-defined meta data using alluvium plot

Usage

plotSampleInfo(spe_object, column2plot, textsize = 3)
plotSampleInfo(spe_object, column2plot, textsize = 3)

Arguments

`spe_object`	A SpatialExperiment object.
`column2plot`	Which columns to plot.
`textsize`	text size.

Value

A ggplot object

Examples

library(ggalluvial)

data("dkd_spe_subset")
plotSampleInfo(dkd_spe_subset, column2plot = c("SlideName", "disease_status", "region"))

library(ggalluvial)

data("dkd_spe_subset")
plotSampleInfo(dkd_spe_subset, column2plot = c("SlideName", "disease_status", "region"))

Plot the PCA scree plot.

Description

Plot the PCA scree plot.

Usage

plotScreePCA(
  spe_object,
  dims = ncol(spe_object),
  precomputed = NULL,
  assay = 1,
  bar_color = "black",
  bar_fill = "royalblue",
  bar_width = 0.8,
  point_col = "tomato3",
  line_col = "tomato3",
  point_size = 2
)
plotScreePCA(
  spe_object,
  dims = ncol(spe_object),
  precomputed = NULL,
  assay = 1,
  bar_color = "black",
  bar_fill = "royalblue",
  bar_width = 0.8,
  point_col = "tomato3",
  line_col = "tomato3",
  point_size = 2
)

Arguments

`spe_object`	A SpatialExperiment object.
`dims`	The top n dimensions to be plotted
`precomputed`	a dimensional reduction results from `stats::prcomp`. result in `reducedDims(object)` to plot. Default is NULL, we will compute for you.
`assay`	a numeric or character, specifying the assay to use (for `SummarizedExperiment` and its derivative classes).
`bar_color`	Color for bar.
`bar_fill`	Fill for bar.
`bar_width`	Bar width.
`point_col`	Color for point.
`line_col`	Color for line.
`point_size`	Point size.

Value

A ggplot object.

Examples

data("dkd_spe_subset")
plotScreePCA(dkd_spe_subset, dims = 10)
data("dkd_spe_subset")
plotScreePCA(dkd_spe_subset, dims = 10)

Preparing the inputs for SpatialDecon for doing deconvolution on spatial data

Description

Preparing the inputs for SpatialDecon for doing deconvolution on spatial data

Usage

prepareSpatialDecon(
  spe,
  assay2use = "logcounts",
  negProbeName = "NegProbe-WTX",
  pool = NA
)
prepareSpatialDecon(
  spe,
  assay2use = "logcounts",
  negProbeName = "NegProbe-WTX",
  pool = NA
)

Arguments

`spe`	SpatialExperiment object.
`assay2use`	The name of the assay to use. By default is logcounts.
`negProbeName`	The name of the negative probe gene. By default is NegProbe-WTX.
`pool`	A vector indicates the pools of the genes. This is required when there are more than one Negative Probes.

Value

A list of two dataframes. The first data.frame is the normalised count, the second data.frame is the background for the data.

Examples

library(ExperimentHub)
eh <- ExperimentHub()

query(eh, "standR")
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]

spe <- readGeoMx(countFile, sampleAnnoFile, rmNegProbe = FALSE)

out <- prepareSpatialDecon(spe)

library(ExperimentHub)
eh <- ExperimentHub()

query(eh, "standR")
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]

spe <- readGeoMx(countFile, sampleAnnoFile, rmNegProbe = FALSE)

out <- prepareSpatialDecon(spe)

Import GeoMX DSP data into a saptial experiment object from file paths

Description

Import GeoMX DSP data into a saptial experiment object from file paths

Usage

readGeoMx(
  countFile,
  sampleAnnoFile,
  featureAnnoFile = NA,
  rmNegProbe = TRUE,
  NegProbeName = "NegProbe-WTX",
  colnames.as.rownames = c("TargetName", "SegmentDisplayName", "TargetName"),
  coord.colnames = c("ROICoordinateX", "ROICoordinateY")
)
readGeoMx(
  countFile,
  sampleAnnoFile,
  featureAnnoFile = NA,
  rmNegProbe = TRUE,
  NegProbeName = "NegProbe-WTX",
  colnames.as.rownames = c("TargetName", "SegmentDisplayName", "TargetName"),
  coord.colnames = c("ROICoordinateX", "ROICoordinateY")
)

Arguments

`countFile`	tsv file or a dataframe object. Count matrix, with samples in columns and features/genes in rows. The first column is gene names/ids.
`sampleAnnoFile`	tsv file or a dataframe object. Sample annotations.
`featureAnnoFile`	tsv file or a dataframe object. Feature/Gene annotations.
`rmNegProbe`	Logical. Default is TRUE, indicating there are negative probe genes in the data.
`NegProbeName`	Character. Name of negative probe genes, default is NegProbe-WTX.
`colnames.as.rownames`	Vector of characters, length of 3. Column names used to capture gene names, sample names and gene names in countFile, sampleAnnoFile and featureAnnoFile, respectively.
`coord.colnames`	Vector of characters, length of 2. Column names used to capture ROI coordinates.

Value

A SpatialExperiment object.

Examples

library(ExperimentHub)

eh <- ExperimentHub()
query(eh, "standR")
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]

spe <- readGeoMx(countFile, sampleAnnoFile, rmNegProbe = FALSE)

library(ExperimentHub)

eh <- ExperimentHub()
query(eh, "standR")
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]

spe <- readGeoMx(countFile, sampleAnnoFile, rmNegProbe = FALSE)

Import GeoMX DSP data into a spatial experiment object from DGEList object

Description

Import GeoMX DSP data into a spatial experiment object from DGEList object

Usage

readGeoMxFromDGE(dge_object, spatialCoord = NULL)
readGeoMxFromDGE(dge_object, spatialCoord = NULL)

Arguments

`dge_object`	a DGEList object (created using edgeR::DGEList).
`spatialCoord`	a matrix with coordinates of samples, rowname must be cosistent with the colnames of dge_object.

Value

A SpatialExperiment object.

Examples

# making a simple DGEList object
ng <- 1000
ns <- 10
Counts <- matrix(rnbinom(ng * ns, mu = 5, size = 2), ng, ns)
rownames(Counts) <- seq(ng)
y <- edgeR::DGEList(counts = Counts, group = rep(seq(2), each = 5))

# transfer into spatial experiment object
coords <- matrix(rnorm(2 * ns), 10, 2)
spe <- readGeoMxFromDGE(dge_object = y, spatialCoord = coords)
spe

# making a simple DGEList object
ng <- 1000
ns <- 10
Counts <- matrix(rnbinom(ng * ns, mu = 5, size = 2), ng, ns)
rownames(Counts) <- seq(ng)
y <- edgeR::DGEList(counts = Counts, group = rep(seq(2), each = 5))

# transfer into spatial experiment object
coords <- matrix(rnorm(2 * ns), 10, 2)
spe <- readGeoMxFromDGE(dge_object = y, spatialCoord = coords)
spe

Transfer SpatialExperiment object into DGEList object for DE analysis

Description

Transfer SpatialExperiment object into DGEList object for DE analysis

Usage

spe2dge(spe)
spe2dge(spe)

Arguments

spe

SpatialExperiment object.

Value

A DGEList.

Examples

data("dkd_spe_subset")


spe_tmm <- geomxNorm(dkd_spe_subset, method = "TMM")
dge <- spe2dge(spe_tmm)

data("dkd_spe_subset")


spe_tmm <- geomxNorm(dkd_spe_subset, method = "TMM")
dge <- spe2dge(spe_tmm)

Package 'standR'

Help Index

Add QC statistics to the Spatial Experiment object

Description

Usage

Arguments

Value

Examples

Calculate statistics for evaluating batch correction

Description

Usage

Arguments

Value

Examples

Compute and plot the results of a PCA analysis on gene expression data

Description

Usage

Arguments

Value

Examples

Testing multiple K for RUV4 batch correction to find the best K.

Description

Usage

Arguments

Value

Examples

Get negative control genes from each batch of the data

Description

Usage

Arguments

Value

Examples

Batch correction for GeoMX data

Description

Usage

Arguments

Value

Note

References

Examples

Perform normalization to GeoMX data

Description

Usage

Arguments

Value

Note

References

Examples

Compare and evaluate different batch corrected data with plotting.

Description

Usage

Arguments

Value

Examples

Compute and plot the results of any dimension reduction methods on gene expression data

Description

Usage

Arguments

Value

Examples

Plot gene-wise QC plot

Description

Usage

Arguments

Value

Examples

Compute and plot the results of a MDS analysis on gene expression data

Description

Usage

Arguments

Value

Examples

Plot pair-wise PCA plots for multiple dimensions

Description

Usage

Arguments

Value

Examples

Plot PCA bi plot

Description