identify_markers: identify_markers computes differential observation expression...

In patrickCNMartin/Vesalius: Vesalius: Dissecting Tissue Anatomy from Spatial Transcriptomic Data

identify_markers computes differential observation expression between selected territories.

Description

identify_markers computes differential observation expression between selected territories.

Usage

identify_markers(
  vesalius_assay,
  trial = "last",
  norm_method = "last",
  seed = NULL,
  query = NULL,
  cells = NULL,
  sample = FALSE,
  method = "wilcox",
  log_fc = 0.25,
  pval = 0.05,
  min_pct = 0.05,
  min_spatial_index = 10,
  genes = NULL,
  verbose = TRUE,
  ...
)

Arguments

vesalius_assay	a vesalius_assay
trial	character string - which territory trial that should be used to select territorires. Default is last one computed
norm_method	charcater string - which normalisation method should be used.
seed	Integer or vector of integers describing territories to be included in group 1 for differential gene expression analysis.
query	Integer or vector of integers describing territories to be included in group 2 for differential gene expression analysis. Default = NULL
cells	character vector containing barcodes of cells of interest.
sample	logical
method	character describing the statistical test to use in order to extract differantial gene expression. Select from: "wilcox", "t.test", "chisq", "fisher.exact", "DEseq2", "QLF", "LRT","logit"
log_fc	numeric describing minimum log fold change value for differential gene expression. Default set at 0.25.
pval	numeric for pval threshold. Default set at 0.05
min_pct	numeric defining the minimum percentage of cells that should contain any given gene. Deault set at 0.05
min_spatial_index	integer defining minimum number of barcodes in a territory.
genes	character vector - vector of gene names to use directly for DEG analysis.
verbose	logical - progress message output
...	other parameters parsed to DESeq2 or edgeR (not functional)

Details

Identifying markers is a key aspect of spatial data analysis. This functions let's you select which territory trial you which to use. Note that this can be any territory trial that you have run, including color segments, isolated territories, dilated or eroded territories and layered territories. By default, identify_markers takes the last one that has been computed.

The normalisation method refers to the normalisation method applied to the count matrices. If you use, DESeq2, QLF (edgeR) or LRT (edgeR), raw counts will be selected and will ignore any cother command. This is a requirement for both of these packages.

If you have some cells you are interested in comparing between territories, you can simply parse a character vector containing the barcodes of your cells of interest. identify_markers will automatically retrieve cells in each territory and only use these cell for comparison.

Once the Differentially expressed genes/oberservations have been computed they are stored in the vesalius_assay object. This allows you to run multiple trial and have all these trials sorted within your object.

To retrieve them from the object, you can use get_markers

Value

a vesalius_assay object

Examples

## Not run: 
data(vesalius)
# First we build a simple object
ves <- build_vesalius_object(coordinates, counts)
# We can do a simple run
ves <- build_vesalius_embeddings(ves)

# simple smoothing
ves <- smooth_image(ves, dimensions = seq(1, 30))

# quick segmentation
ves <- segment_image(ves, dimensions = seq(1, 30))

# isolate territories
ves <- isolate_territories(ves)

# identify markers
ves <- identify_markers(ves, seed = c(3,5), query = 8)
deg <- get_markers(ves)

## End(Not run)

patrickCNMartin/Vesalius documentation built on April 17, 2025, 11:31 p.m.