Identify differentially abundant features across image

calc_spatial_diff_ex() Calculates differential expression analysis using annotations in a SpatialExperiment object. Differential expression is calculated using functionality from the limma package.

calc_spatial_diff_ex(
  spe,
  assay_name = "proteomics",
  count_based = FALSE,
  log_transformed = FALSE,
  category_col,
  compare_vals
)

Arguments

spe: Spatial Experiment object containing data to be used for differential expression analysis
assay_name: Name of the dataset stored in the spe object, that is to be used for the differential expression analysis. Example: znormalized_log2
count_based: Set to TRUE of the data are count based, e.g. RNA-Seq
log_transformed: Is the data given in spe log2 transformed TRUE or FALSE
category_col: Name of the column that specifies category of each sample. Example: "IsletOrNot" #Categories from category_col will be compared in the differential expression analysis
compare_vals: A vector containing names of categories from category_col to be compared. Only required if there are more than two values in category_col

Value

A Spatial Experiment object containing differential expression results, stored in rowData(diffEx.spe) and assays(diffEx.spe) which contains the dataset on which differential expresssion analysis was carried out

Examples

data(smallPancData)
data(pancMeta)
data(protMeta)
pooledData <- dplyr::bind_cols(smallPancData)
pooled.panc.spe <- convert_to_spe(pooledData,
  pancMeta,
  protMeta,
  feature_meta_colname = "pancProts"
)
#> Spatial object created without spatial coordinate 
#>          column names provided. Distance based analysis will not be enabled.
#> Note: Only mapping metadata for 2986 features out of 3000 data points
diffex.spe <- calc_spatial_diff_ex(pooled.panc.spe,
  category_col = "IsletOrNot"
)
#> Warning: Partial NA coefficients for 2 probe(s)
#> We found 0 features with a logFC greater than 1 and 
#>                  an ajusted p-value less than 0.05