1 Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("standR")

The development version of standR can be installed from GitHub:

devtools::install_github("DavisLaboratory/standR")

2 Quick start

library(standR)
library(SpatialExperiment)
library(limma)
library(ExperimentHub)

2.1 Load data for this guide

This is the background for the data:

NanoString GeoMx DSP dataset of diabetic kidney disease (DKD) vs healthy kidney tissue. Seven slides were analyzed, 4 DKD and 3 healthy. Regions of Interest (ROI) were focused two different parts of a kidney’s structure: tubules or glomeruli. Individual glomeruli were identified by a pathologist as either relatively healthy or diseased regardless if the tissue was DKD or healthy. Tubule ROIs were segmented into distal (PanCK) and proximal (neg) tubules. While both distal and proximal tubules are called tubules, they perform very different functions in the kidney.

eh <- ExperimentHub()

query(eh, "standR")
## ExperimentHub with 3 records
## # snapshotDate(): 2024-10-24
## # $dataprovider: Nanostring
## # $species: NA
## # $rdataclass: data.frame
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## #   rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH7364"]]' 
## 
##            title                   
##   EH7364 | GeomxDKDdata_count      
##   EH7365 | GeomxDKDdata_sampleAnno 
##   EH7366 | GeomxDKDdata_featureAnno
countFile <- eh[["EH7364"]]
sampleAnnoFile <- eh[["EH7365"]]
featureAnnoFile <- eh[["EH7366"]]

spe <- readGeoMx(countFile, sampleAnnoFile, featureAnnoFile = featureAnnoFile, rmNegProbe = TRUE)

2.2 QC

2.2.1 metadata visualization

Based on the description of the data, we know that all glomerulus are classified as abnormal and healthy, and tubule are classified as neg and PanCK.

We therefore merge the region-related annotations to avoid collinearity, which can affect the process of batch correction.

colData(spe)$regions <- paste0(colData(spe)$region,"_",colData(spe)$SegmentLabel) |> 
  (\(.) gsub("_Geometric Segment","",.))() |>
  paste0("_",colData(spe)$pathology) |>
  (\(.) gsub("_NA","_ns",.))()

library(ggalluvial)

plotSampleInfo(spe, column2plot = c("SlideName","disease_status","regions"))

2.2.2 Gene level QC

spe <- addPerROIQC(spe, rm_genes = TRUE)
plotGeneQC(spe, ordannots = "regions", col = regions, point_size = 2)