Application to high resolution spatial transcriptomics data

This tutorial demonstrates how to apply SpaCET to a colorectal cancer Slide-seq dataset from Zhao et al, 2021. Each bead is 10 µm in diameter covering 1-2 cells. Before running the tutorial, make sure that the SpaCET package and its dependencies have been installed.

Create SpaCET object

User need create.SpaCET.object to create an SpaCET object by preparing four types of input data referring to a tumor ST sample.

1. spatial transcriptomics count data. The spatial transcriptomics count data must be in the format of matrix with gene name (row) x spot ID (column).
2. spatial location information. The spot coordinates should be in the format of matrix with spot ID (row) x coordinates (column). This 1st and 2nd columns represent X and Y coordinates, respectively.
3. path to the H&E image file. The image path can be NA if unavailable.
4. platform.

library(SpaCET)

hiresST_Path <- system.file("extdata", 'hiresST_CRC', package = 'SpaCET')

# load count matrix
load(paste0(hiresST_Path,"/counts.rda"))

# show count matrix
counts[1:6,1:3]

## 6 x 3 sparse Matrix of class "dgCMatrix"
##         2364.115x3072.225 3484.52x1734.005 2279.745x2951.975
## A1BG                    .                .                 .
## A1CF                    .                1                 2
## A2M                     .                .                 .
## A2ML1                   .                .                 .
## A3GALT2                 .                .                 .
## A4GALT                  .                .                 .

# load count matrix
load(paste0(hiresST_Path,"/spotCoordinates.rda"))

# show count matrix
head(spotCoordinates)

##                          X        Y
## 2364.115x3072.225 2364.115 3072.225
## 3484.52x1734.005  3484.520 1734.005
## 2279.745x2951.975 2279.745 2951.975
## 3061.825x1556.49  3061.825 1556.490
## 2274.22x2982.525  2274.220 2982.525
## 2986.945x1603.68  2986.945 1603.680

# create an SpaCET object.
SpaCET_obj <- create.SpaCET.object(
  counts=counts,
  spotCoordinates=spotCoordinates,
  imagePath=NA,
  platform = "SlideSeq"
)

# show this object.
str(SpaCET_obj)

Deconvolve ST data

We use the in-house reference to deconvolve this ST sample.

# deconvolve ST data
SpaCET_obj <- SpaCET.deconvolution(SpaCET_obj, cancerType="CRC", coreNo=8)
# Since Windows does not support parallel computation, please set coreNo=1 for Windows OS.

# show the ST deconvolution results
SpaCET_obj@results$deconvolution$propMat[1:13,1:3]

##             2364.115x3072.225 3484.52x1734.005 2279.745x2951.975
## Malignant                   1                1                 1
## CAF                         0                0                 0
## Endothelial                 0                0                 0
## Plasma                      0                0                 0
## B cell                      0                0                 0
## T CD4                       0                0                 0
## T CD8                       0                0                 0
## NK                          0                0                 0
## cDC                         0                0                 0
## pDC                         0                0                 0
## Macrophage                  0                0                 0
## Mast                        0                0                 0
## Neutrophil                  0                0                 0

Visualize the cell type proportion

We provide SpaCET.visualize.spatialFeature to present the spatial distribution of cell types. spatialFeatures could be a vector of cell types. If user would like to visualize multiple cell types in a single panel, you can assign a list to spatialFeatures. For example, combine CAF and endothelial cell types into stromal cell.

# show the spatial distribution of malignant cells and macrophages.
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "CellFraction", 
  spatialFeatures = list(Malignant=c("Malignant"),Stromal=c("CAF","Endothelial")),
  sameScaleForFraction = TRUE,
  pointSize = 0.6
)

User can check the most abundant cell type in each bead by setting spatialType as “MostAbundantCellType”. spatialFeatures could be “MajorLineage” or “SubLineage”.

# load the color for cell types
load(paste0(hiresST_Path,"/colors_vector.rda"))

# show colors
head(colors_vector)

## Malignant            CAF    Endothelial Unidentifiable         B cell            cDC 
## "#f3c300"      "#be0032"      "#0067a5"      "#e25822"      "#008856"      "#782AB6" 

# show the spatial distribution of all cell types.
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "MostAbundantCellType", 
  spatialFeatures = "MajorLineage", 
  colors = colors_vector,
  pointSize = 0.6
)