In data2intelligence/SpaCE: Spatial Cellular Estimator for Tumors

output: github_document

knitr::opts_chunk$set(
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In this vignette, we will demonstrate how to use SpaCET to calculate and visualize spots' gene set score for the in-house or user-defined gene sets. The in-house gene sets include hallmarks, cancer cell states, and tertiary lymphoid structures (TLS).

Create SpaCET object

To read your ST data into R, user can create an SpaCET object by using create.SpaCET.object or create.SpaCET.object.10X. Specifically, if users are analyzing an ST dataset from 10x Visium, they only need to input "visiumPath" by using create.SpaCET.object.10X. Please make sure that "visiumPath" points to the standard output folders of 10x Space Ranger, which has both "filtered_feature_bc_matrix" and "spatial" folders.

library(SpaCET)

# set the path to the in-house breast cancer ST data. User can set the paths to their own data.
visiumPath <- file.path(system.file(package = "SpaCET"), "extdata/Visium_BC")

# load ST data to create an SpaCET object.
SpaCET_obj <- create.SpaCET.object.10X(visiumPath = visiumPath)

# calculate the QC metrics
SpaCET_obj <- SpaCET.quality.control(SpaCET_obj)

Calculate hallmark score

The in-house 50 hallmark gene sets were collected from MSigDB{target="_blank"} and user just need to set GeneSets as "Hallmark". SpaCET.GeneSetScore will call UCell package to calculate gene set score. The results are stored in SpaCET_obj@results$GeneSetScore as a matrix.

# run gene set calculation
SpaCET_obj <- SpaCET.GeneSetScore(SpaCET_obj, GeneSets="Hallmark")

# show results
SpaCET_obj@results$GeneSetScore[1:6,1:6]

# show all gene sets
rownames(SpaCET_obj@results$GeneSetScore)

# visualize two gene sets
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "GeneSetScore", 
  spatialFeatures = c("HALLMARK_HYPOXIA","HALLMARK_TGF_BETA_SIGNALING")
)

Calculate cancer cell state score

A recent study{target="_blank"} identifies a catalog of gene modules whose expression defines 16 recurrent cancer cell states through a pan-caner single-cell RNA-sequencing analysis. In order to use them, user just need to set GeneSets as "CancerCellState".

# run gene set calculation
SpaCET_obj <- SpaCET.GeneSetScore(SpaCET_obj, GeneSets="CancerCellState")

# show all gene sets
rownames(SpaCET_obj@results$GeneSetScore)

# visualize two gene sets
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "GeneSetScore", 
  spatialFeatures = c("CancerCellState_Cycle","CancerCellState_cEMT")
)

Calculate TLS score

User just need to set GeneSets as "TLS" to calculate tertiary lymphoid structure score. We collected a 30-gene TLS signature from this study{target="_blank"}.

# run gene set calculation
SpaCET_obj <- SpaCET.GeneSetScore(SpaCET_obj, GeneSets="TLS")

# visualize TLS
SpaCET.visualize.spatialFeature(
  SpaCET_obj, 
  spatialType = "GeneSetScore", 
  spatialFeatures = c("TLS")
)

Calculate other gene sets' score

There are two methods to build customized gene sets. 1) Generate a list to define some simple gene sets (e.g., T cell and Myeloid signatures). 2) Download a gmt file from MSigDB{target="_blank"} (e.g., GO Ontology), and then use read.gmt to read this file as a list.

# 1)
gmt1 <- list(
  Tcell = c("CD2","CD3E","CD3D"), 
  Myeloid = c("SPI1","FCER1G","CSF1R")
)
SpaCET_obj <- SpaCET.GeneSetScore(SpaCET_obj, GeneSets = gmt1)

# 2)
gmt2 <- read.gmt("Path_to_gmt_file")
SpaCET_obj <- SpaCET.GeneSetScore(SpaCET_obj, GeneSets = gmt2)