PRECAST: Human Breast Cancer Data Analysis

Wei Liu

2022-10-18

This vignette introduces the PRECAST workflow for the analysis of integrating multiple spatial transcriptomics datasets. The workflow consists of three steps

• Independent preprocessing and model setting
• Probabilistic embedding, clustering and alignment using PRECAST model
• Downstream analysis (i.e. visualization of clusters and embeddings, combined differential expression analysis)

We demonstrate the use of PRECAST to two sliced human breast cancer Visium data that are here, which can be downloaded to the current working path by the following command:

githubURL <- "https://github.com/feiyoung/PRECAST/blob/main/vignettes_data/bc2.rda?raw=true"
download.file(githubURL, "bc2.rda", mode = "wb")

Then load to R

load("bc2.rda")

This data is also available at 10X genomics data website:

• Section1: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1
• Section2: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_2

Users require the two folders for each dataset: spatial and filtered_feature_bc_matrix. Then the data can be read by the following commond.

# library(DR.SC) dir.file <- 'Section' ## the folders Section1 and Section2, and each includes
# two folders spatial and filtered_feature_bc_matrix seuList <- list() for (r in 1:2) {
# message('r = ', r) seuList[[r]] <- read10XVisium(paste0(dir.file, r)) } bc2 <- seuList

The package can be loaded with the command:

library(PRECAST)
library(Seurat)

bc2  ## a list including two Seurat object

head(bc2[[1]])

## Create PRECASTObject.
set.seed(2022)
PRECASTObj <- CreatePRECASTObject(bc2)

## check the number of genes/features after filtering step
PRECASTObj@seulist

## Add adjacency matrix list for a PRECASTObj object to prepare for PRECAST model fitting.
PRECASTObj <- AddAdjList(PRECASTObj, platform = "Visium")

## Add a model setting in advance for a PRECASTObj object. verbose =TRUE helps outputing the
## information in the algorithm.
PRECASTObj <- AddParSetting(PRECASTObj, Sigma_equal = FALSE, verbose = TRUE, int.model = NULL)

### Given K
PRECASTObj <- PRECAST(PRECASTObj, K = 14)

## backup the fitting results in resList
resList <- PRECASTObj@resList
PRECASTObj <- selectModel(PRECASTObj)

seuInt <- IntegrateSpaData(PRECASTObj, species = "Human")
seuInt
## The low-dimensional embeddings obtained by PRECAST are saved in PRECAST reduction slot.

cols_cluster <- c("#FD7446", "#709AE1", "#31A354", "#9EDAE5", "#DE9ED6", "#BCBD22", "#CE6DBD", "#DADAEB",
    "yellow", "#FF9896", "#91D1C2", "#C7E9C0", "#6B6ECF", "#7B4173")
p12 <- SpaPlot(seuInt, batch = NULL, point_size = 1, cols = cols_cluster, combine = TRUE)
p12
# users can plot each sample by setting combine=FALSE

seuInt <- AddUMAP(seuInt)
p13 <- SpaPlot(seuInt, batch = NULL, item = "RGB_UMAP", point_size = 2, combine = TRUE, text_size = 15)
p13
# seuInt <- AddTSNE(seuInt) SpaPlot(seuInt, batch=NULL,item='RGB_TSNE',point_size=2,
# combine=T, text_size=15)

seuInt <- AddTSNE(seuInt, n_comp = 2)
library(patchwork)
p1 <- dimPlot(seuInt, item = "cluster", point_size = 0.5, font_family = "serif", cols = cols_cluster)  # Times New Roman
p2 <- dimPlot(seuInt, item = "batch", point_size = 0.5, font_family = "serif")
p1 + p2

library(Seurat)
dat_deg <- FindAllMarkers(seuInt)
library(dplyr)
n <- 10
dat_deg %>%
    group_by(cluster) %>%
    top_n(n = n, wt = avg_log2FC) -> top10

seuInt <- ScaleData(seuInt)
seus <- subset(seuInt, downsample = 400)

color_id <- as.numeric(levels(Idents(seus)))
library(ggplot2)
## HeatMap
p1 <- doHeatmap(seus, features = top10$gene, cell_label = "Domain", grp_label = F, grp_color = cols_cluster[color_id],
    pt_size = 6, slot = "scale.data") + theme(legend.text = element_text(size = 10), legend.title = element_text(size = 13,
    face = "bold"), axis.text.y = element_text(size = 5, face = "italic", family = "serif"))
p1

sessionInfo()