## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----installation, eval=FALSE-------------------------------------------------
#
# if(!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("SpatialDecon")
#
## ----setup--------------------------------------------------------------------
library(SpatialDecon)
library(SeuratObject)
## ----loaddata-----------------------------------------------------------------
# download data:
con <- gzcon(url("https://github.com/almaan/her2st/raw/master/data/ST-cnts/G1.tsv.gz"))
txt <- readLines(con)
temp <- read.table(textConnection(txt), sep = "\t", header = TRUE, row.names = 1)
# parse data
raw = t(as.matrix(temp))
norm = sweep(raw, 2, colSums(raw), "/") * mean(colSums(raw))
x = as.numeric(substr(rownames(temp), 1, unlist(gregexpr("x", rownames(temp))) - 1))
y = -as.numeric(substr(rownames(temp), unlist(gregexpr("x", rownames(temp))) + 1, nchar(rownames(temp))))
# put into a seurat object:
andersson_g1 = CreateSeuratObject(counts = raw, assay="Spatial")
andersson_g1@meta.data$x = x
andersson_g1@meta.data$y = y
## ----showsafetme, fig.height=5, fig.width=10, fig.cap = "The safeTME cell profile matrix"----
data("safeTME")
data("safeTME.matches")
signif(safeTME[seq_len(3), seq_len(3)], 2)
heatmap(sweep(safeTME, 1, apply(safeTME, 1, max), "/"),
labRow = NA, margins = c(10, 5))
## ----downloadmatrix, fig.height=7, fig.width=10, fig.cap = "The Mouse Spleen profile matrix", eval=T----
mousespleen <- download_profile_matrix(species = "Mouse",
age_group = "Adult",
matrixname = "Spleen_MCA")
dim(mousespleen)
mousespleen[1:4,1:4]
head(cellGroups)
metadata
heatmap(sweep(mousespleen, 1, apply(mousespleen, 1, max), "/"),
labRow = NA, margins = c(10, 5), cexCol = 0.7)
## ----single cell data---------------------------------------------------------
data("mini_singleCell_dataset")
mini_singleCell_dataset$mtx@Dim # genes x cells
as.matrix(mini_singleCell_dataset$mtx)[1:4,1:4]
head(mini_singleCell_dataset$annots)
table(mini_singleCell_dataset$annots$LabeledCellType)
## ----creatematrix, fig.height=7, fig.width=10, fig.cap = "Custom profile matrix"----
custom_mtx <- create_profile_matrix(mtx = mini_singleCell_dataset$mtx, # cell x gene count matrix
cellAnnots = mini_singleCell_dataset$annots, # cell annotations with cell type and cell name as columns
cellTypeCol = "LabeledCellType", # column containing cell type
cellNameCol = "CellID", # column containing cell ID/name
matrixName = "custom_mini_colon", # name of final profile matrix
outDir = NULL, # path to desired output directory, set to NULL if matrix should not be written
normalize = FALSE, # Should data be normalized?
minCellNum = 5, # minimum number of cells of one type needed to create profile, exclusive
minGenes = 10, # minimum number of genes expressed in a cell, exclusive
scalingFactor = 5, # what should all values be multiplied by for final matrix
discardCellTypes = TRUE) # should cell types be filtered for types like mitotic, doublet, low quality, unknown, etc.
head(custom_mtx)
heatmap(sweep(custom_mtx, 1, apply(custom_mtx, 1, max), "/"),
labRow = NA, margins = c(10, 5), cexCol = 0.7)
## ----createSeuratmatrix-------------------------------------------------------
library(SeuratObject)
data("mini_singleCell_dataset")
rownames(mini_singleCell_dataset$annots) <- mini_singleCell_dataset$annots$CellID
seuratObject <- CreateSeuratObject(counts = mini_singleCell_dataset$mtx, meta.data = mini_singleCell_dataset$annots)
Idents(seuratObject) <- seuratObject$LabeledCellType
rm(mini_singleCell_dataset)
annots <- data.frame(cbind(cellType=as.character(Idents(seuratObject)),
cellID=names(Idents(seuratObject))))
custom_mtx_seurat <- create_profile_matrix(mtx = seuratObject@assays$RNA@counts,
cellAnnots = annots,
cellTypeCol = "cellType",
cellNameCol = "cellID",
matrixName = "custom_mini_colon",
outDir = NULL,
normalize = FALSE,
minCellNum = 5,
minGenes = 10)
head(custom_mtx_seurat)
paste("custom_mtx and custom_mtx_seurat are identical", all(custom_mtx == custom_mtx_seurat))
## ----runiss-------------------------------------------------------------------
res = runspatialdecon(object = andersson_g1,
bg = 0.01,
X = safeTME,
align_genes = TRUE)
str(res)
## ----plotissres, fig.height = 5, fig.width = 8, fig.cap = "Cell abundance estimates"----
heatmap(res$beta, cexCol = 0.5, cexRow = 0.7, margins = c(10,7))
## ----showmatches--------------------------------------------------------------
str(safeTME.matches)
## ----puretumor----------------------------------------------------------------
sharedgenes = intersect(rownames(raw), rownames(safeTME))
plot(colSums(raw), colSums(raw[sharedgenes, ]), log = "xy")
hist(colSums(raw[sharedgenes, ]) / colSums(raw), breaks = 20)
alltumor = colSums(raw[sharedgenes, ]) / colSums(raw) < 0.03 # for alma data
table(alltumor)
## ----wts----------------------------------------------------------------------
sd_from_noise <- runErrorModel(counts = raw, platform = "st")
wts <- 1 / sd_from_noise
## ----runisstils---------------------------------------------------------------
# run spatialdecon with all the bells and whistles:
restils = runspatialdecon(object = andersson_g1, # Seurat object
X = safeTME, # safeTME matrix, used by default
bg = 0.01, # Recommended value of 0.01 in Visium/ST data
wts = wts, # weight
cellmerges = safeTME.matches, # safeTME.matches object, used by default
is_pure_tumor = alltumor, # identities of the Tumor segments/observations
n_tumor_clusters = 5) # how many distinct tumor profiles to append to safeTME
str(restils)
## ----shownewX, fig.height=5, fig.width=8, fig.cap = "safeTME merged with newly-derived tumor profiles"----
heatmap(sweep(restils$X, 1, apply(restils$X, 1, max), "/"),
labRow = NA, margins = c(10, 5))
## ----florets, fig.width=8, fig.height=6, fig.cap = "TIL abundance plotted on PC space"----
# PCA of the normalized data:
pc = prcomp(t(log2(pmax(norm, 1))))$x[, c(1, 2)]
# run florets function:
par(mar = c(5,5,1,1))
layout(mat = (matrix(c(1, 2), 1)), widths = c(6, 2))
florets(x = pc[, 1], y = pc[, 2],
b = restils$beta, cex = .5,
xlab = "PC1", ylab = "PC2")
par(mar = c(0,0,0,0))
frame()
legend("center", fill = cellcols[rownames(restils$beta)],
legend = rownames(restils$beta), cex = 0.7)
## ----floretsxy, fig.width=8, fig.height=6, fig.cap = "TIL abundance plotted on physical space"----
# and plot florets in space:
par(mar = c(5,5,1,1))
layout(mat = (matrix(c(1, 2), 1)), widths = c(6, 2))
florets(x = x, y = y,
b = restils$beta, cex = .5,
xlab = "", ylab = "")
par(mar = c(0,0,0,0))
frame()
legend("center", fill = cellcols[rownames(restils$beta)],
legend = rownames(restils$beta), cex = 0.7)
## ----collapse, fig.width=5, fig.height=5, fig.cap="Cell abundance estimates with related cell types collapsed"----
matching = list()
matching$myeloid = c( "macrophages", "monocytes", "mDCs")
matching$T.NK = c("CD4.T.cells","CD8.T.cells", "Treg", "NK")
matching$B = c("B")
matching$mast = c("mast")
matching$neutrophils = c("neutrophils")
matching$stroma = c("endothelial.cells", "fibroblasts")
collapsed = collapseCellTypes(fit = restils,
matching = matching)
heatmap(collapsed$beta, cexRow = 0.85, cexCol = 0.75)
## ----sessioninfo--------------------------------------------------------------
sessionInfo()