一年了，我们都需要总结，这一篇我们回顾一下SPOTlight，非常好的方法，建议大家也总结一下，看看2021年，自己都得到了什么。
SPOTlight 的目标是提供一种工具，能够对包含细胞混合物的每个捕获位置中存在的细胞类型和细胞类型比例进行解卷积，最初是为 10X 的 Visium - 空间转录组学技术开发的， it can be used for all technologies returning mixtures of cells。 SPOTlight 基于通过 NMFreg 模型为每种细胞类型查找topics profiles singatures，并找到最适合我们想要解卷积的spot的组合。
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Libraries
library(Matrix)
library(data.table)
library(Seurat)
library(SeuratData)
library(dplyr)
library(gt)
library(SPOTlight)
library(igraph)
library(RColorBrewer)
Load data
Load single-cell reference dataset.
path_to_data <- system.file(package = "SPOTlight")
cortex_sc <- readRDS(glue::glue("{path_to_data}/allen_cortex_dwn.rds"))
Load Spatial data
if (! "stxBrain" %in% SeuratData::AvailableData()[, "Dataset"]) {
# If dataset not downloaded proceed to download it
SeuratData::InstallData("stxBrain")
}
# Load data
anterior <- SeuratData::LoadData("stxBrain", type = "anterior1")
Pre-processing,设置种子的功能大家还知道么？？
set.seed(123)
cortex_sc <- Seurat::SCTransform(cortex_sc, verbose = FALSE) %>%
Seurat::RunPCA(., verbose = FALSE) %>%
Seurat::RunUMAP(., dims = 1:30, verbose = FALSE)
Visualize the clustering
Seurat::DimPlot(cortex_sc,
group.by = "subclass",
label = TRUE) + Seurat::NoLegend()
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Descriptive
cortex_sc@meta.data %>%
dplyr::count(subclass) %>%
gt::gt(.[-1, ]) %>%
gt::tab_header(
title = "Cell types present in the reference dataset",
) %>%
gt::cols_label(
subclass = gt::html("Cell Type")
)
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Compute marker genes
为了确定最重要的标记基因，我们可以使用函数 Seurat::FindAllMarkers，它将返回每个cluster的标记。
Seurat::Idents(object = cortex_sc) <- cortex_sc@meta.data$subclass
cluster_markers_all <- Seurat::FindAllMarkers(object = cortex_sc,
assay = "SCT",
slot = "data",
verbose = TRUE,
only.pos = TRUE)
saveRDS(object = cluster_markers_all,
file = here::here("inst/markers_sc.RDS"))
SPOTlight Decomposition
set.seed(123)
spotlight_ls <- spotlight_deconvolution(
se_sc = cortex_sc,
counts_spatial = anterior@assays$Spatial@counts,
clust_vr = "subclass", # Variable in sc_seu containing the cell-type annotation
cluster_markers = cluster_markers_all, # Dataframe with the marker genes
cl_n = 100, # number of cells per cell type to use
hvg = 3000, # Number of HVG to use
ntop = NULL, # How many of the marker genes to use (by default all)
transf = "uv", # Perform unit-variance scaling per cell and spot prior to factorzation and NLS
method = "nsNMF", # Factorization method
min_cont = 0 # Remove those cells contributing to a spot below a certain threshold
)
saveRDS(object = spotlight_ls, file = here::here("inst/spotlight_ls.rds"))
Read RDS object
spotlight_ls <- readRDS(file = here::here("inst/spotlight_ls.rds"))
nmf_mod <- spotlight_ls[[1]]
decon_mtrx <- spotlight_ls[[2]]
Assess deconvolution
Before even looking at the decomposed spots we can gain insight on how well the model performed by looking at the topic profiles for the cell types.
The first thing we can do is look at how specific the topic profiles are for each cell type.
h <- NMF::coef(nmf_mod[[1]])
rownames(h) <- paste("Topic", 1:nrow(h), sep = "_")
topic_profile_plts <- SPOTlight::dot_plot_profiles_fun(
h = h,
train_cell_clust = nmf_mod[[2]])
topic_profile_plts[[2]] + ggplot2::theme(
axis.text.x = ggplot2::element_text(angle = 90),
axis.text = ggplot2::element_text(size = 12))
图片.png
接下来我们可以看看每个细胞类型中每个细胞的各个topic profiles的行为。
在这里，我们期望来自同一细胞类型的所有细胞显示出相似的topic profiles分布，否则该cluster中可能会有更多的子结构，我们可能只捕获其中一个。
topic_profile_plts[[1]] + theme(axis.text.x = element_text(angle = 90),
axis.text = element_text(size = 12))
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Lastly we can take a look at which genes are the most important for each topic and therefore get an insight into which genes are driving them.
basis_spotlight <- data.frame(NMF::basis(nmf_mod[[1]]))
colnames(basis_spotlight) <- unique(stringr::str_wrap(nmf_mod[[2]], width = 30))
basis_spotlight %>%
dplyr::arrange(desc(Astro)) %>%
round(., 5) %>%
DT::datatable(., filter = "top")
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Visualization
Join decomposition with metadata
# This is the equivalent to setting min_cont to 0.04
decon_mtrx_sub <- decon_mtrx[, colnames(decon_mtrx) != "res_ss"]
decon_mtrx_sub[decon_mtrx_sub < 0.08] <- 0
decon_mtrx <- cbind(decon_mtrx_sub, "res_ss" = decon_mtrx[, "res_ss"])
rownames(decon_mtrx) <- colnames(anterior)
decon_df <- decon_mtrx %>%
data.frame() %>%
tibble::rownames_to_column("barcodes")
anterior@meta.data <- anterior@meta.data %>%
tibble::rownames_to_column("barcodes") %>%
dplyr::left_join(decon_df, by = "barcodes") %>%
tibble::column_to_rownames("barcodes")
Specific cell-types
we can use the standard Seurat::SpatialFeaturePlot to view predicted celltype proportions one at a time.
Seurat::SpatialFeaturePlot(
object = anterior,
features = c("L2.3.IT", "L6b", "Meis2", "Oligo"),
alpha = c(0.1, 1))
图片.png
Spatial scatterpies
cell_types_all <- colnames(decon_mtrx)[which(colnames(decon_mtrx) != "res_ss")]
SPOTlight::spatial_scatterpie(se_obj = anterior,
cell_types_all = cell_types_all,
img_path = "sample_data/spatial/tissue_lowres_image.png",
pie_scale = 0.4)
图片.png
Plot spot composition of spots containing cell-types of interest
SPOTlight::spatial_scatterpie(se_obj = anterior,
cell_types_all = cell_types_all,
img_path = "sample_data/spatial/tissue_lowres_image.png",
cell_types_interest = "L6b",
pie_scale = 0.8)
图片.png
Spatial interaction graph
现在我们知道在每个点内发现了哪些细胞类型，我们可以制作一个表示空间相互作用的图，其中细胞类型之间的边缘越强，我们在同一点内发现它们的频率越高。 为此，我们只需要运行 get_spatial_interaction_graph 函数，该函数将打印绘图并返回绘图所需的元素。
graph_ntw <- SPOTlight::get_spatial_interaction_graph(decon_mtrx = decon_mtrx[, cell_types_all])
If you want to tune how the graph looks you can do the following or you can check out more options here:
deg <- degree(graph_ntw, mode="all")
# Get color palette for difusion
edge_importance <- E(graph_ntw)$importance
# Select a continuous palette
qual_col_pals <- brewer.pal.info[brewer.pal.info$category == 'seq',]
# Create a color palette
getPalette <- colorRampPalette(brewer.pal(9, "YlOrRd"))
# Get how many values we need
grad_edge <- seq(0, max(edge_importance), 0.1)
# Generate extended gradient palette dataframe
graph_col_df <- data.frame(value = as.character(grad_edge),
color = getPalette(length(grad_edge)),
stringsAsFactors = FALSE)
# Assign color to each edge
color_edge <- data.frame(value = as.character(round(edge_importance, 1)), stringsAsFactors = FALSE) %>%
dplyr::left_join(graph_col_df, by = "value") %>%
dplyr::pull(color)
# Open a pdf file
plot(graph_ntw,
# Size of the edge
edge.width = edge_importance,
edge.color = color_edge,
# Size of the buble
vertex.size = deg,
vertex.color = "#cde394",
vertex.frame.color = "white",
vertex.label.color = "black",
vertex.label.family = "Ubuntu", # Font family of the label (e.g.“Times”, “Helvetica”)
layout = layout.circle)
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Lastly one can compute cell-cell correlations to see groups of cells that correlate positively or negatively.
# Remove cell types not predicted to be on the tissue
decon_mtrx_sub <- decon_mtrx[, cell_types_all]
decon_mtrx_sub <- decon_mtrx_sub[, colSums(decon_mtrx_sub) > 0]
# Compute correlation
decon_cor <- cor(decon_mtrx_sub)
# Compute correlation P-value
p.mat <- corrplot::cor.mtest(mat = decon_mtrx_sub, conf.level = 0.95)
# Visualize
ggcorrplot::ggcorrplot(
corr = decon_cor,
p.mat = p.mat[[1]],
hc.order = TRUE,
type = "full",
insig = "blank",
lab = TRUE,
outline.col = "lightgrey",
method = "square",
# colors = c("#4477AA", "white", "#BB4444"))
colors = c("#6D9EC1", "white", "#E46726"),
title = "Predicted cell-cell proportion correlation",
legend.title = "Correlation\n(Pearson)") +
ggplot2::theme(
plot.title = ggplot2::element_text(size = 22, hjust = 0.5, face = "bold"),
legend.text = ggplot2::element_text(size = 12),
legend.title = ggplot2::element_text(size = 15),
axis.text.x = ggplot2::element_text(angle = 90),
axis.text = ggplot2::element_text(size = 18, vjust = 0.5))
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Step-by-Step insight
Here we are going to show step by step what is going on and all the different steps involved in the process
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Downsample data
如果数据集非常大，我们希望在细胞数量和基因数量方面对其进行下采样，以训练模型。 为了进行下采样，我们希望保留每个簇的代表性细胞数量和最重要的基因。 我们表明这种下采样不会影响模型的性能并大大加快了模型训练的速度。
# Downsample scRNAseq to select gene set and number of cells to train the model
se_sc_down <- downsample_se_obj(se_obj = cortex_sc,
clust_vr = "subclass",
cluster_markers = cluster_markers_all,
cl_n = 100,
hvg = 3000)
Train NMF model
Once we have the data ready to pass to the model we can train it as shown below.
start_time <- Sys.time()
nmf_mod_ls <- train_nmf(cluster_markers = cluster_markers_all,
se_sc = se_sc_down,
mtrx_spatial = anterior@assays$Spatial@counts,
clust_vr = "subclass",
ntop = NULL,
hvg = 3000,
transf = "uv",
method = "nsNMF")
nmf_mod <- nmf_mod_ls[[1]]
Extract matrices form the model:
# get basis matrix W
w <- basis(nmf_mod)
dim(w)
# get coefficient matrix H
h <- coef(nmf_mod)
dim(h)
Look at cell-type specific topic profile
rownames(h) <- paste("Topic", 1:nrow(h), sep = "_")
topic_profile_plts <- dot_plot_profiles_fun(
h = h,
train_cell_clust = nmf_mod_ls[[2]]
)
topic_profile_plts[[2]] + theme(axis.text.x = element_text(angle = 90))
Spot Deconvolution
# Extract count matrix
spot_counts <- anterior@assays$Spatial@counts
# Subset to genes used to train the model
spot_counts <- spot_counts[rownames(spot_counts) %in% rownames(w), ]
Run spots through the basis to get the pertinent coefficients. To do this for every spot we are going to set up a system of linear equations where we need to find the coefficient, we will use non-negative least squares to determine the best coefficient fit.
ct_topic_profiles <- topic_profile_per_cluster_nmf(h = h,
train_cell_clust = nmf_mod_ls[[2]])
decon_mtrx <- mixture_deconvolution_nmf(nmf_mod = nmf_mod,
mixture_transcriptome = spot_counts,
transf = "uv",
reference_profiles = ct_topic_profiles,
min_cont = 0.09)
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