knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring pathway activities from prior knowledge.
In this notebook we showcase how to use decoupleR
for pathway activity
inference with a down-sampled PBMCs 10X data-set. The data consists of 160
PBMCs from a Healthy Donor. The original data is freely available from 10x Genomics
here
from this webpage.
First, we need to load the relevant packages, Seurat
to handle scRNA-seq data
and decoupleR
to use statistical methods.
## We load the required packages library(Seurat) library(decoupleR) # Only needed for data handling and plotting library(dplyr) library(tibble) library(tidyr) library(patchwork) library(ggplot2) library(pheatmap)
Here we used a down-sampled version of the data used in the Seurat
vignette.
We can open the data like this:
inputs_dir <- system.file("extdata", package = "decoupleR") data <- readRDS(file.path(inputs_dir, "sc_data.rds"))
We can observe that we have different cell types:
DimPlot(data, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
PROGENy is a comprehensive resource containing a curated collection of pathways and their target genes, with weights for each interaction. For this example we will use the human weights (other organisms are available) and we will use the top 500 responsive genes ranked by p-value. Here is a brief description of each pathway:
To access it we can use decoupleR
:
net <- get_progeny(organism = 'human', top = 500) net
To infer pathway enrichment scores we will run the Multivariate Linear Model (mlm
) method. For each sample in our dataset (mat
), it fits a linear model that predicts the observed gene expression based on all pathways' Pathway-Gene interactions weights.
Once fitted, the obtained t-values of the slopes are the scores. If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.
To run decoupleR
methods, we need an input matrix (mat
), an input prior
knowledge network/resource (net
), and the name of the columns of net that we
want to use.
# Extract the normalized log-transformed counts mat <- as.matrix(data@assays$RNA@data) # Run mlm acts <- run_mlm(mat=mat, net=net, .source='source', .target='target', .mor='weight', minsize = 5) acts
From the obtained results, we will select the ulm
activities and store
them in our object as a new assay called pathwaysmlm
:
# Extract mlm and store it in pathwaysmlm in data data[['pathwaysmlm']] <- acts %>% pivot_wider(id_cols = 'source', names_from = 'condition', values_from = 'score') %>% column_to_rownames('source') %>% Seurat::CreateAssayObject(.) # Change assay DefaultAssay(object = data) <- "pathwaysmlm" # Scale the data data <- ScaleData(data) data@assays$pathwaysmlm@data <- data@assays$pathwaysmlm@scale.data
This new assay can be used to plot activities. Here we visualize the Trail pathway, associated with apoptosis, which seems that in B and NK cells is more active.
p1 <- DimPlot(data, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend() + ggtitle('Cell types') p2 <- (FeaturePlot(data, features = c("Trail")) & scale_colour_gradient2(low = 'blue', mid = 'white', high = 'red')) + ggtitle('Trail activity') p1 | p2
We can also see what is the mean activity per group across pathways:
# Extract activities from object as a long dataframe df <- t(as.matrix(data@assays$pathwaysmlm@data)) %>% as.data.frame() %>% mutate(cluster = Idents(data)) %>% pivot_longer(cols = -cluster, names_to = "source", values_to = "score") %>% group_by(cluster, source) %>% summarise(mean = mean(score)) # Transform to wide matrix top_acts_mat <- df %>% pivot_wider(id_cols = 'cluster', names_from = 'source', values_from = 'mean') %>% column_to_rownames('cluster') %>% as.matrix() # Choose color palette palette_length = 100 my_color = colorRampPalette(c("Darkblue", "white","red"))(palette_length) my_breaks <- c(seq(-2, 0, length.out=ceiling(palette_length/2) + 1), seq(0.05, 2, length.out=floor(palette_length/2))) # Plot pheatmap(top_acts_mat, border_color = NA, color=my_color, breaks = my_breaks)
In this specific example, we can observe that Trail is more active in B and NK cells.
options(width = 120) sessioninfo::session_info()
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