scRNA-seq yield many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring transcription factor (TF) activities from prior knowledge.
In this notebook we showcase how to use
decoupleR for transcription factor 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
from this webpage.
First, we need to load the relevant packages,
Seurat to handle scRNA-seq data
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
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()
DoRothEA is a comprehensive resource containing a curated collection of TFs and their transcriptional targets. Since these regulons were gathered from different types of evidence, interactions in DoRothEA are classified in different confidence levels, ranging from A (highest confidence) to D (lowest confidence). Moreover, each interaction is weighted by its confidence level and the sign of its mode of regulation (activation or inhibition).
For this example we will use the human version (mouse is also available) and we
will use the confidence levels ABC. We can use
decoupleR to retrieve it from
net <- get_dorothea(organism='human', levels=c('A', 'B', 'C')) net #> # A tibble: 32,277 × 4 #> source confidence target mor #> <chr> <chr> <chr> <dbl> #> 1 ADNP C ATF7IP 0.333 #> 2 ADNP C DYRK1A 0.333 #> 3 ADNP C TLK1 0.333 #> 4 ADNP C ZMYM4 0.333 #> 5 AHR C ARHGAP15 0.333 #> 6 AHR C ARID5B 0.333 #> 7 AHR B ASAP1 0.5 #> 8 AHR C CREB5 0.333 #> 9 AHR C CTNNA1 0.333 #> 10 AHR C CTNNA2 0.333 #> # … with 32,267 more rows
To infer activities we will run the Weighted Mean method (
wmean). It infers
regulator activities by first multiplying each target feature by its associated
weight which then are summed to an enrichment score
permutations of random target features can be performed to obtain a null
distribution that can be used to compute a z-score
norm_wmean, or a corrected
corr_wmean by multiplying
wmean by the minus log10 of the obtained
In this example we use
wmean but we could have used any other.
To see what methods are available use
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$RNA@data) # Run wmean acts <- run_wmean(mat=mat, net=net, .source='source', .target='target', .mor='mor', times = 100, minsize = 5) acts #> # A tibble: 131,520 × 5 #> statistic source condition score p_value #> <chr> <chr> <chr> <dbl> <dbl> #> 1 corr_wmean AHR AAACATACAACCAC-1 0.947 0.02 #> 2 corr_wmean AHR AAACGCTGTTTCTG-1 0.203 0.24 #> 3 corr_wmean AHR AACCTTTGGACGGA-1 1.49 0.02 #> 4 corr_wmean AHR AACGCCCTCGTACA-1 0.0682 0.56 #> 5 corr_wmean AHR AACGTCGAGTATCG-1 0.0285 0.36 #> 6 corr_wmean AHR AACTCACTCAAGCT-1 0.389 0.14 #> 7 corr_wmean AHR AAGATGGAAAACAG-1 0.173 0.24 #> 8 corr_wmean AHR AAGATTACCGCCTT-1 1.32 0.02 #> 9 corr_wmean AHR AAGCCATGAACTGC-1 0.560 0.08 #> 10 corr_wmean AHR AAGGTCTGCAGATC-1 0.149 0.28 #> # … with 131,510 more rows
From the obtained results, we will select the
norm_wmean activities and store
them in our object as a new assay called
# Extract norm_wmean and store it in tfswmean in pbmc data[['tfswmean']] <- acts %>% filter(statistic == 'norm_wmean') %>% pivot_wider(id_cols = 'source', names_from = 'condition', values_from = 'score') %>% column_to_rownames('source') %>% Seurat::CreateAssayObject(.) # Change assay DefaultAssay(object = data) <- "tfswmean" # Scale the data data <- ScaleData(data) data@assays$tfswmean@data <- data@firstname.lastname@example.org
This new assay can be used to plot activities. Here we observe the activity inferred for PAX5 across cells, which it is particulary active in B cells. Interestingly, PAX5 is a known TF crucial for B cell identity and function. The inference of activities from “foot-prints” of target genes is more informative than just looking at the molecular readouts of a given TF, as an example here is the gene expression of PAX5, which is not very informative by itself:
p1 <- DimPlot(data, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend() + ggtitle('Cell types') p2 <- (FeaturePlot(data, features = c("PAX5")) & scale_colour_gradient2(low = 'blue', mid = 'white', high = 'red')) + ggtitle('PAX5 activity') DefaultAssay(object = data) <- "RNA" p3 <- FeaturePlot(data, features = c("PAX5")) + ggtitle('PAX5 expression') DefaultAssay(object = data) <- "tfswmean" p1 | p2 | p3