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 here from this webpage.

1 Loading packages

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)

2 Loading the data-set

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()

3 CollecTRI network

CollecTRI is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous DoRothEA network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).

For this example we will use the human version (mouse and rat are also available). We can use decoupleR to retrieve it from OmniPath. The argument split_complexes keeps complexes or splits them into subunits, by default we recommend to keep complexes together.

net <- get_collectri(organism='human', split_complexes=FALSE)
net
#> # A tibble: 42,595 × 3
#>    source target   mor
#>    <chr>  <chr>  <dbl>
#>  1 MYC    TERT       1
#>  2 SPI1   BGLAP      1
#>  3 SMAD3  JUN        1
#>  4 SMAD4  JUN        1
#>  5 STAT5A IL2        1
#>  6 STAT5B IL2        1
#>  7 RELA   FAS        1
#>  8 WT1    NR0B1      1
#>  9 NR0B2  CASP1      1
#> 10 SP1    ALDOA      1
#> # ℹ 42,585 more rows

4 Activity inference with Weighted Mean

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 wmean. Furthermore, 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 estimate corr_wmean by multiplying wmean by the minus log10 of the obtained empirical p-value.

In this example we use wmean but we could have used any other. To see what methods are available use show_methods().

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 wmean
acts <- run_wmean(mat=mat, net=net, .source='source', .target='target',
                  .mor='mor', times = 100, minsize = 5)
acts
#> # A tibble: 239,040 × 5
#>    statistic  source condition        score p_value
#>    <chr>      <chr>  <chr>            <dbl>   <dbl>
#>  1 corr_wmean ABL1   AAACATACAACCAC-1 1.16     0.02
#>  2 corr_wmean ABL1   AAACGCTGTTTCTG-1 0.121    0.38
#>  3 corr_wmean ABL1   AACCTTTGGACGGA-1 1.24     0.02
#>  4 corr_wmean ABL1   AACGCCCTCGTACA-1 0.533    0.1 
#>  5 corr_wmean ABL1   AACGTCGAGTATCG-1 0.468    0.12
#>  6 corr_wmean ABL1   AACTCACTCAAGCT-1 0.563    0.08
#>  7 corr_wmean ABL1   AAGATGGAAAACAG-1 0.269    0.14
#>  8 corr_wmean ABL1   AAGATTACCGCCTT-1 1.17     0.02
#>  9 corr_wmean ABL1   AAGCCATGAACTGC-1 0.397    0.16
#> 10 corr_wmean ABL1   AAGGTCTGCAGATC-1 0        0.42
#> # ℹ 239,030 more rows

5 Visualization

From the obtained results, we will select the norm_wmean activities and store them in our object as a new assay called tfswmean:

# 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$tfswmean@data <- data@assays$tfswmean@scale.data

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