Transcription factor activity inference in bulk RNA-seq
25 April 2023
Package
decoupleR 2.6.0
Bulk RNA-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 bulk RNA-seq data-set where the transcription factor FOXA2 was
knocked out in pancreatic cancer cell lines.
The data consists of 3 Wild Type (WT) samples and 3 Knock Outs (KO). They are freely available in GEO.
1 Loading packages
First, we need to load the relevant packages:
## We load the required packages
library(decoupleR)
library(dplyr)
library(tibble)
library(tidyr)
library(ggplot2)
library(pheatmap)
library(ggrepel)2 Loading the data-set
Here we used an already processed bulk RNA-seq data-set. We provide the
normalized log-transformed counts, the experimental design meta-data and the
Differential Expressed Genes (DEGs) obtained using limma.
We can open the data like this:
inputs_dir <- system.file("extdata", package = "decoupleR")
data <- readRDS(file.path(inputs_dir, "bk_data.rds"))From data we can extract the mentioned information. Here we see the normalized
log-transformed counts:
# Remove NAs and set row names
counts <- data$counts %>%
dplyr::mutate_if(~ any(is.na(.x)), ~ if_else(is.na(.x),0,.x)) %>%
column_to_rownames(var = "gene") %>%
as.matrix()
head(counts)
#> PANC1.WT.Rep1 PANC1.WT.Rep2 PANC1.WT.Rep3 PANC1.FOXA2KO.Rep1 PANC1.FOXA2KO.Rep2 PANC1.FOXA2KO.Rep3
#> NOC2L 10.052588 11.949123 12.057774 12.312291 12.139918 11.494205
#> PLEKHN1 7.535115 8.125993 8.714880 8.048196 8.290154 8.621239
#> PERM1 6.281242 6.424582 6.589668 6.293285 6.486136 6.775344
#> ISG15 10.938252 11.469081 11.425415 11.549986 11.371464 11.178157
#> AGRN 6.956335 7.196108 7.522550 7.061549 7.485534 7.071555
#> C1orf159 9.546224 9.788721 9.794589 9.850830 9.988069 9.965357The design meta-data:
design <- data$design
design
#> # A tibble: 6 × 2
#> sample condition
#> <chr> <chr>
#> 1 PANC1.WT.Rep1 PANC1.WT
#> 2 PANC1.WT.Rep2 PANC1.WT
#> 3 PANC1.WT.Rep3 PANC1.WT
#> 4 PANC1.FOXA2KO.Rep1 PANC1.FOXA2KO
#> 5 PANC1.FOXA2KO.Rep2 PANC1.FOXA2KO
#> 6 PANC1.FOXA2KO.Rep3 PANC1.FOXA2KOAnd the results of limma, of which we are interested in extracting the
obtained t-value and p-value from the contrast:
# Extract t-values per gene
deg <- data$limma_ttop %>%
select(ID, logFC, t, P.Value) %>%
filter(!is.na(t)) %>%
column_to_rownames(var = "ID") %>%
as.matrix()
head(deg)
#> logFC t P.Value
#> RHBDL2 -1.823940 -12.810588 3.030276e-06
#> PLEKHH2 -1.568830 -10.794453 9.932046e-06
#> HEG1 -1.725806 -9.788112 1.939734e-05
#> CLU -1.786200 -9.761618 1.975813e-05
#> FHL1 2.087082 8.950191 3.552199e-05
#> RBP4 -1.728960 -8.529074 4.904579e-053 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: 45,398 × 3
#> source target mor
#> <chr> <chr> <dbl>
#> 1 MYC TERT 1
#> 2 SPI1 BGLAP 1
#> 3 SMAD3 ADAM2 1
#> 4 SMAD3 JUN 1
#> 5 SMAD4 ADAM2 1
#> 6 SMAD4 JUN 1
#> 7 STAT5A IL2 1
#> 8 STAT5B IL2 1
#> 9 RELA FAS 1
#> 10 WT1 NR0B1 1
#> # ℹ 45,388 more rows4 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.
# Run wmean
sample_acts <- run_wmean(mat=counts, net=net, .source='source', .target='target',
.mor='mor', times = 100, minsize = 5)
sample_acts
#> # A tibble: 10,512 × 5
#> statistic source condition score p_value
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 corr_wmean ABL1 PANC1.FOXA2KO.Rep1 1.08 0.7
#> 2 corr_wmean ABL1 PANC1.FOXA2KO.Rep2 0.399 0.88
#> 3 corr_wmean ABL1 PANC1.FOXA2KO.Rep3 0.415 0.88
#> 4 corr_wmean ABL1 PANC1.WT.Rep1 0 1
#> 5 corr_wmean ABL1 PANC1.WT.Rep2 0.601 0.82
#> 6 corr_wmean ABL1 PANC1.WT.Rep3 1.33 0.64
#> 7 corr_wmean AHR PANC1.FOXA2KO.Rep1 0.962 0.76
#> 8 corr_wmean AHR PANC1.FOXA2KO.Rep2 4.27 0.3
#> 9 corr_wmean AHR PANC1.FOXA2KO.Rep3 4.52 0.28
#> 10 corr_wmean AHR PANC1.WT.Rep1 0.960 0.76
#> # ℹ 10,502 more rows5 Visualization
From the obtained results, we will select the norm_wmean activities and we
will observe the most variable activities across samples in a heat-map:
n_tfs <- 25
# Transform to wide matrix
sample_acts_mat <- sample_acts %>%
filter(statistic == 'norm_wmean') %>%
pivot_wider(id_cols = 'condition', names_from = 'source',
values_from = 'score') %>%
column_to_rownames('condition') %>%
as.matrix()
# Get top tfs with more variable means across clusters
tfs <- sample_acts %>%
group_by(source) %>%
summarise(std = sd(score)) %>%
arrange(-abs(std)) %>%
head(n_tfs) %>%
pull(source)
sample_acts_mat <- sample_acts_mat[,tfs]
# Scale per sample
sample_acts_mat <- scale(sample_acts_mat)
# Choose color palette
palette_length = 100
my_color = colorRampPalette(c("Darkblue", "white","red"))(palette_length)
my_breaks <- c(seq(-3, 0, length.out=ceiling(palette_length/2) + 1),
seq(0.05, 3, length.out=floor(palette_length/2)))
# Plot
pheatmap(sample_acts_mat, border_color = NA, color=my_color, breaks = my_breaks) 
We can also infer pathway activities from the t-values of the DEGs between KO and WT:
# Run wmean
contrast_acts <- run_wmean(mat=deg[, 't', drop=FALSE], net=net, .source='source', .target='target',
.mor='mor', times = 100, minsize = 5)
contrast_acts
#> # A tibble: 1,752 × 5
#> statistic source condition score p_value
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 corr_wmean ABL1 t 0.133 0.54
#> 2 corr_wmean AHR t 0.0162 0.74
#> 3 corr_wmean AIRE t -0.0469 0.24
#> 4 corr_wmean AP1 t 0.246 0.04
#> 5 corr_wmean APEX1 t 0.00919 0.9
#> 6 corr_wmean AR t 0.00740 0.32
#> 7 corr_wmean ARID1A t -0.0442 0.46
#> 8 corr_wmean ARID3A t 1.04 0.1
#> 9 corr_wmean ARID3B t 0.310 0.36
#> 10 corr_wmean ARID4A t -0.0570 0.48
#> # ℹ 1,742 more rowsWe select the norm_wmean activities and then we show the changes
in activity between KO and WT:
# Filter norm_wmean
f_contrast_acts <- contrast_acts %>%
filter(statistic == 'norm_wmean') %>%
mutate(rnk = NA)
# Filter top TFs in both signs
msk <- f_contrast_acts$score > 0
f_contrast_acts[msk, 'rnk'] <- rank(-f_contrast_acts[msk, 'score'])
f_contrast_acts[!msk, 'rnk'] <- rank(-abs(f_contrast_acts[!msk, 'score']))
tfs <- f_contrast_acts %>%
arrange(rnk) %>%
head(n_tfs) %>%
pull(source)
f_contrast_acts <- f_contrast_acts %>%
filter(source %in% tfs)
# Plot
ggplot(f_contrast_acts, aes(x = reorder(source, score), y = score)) +
geom_bar(aes(fill = score), stat = "identity") +
scale_fill_gradient2(low = "darkblue", high = "indianred",
mid = "whitesmoke", midpoint = 0) +
theme_minimal() +
theme(axis.title = element_text(face = "bold", size = 12),
axis.text.x =
element_text(angle = 45, hjust = 1, size =10, face= "bold"),
axis.text.y = element_text(size =10, face= "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
xlab("Pathways")
The TFs HLF and IRX1 are deactivated in KO when compared to WT, while FOSL1 and MYC seem to be activated.
We can further visualize the most differential target genes in each TF along their p-values to interpret the results. For example, let’s see the genes that are belong to FOXA2:
tf <- 'FOXA2'
df <- net %>%
filter(source == tf) %>%
arrange(target) %>%
mutate(ID = target, color = "3") %>%
column_to_rownames('target')
inter <- sort(intersect(rownames(deg),rownames(df)))
df <- df[inter, ]
df[,c('logfc', 't_value', 'p_value')] <- deg[inter, ]
df <- df %>%
mutate(color = if_else(mor > 0 & t_value > 0, '1', color)) %>%
mutate(color = if_else(mor > 0 & t_value < 0, '2', color)) %>%
mutate(color = if_else(mor < 0 & t_value > 0, '2', color)) %>%
mutate(color = if_else(mor < 0 & t_value < 0, '1', color))
ggplot(df, aes(x = logfc, y = -log10(p_value), color = color, size=abs(mor))) +
geom_point() +
scale_colour_manual(values = c("red","royalblue3","grey")) +
geom_label_repel(aes(label = ID, size=1)) +
theme_minimal() +
theme(legend.position = "none") +
geom_vline(xintercept = 0, linetype = 'dotted') +
geom_hline(yintercept = 0, linetype = 'dotted') +
ggtitle(tf)
Here blue means that the sign of multiplying the mor and t-value is negative,
meaning that these genes are “deactivating” the TF, and red means that the sign
is positive, meaning that these genes are “activating” the TF. In this particular
case, FOXA2 target genes seem to be more under-expressed in KO than in WT,
therefore the KO worked.
6 Session information
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
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#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2023-04-25
#> pandoc 2.7.3 @ /usr/bin/ (via rmarkdown)
#>
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