Pathway activity inference in bulk RNA-seq
3 April 2022
Package
decoupleR 2.0.1
Bulk RNA-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 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(tidyverse)
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 from the contrast:
# Extract t-values per gene
deg <- data$limma_ttop %>%
select(ID, t) %>%
filter(!is.na(t)) %>%
column_to_rownames(var = "ID") %>%
as.matrix()
head(deg)
#> t
#> RHBDL2 -12.810588
#> PLEKHH2 -10.794453
#> HEG1 -9.788112
#> CLU -9.761618
#> FHL1 8.950191
#> RBP4 -8.5290743 PROGENy model
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
(mouse is also available) and we will use the top 100 responsive genes ranked
by p-value. We can use decoupleR to retrieve it from OmniPath:
net <- get_progeny(organism = 'human', top = 100)
net
#> # A tibble: 1,400 × 4
#> source target weight p_value
#> <chr> <chr> <dbl> <dbl>
#> 1 Androgen TMPRSS2 11.5 2.38e-47
#> 2 Androgen NKX3-1 10.6 2.21e-44
#> 3 Androgen MBOAT2 10.5 4.63e-44
#> 4 Androgen KLK2 10.2 1.94e-40
#> 5 Androgen SARG 11.4 2.79e-40
#> 6 Androgen SLC38A4 7.36 1.25e-39
#> 7 Androgen MTMR9 6.13 2.53e-38
#> 8 Androgen ZBTB16 10.6 1.57e-36
#> 9 Androgen KCNN2 9.47 7.71e-36
#> 10 Androgen OPRK1 -5.63 1.11e-35
#> # … with 1,390 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='weight', times = 100, minsize = 5)
sample_acts
#> # A tibble: 252 × 5
#> statistic source condition score p_value
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 corr_wmean Androgen PANC1.FOXA2KO.Rep1 4.24 0.26
#> 2 corr_wmean Androgen PANC1.FOXA2KO.Rep2 6.07 0.140
#> 3 corr_wmean Androgen PANC1.FOXA2KO.Rep3 2.87 0.4
#> 4 corr_wmean Androgen PANC1.WT.Rep1 7.10 0.100
#> 5 corr_wmean Androgen PANC1.WT.Rep2 3.93 0.28
#> 6 corr_wmean Androgen PANC1.WT.Rep3 6.59 0.120
#> 7 corr_wmean EGFR PANC1.FOXA2KO.Rep1 0.0188 0.82
#> 8 corr_wmean EGFR PANC1.FOXA2KO.Rep2 0.0224 0.8
#> 9 corr_wmean EGFR PANC1.FOXA2KO.Rep3 0.0372 0.66
#> 10 corr_wmean EGFR PANC1.WT.Rep1 -0.0520 0.28
#> # … with 242 more rows5 Visualization
From the obtained results, we will select the norm_wmean activities and we
will observe the obtained activities per sample in a heat-map:
# 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()
# 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 observe that WT samples have higher activities for p53 and TGFb than KO. On the other hand, KO show higher activities for MAPK and PI3K.
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, net=net, .source='source', .target='target',
.mor='weight', times = 100, minsize = 5)
contrast_acts
#> # A tibble: 42 × 5
#> statistic source condition score p_value
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 corr_wmean Androgen V1 -0.00231 0.0200
#> 2 corr_wmean EGFR V1 0.533 0.1
#> 3 corr_wmean Estrogen V1 0.0550 0.48
#> 4 corr_wmean Hypoxia V1 -0.00295 0.94
#> 5 corr_wmean JAK-STAT V1 1.90 0.02
#> 6 corr_wmean MAPK V1 1.99 0.02
#> 7 corr_wmean NFkB V1 2.04 0.02
#> 8 corr_wmean PI3K V1 0.962 0.02
#> 9 corr_wmean TGFb V1 -0.217 0.2
#> 10 corr_wmean TNFa V1 1.58 0.02
#> # … with 32 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')
# 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")
As observed before, the pathways p53 and TGFb are deactivated in KO when compared to WT, while JAK-STAT and others seem to be activated.
We can further visualize the most responsive genes in each pathway along their t-values to interpret the results. For example, let’s see the genes that are belong to the MAPK pathway:
pathway <- 'MAPK'
df <- net %>%
filter(source == pathway) %>%
arrange(target) %>%
mutate(ID = target, color = "3") %>%
column_to_rownames('target')
inter <- sort(intersect(rownames(deg),rownames(df)))
df <- df[inter, ]
df['t_value'] <- deg[inter, ]
df <- df %>%
mutate(color = if_else(weight > 0 & t_value > 0, '1', color)) %>%
mutate(color = if_else(weight > 0 & t_value < 0, '2', color)) %>%
mutate(color = if_else(weight < 0 & t_value > 0, '2', color)) %>%
mutate(color = if_else(weight < 0 & t_value < 0, '1', color))
ggplot(df, aes(x = weight, y = t_value, color = color)) + geom_point() +
scale_colour_manual(values = c("red","royalblue3","grey")) +
geom_label_repel(aes(label = ID)) +
theme_minimal() +
theme(legend.position = "none") +
geom_vline(xintercept = 0, linetype = 'dotted') +
geom_hline(yintercept = 0, linetype = 'dotted') +
ggtitle(pathway)
#> Warning: ggrepel: 66 unlabeled data points (too many overlaps). Consider increasing max.overlaps
The pathway seems to be active since the majority of target genes with positive weights have positive t-values (1st quadrant), and the majority of the ones with negative weights have negative t-values (3d quadrant).
6 Session information
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