decoupleR 2.10.0
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.
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)
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
.
For this example we use limma
but we could have used DeSeq2
, edgeR
or any
other statistical framework. decoupleR requires a gene level statistic to
perform enrichment analysis but it is agnostic of how it was generated. However,
we do recommend to use statistics that include the direction of change and its
significance, for example the t-value obtained for limma
(t
) or DeSeq2
(stat
).
edgeR does not return such statistic but we can create our own by weighting the
obtained logFC by pvalue with this formula: -log10(pvalue) * logFC
.
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$counts %>%
dplyr::mutate_if(~ any(is.na(.x)), ~ if_else(is.na(.x),0,.x)) %>%
column_to_rownames(var = "gene") %>%
as.matrix()
head(counts)
#> PANC1.WT.Rep1 PANC1.WT.Rep2 PANC1.WT.Rep3 PANC1.FOXA2KO.Rep1 PANC1.FOXA2KO.Rep2 PANC1.FOXA2KO.Rep3
#> NOC2L 10.052588 11.949123 12.057774 12.312291 12.139918 11.494205
#> PLEKHN1 7.535115 8.125993 8.714880 8.048196 8.290154 8.621239
#> PERM1 6.281242 6.424582 6.589668 6.293285 6.486136 6.775344
#> ISG15 10.938252 11.469081 11.425415 11.549986 11.371464 11.178157
#> AGRN 6.956335 7.196108 7.522550 7.061549 7.485534 7.071555
#> C1orf159 9.546224 9.788721 9.794589 9.850830 9.988069 9.965357
The design meta-data:
design <- data$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.FOXA2KO
And 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 -8.529074
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)
#> Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
#> dat <- vroom(...)
#> problems(dat)
net
#> # A tibble: 7,000 × 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
#> # ℹ 6,990 more rows
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.