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

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.

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") %>% 
#>          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
#> # 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     

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") %>%
#>                  t
#> RHBDL2  -12.810588
#> PLEKHH2 -10.794453
#> HEG1     -9.788112
#> CLU      -9.761618
#> FHL1      8.950191
#> RBP4     -8.529074

3 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 (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:

  • Androgen: involved in the growth and development of the male reproductive organs.
  • EGFR: regulates growth, survival, migration, apoptosis, proliferation, and differentiation in mammalian cells
  • Estrogen: promotes the growth and development of the female reproductive organs.
  • Hypoxia: promotes angiogenesis and metabolic reprogramming when O2 levels are low.
  • JAK-STAT: involved in immunity, cell division, cell death, and tumor formation.
  • MAPK: integrates external signals and promotes cell growth and proliferation.
  • NFkB: regulates immune response, cytokine production and cell survival.
  • p53: regulates cell cycle, apoptosis, DNA repair and tumor suppression.
  • PI3K: promotes growth and proliferation.
  • TGFb: involved in development, homeostasis, and repair of most tissues.
  • TNFa: mediates haematopoiesis, immune surveillance, tumour regression and protection from infection.
  • Trail: induces apoptosis.
  • VEGF: mediates angiogenesis, vascular permeability, and cell migration.
  • WNT: regulates organ morphogenesis during development and tissue repair.

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)
#> # 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

4 Activity inference with Multivariate Linear Model (MLM)

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.