Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Downstream analysis tools can be used to summarize deregulation events into a smaller set of biologically interpretable features. In particular, methods that estimate the activity of transcription factors (TFs) from gene expression are commonly used. It has been shown that the transcriptional targets of a TF yield a much more robust estimation of the TF activity than observing the expression of the TF itself. Consequently, for the estimation of transcription factor activities, a network of transcriptional regulation is required in combination with a statistical algorithm that summarizes the expression of the target genes into a single activity score. Over the years, many different regulatory networks and statistical algorithms have been developed, mostly in a fixed combination of one network and one algorithm. To systematically evaluate both networks and algorithms, we developed decoupleR , an R package that allows users to apply efficiently any combination provided.
For more information about how this package has been used with real data, please check the following links:
- Decoupling statistics and networks: a crowdsourced systematic assessment of transcription factor activity estimation from transcriptomics data.
- Creation of benchmarking pipelines.
Get the latest stable R release from
CRAN.
Then install decoupleR using from
Bioconductor the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("decoupleR")
# Check that you have a valid Bioconductor installation
BiocManager::valid()Then install development version from GitHub with:
BiocManager::install("saezlab/decoupleR")library(decoupleR)
inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")
mat <- file.path(inputs_dir, "input-expr_matrix.rds") %>%
readRDS() %>%
dplyr::glimpse()
#> num [1:18490, 1:4] 3.251 0.283 -2.253 0.782 -4.575 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:18490] "A1BG" "A1CF" "A2M" "A2ML1" ...
#> ..$ : chr [1:4] "GSM2753335" "GSM2753336" "GSM2753337" "GSM2753338"
network <- file.path(inputs_dir, "input-dorothea_genesets.rds") %>%
readRDS() %>%
dplyr::glimpse()
#> Rows: 151
#> Columns: 5
#> $ tf <chr> "FOXO4", "FOXO4", "FOXO4", "FOXO4", "FOXO4", "FOXO4", "FOXO…
#> $ confidence <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
#> $ target <chr> "BCL2L11", "BCL6", "CDKN1A", "CDKN1B", "G6PC", "GADD45A", "…
#> $ mor <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ likelihood <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…decouple() allows access to all decoupleR available statistics in
one place. Statistic functions inside decoupleR always return a tidy
tibble that can be easily processed with the tools provide by the
tidyverse ecosystem.
decouple(
mat = mat,
network = network,
.source = "tf",
.target = "target",
statistics = c("gsva", "mean", "pscira", "scira", "viper", "ora"),
args = list(
gsva = list(verbose = FALSE),
mean = list(.mor = "mor", .likelihood = "likelihood"),
pscira = list(.mor = "mor"),
scira = list(.mor = "mor"),
viper = list(.mor = "mor", .likelihood = "likelihood", verbose = FALSE),
ora = list()
)
)
#> # A tibble: 140 x 11
#> run_id statistic tf condition score p_value estimate conf.low conf.high
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 gsva FOXO4 GSM2753335 -0.380 NA NA NA NA
#> 2 1 gsva FOXO4 GSM2753336 -0.300 NA NA NA NA
#> 3 1 gsva FOXO4 GSM2753337 0.239 NA NA NA NA
#> 4 1 gsva FOXO4 GSM2753338 0.0907 NA NA NA NA
#> 5 1 gsva NFIC GSM2753335 -0.0845 NA NA NA NA
#> 6 1 gsva NFIC GSM2753336 0.0778 NA NA NA NA
#> 7 1 gsva NFIC GSM2753337 -0.260 NA NA NA NA
#> 8 1 gsva NFIC GSM2753338 0.281 NA NA NA NA
#> 9 1 gsva RFXAP GSM2753335 -0.810 NA NA NA NA
#> 10 1 gsva RFXAP GSM2753336 -0.472 NA NA NA NA
#> # … with 130 more rows, and 2 more variables: method <chr>, alternative <chr>In turn, we recognize that the use of individual statistics may be of interest. Therefore, these are also exported and ready for use. All statistics follow the same design pattern and arguments, so moving between statistics could be very comfortable.
# viper call is equivalent to the one made by decouple() above.
run_viper(
mat = mat,
network = network,
.source = "tf",
.target = "target",
.likelihood = "likelihood",
verbose = FALSE
)
#> # A tibble: 20 x 4
#> statistic tf condition score
#> <chr> <chr> <chr> <dbl>
#> 1 viper FOXO4 GSM2753335 1.34
#> 2 viper FOXO4 GSM2753336 1.18
#> 3 viper FOXO4 GSM2753337 1.44
#> 4 viper FOXO4 GSM2753338 1.21
#> 5 viper NFIC GSM2753335 0.0696
#> 6 viper NFIC GSM2753336 -0.0265
#> 7 viper NFIC GSM2753337 -0.516
#> 8 viper NFIC GSM2753338 -0.543
#> 9 viper RFXAP GSM2753335 0.488
#> 10 viper RFXAP GSM2753336 1.32
#> 11 viper RFXAP GSM2753337 1.93
#> 12 viper RFXAP GSM2753338 1.93
#> 13 viper SMAD3 GSM2753335 0.176
#> 14 viper SMAD3 GSM2753336 0.0426
#> 15 viper SMAD3 GSM2753337 0.219
#> 16 viper SMAD3 GSM2753338 0.142
#> 17 viper TFAP2A GSM2753335 0.722
#> 18 viper TFAP2A GSM2753336 0.582
#> 19 viper TFAP2A GSM2753337 0.462
#> 20 viper TFAP2A GSM2753338 0.330Are you interested in adding a new statistical method or collaborating in the development of internal tools that allow the extension of the package? Please check out our contribution guide.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.