The digitalDLSorteR R package provides a set of tools to deconvolute and infer cell type proportions of bulk RNA-Seq data through the development of context-specific deconvolution models based on Deep Learning and single-cell RNA-Seq (scRNA-Seq) data. These models are able to accurately enumerate and quantify cell proportions of bulk RNA-Seq samples from specific biological environments. For more details about the algorithm and the functionalities implemented in this package, see Torroja and Sanchez-Cabo, 2019 and https://diegommcc.github.io/digitalDLSorteR/.
digitalDLSorteR is available on CRAN and can be installed as follows:
The development version is available on GitHub and can be also installed in R:
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") devtools::install_github("diegommcc/digitalDLSorteR")
The package depends on the tensorflow R package, so a working Python interpreter with the Tensorflow Python library installed is needed. The
installTFpython function provides an easy way to install a conda environment called
digitaldlsorter-env with all necessary dependencies covered. We recommend installing the TensorFlow Python library in this way, although a custom installation is possible. See the Keras/TensorFlow installation and configuration article of the package website for more details.
The algorithm consists of training Deep Neural Network (DNN) models with simulated bulk RNA-Seq samples whose cell composition is known. These pseudo-bulk RNA-Seq samples are generated by aggregating pre-characterized scRNA-Seq data from specific biological environments. These models are able to accurately deconvolute new bulk RNA-Seq samples from the same environment, as they are able to account for possible environmental-dependent transcriptional changes of specific cells, such as immune cells in complex diseases (e.g., specific subtypes of cancer or atherosclerosis). This aspect overcomes this limitation present in other methods. For instance, in the case of immune cells, published methods often rely on purified transcriptional profiles from peripheral blood mononuclear cells despite the fact these cells are highly variable depending on enviromental conditions. Therefore, considering this feature together with the use of powerful DNN models and the fact that the improvement of scRNA-Seq datasets over time will lead to build more accurate models, digitalDLSorteR offers a good alternative to classical methods based on linear models with pre-defined markers and unreliable transcriptional references.
The package has two main ways of usage:
To use pre-trained context specific deconvolution models, digitalDLSorteR depends on digitalDLSorteRmodels data package as it makes them available. Therefore, it should be installed together with digitalDLSorteR if this functionality wants to be used. To do so, it can be installed from GitHub using devtools:
Once digitalDLSorteRmodels is loaded, the pre-trained models are available. See the article Using pre-trained context-specific deconvolution models for some examples.
In addition, some examples and the vignettes of digitalDLSorteR make use of pre-computed datasets from the digitalDLSorteRdata R package. If you want to inspect these pre-computed DigitalDLSorter objects, you can install it from GitHub using devtools as follows. See Performance of a real model: deconvolution of colorectal cancer samples for an example.
|Chung, W., Eum, H. H., Lee, H. O., Lee, K. M., Lee, H. B., Kim, K. T., et al. (2017). Single-cell RNA-Seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8 (1) 15081 doi:10.1038/ncomms15081|
|Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., et al. (2017). Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49 (5), 708-718 doi:10.1038/ng.3818|
|Torroja, C. and Sánchez-Cabo, F. (2019). digitalDLSorter: A Deep Learning algorithm to quantify immune cell populations based on scRNA-Seq data. Frontiers in Genetics 10 978 doi:10.3389/fgene.2019.00978|