digitalDLSorteR offers the possibility to use pre-trained context-specific deconvolution models included in digitalDLSorteRmodels package (https://github.com/diegommcc/digitalDLSorteRmodels) to deconvolute new bulk RNA-Seq samples from the same biological environment. This is the simplest way to use digitalDLSorteR and only requires loading into R (a raw bulk RNA-Seq matrix with genes as rows (annotated as SYMBOL) and samples as columns) and selecting the desired model. This is done by the deconvDigitalDLSorter
function which normalizes the new samples to counts per million (CPMs) by default, so this matrix must be provided as raw counts. Afterwards, estimated cell composition of each sample can be explored as a bar chart using the barPlotCellTypes
function (workflow summarized in the figure below).
So far, available models just cover two possible biological environments: breast cancer and colorectal cancer. These models are able to accurately deconvolute new samples from the same environment as they have been trained (with transcriptional profiles from these specific contexts).
There are two deconvolution models for breast cancer samples that differ in the level of specificity. Both have been trained using data from @Chung2017 (GSE75688).
breast.chung.generic
: it considers 13 cell types, four of them being the intrinsic molecular subtypes of breast cancer (ER+
, HER2+
, ER+/HER2+
and TNBC
) and the rest immune and stromal cells (Stromal
, Monocyte
, TCD4mem
(memory CD4+ T cells), BGC
(germinal center B cells), Bmem
(memory B cells), DC
(dendritic cells), Macrophage
, TCD8
(CD8+ T cells) and TCD4reg
(regulatory CD4+ T cells)).breast.chung.generic
: this model considers 7 cell types that are generic groups of the cell types considered by the specific version: B cells (Bcell
), T CD4+ cells (TcellCD4
), T CD8+ cells (TcellCD8
), monocytes (Monocyte
), dendritic cells (DCs
), stromal cells (Stromal
) and tumor cells (Tumor
).This model was created with data from @Li2017 (GSE81861). It is called colorectal.li
and considers 10 cell types: cancer cells (CRC
), epithelial (Ep
), monocytes (M
), macrophages (Mc
), fibroblasts (Fb
), CD4 T cells (CD4
), CD8 T cells (CD8Gp
), CD8 T cells (CD8Gn
), germinal center B cells (gB
) and mature B cells (pB
).
The following code chunk shows an example using breast.chung.generic
model and a small portion of data from The Cancer Genome Atlas (TCGA) project [@Koboldt2012; @Ciriello2015] loaded from digitalDLSorteRdata
:
suppressMessages(library(digitalDLSorteR)) # to load pre-trained models if (!requireNamespace("digitalDLSorteRmodels", quietly = TRUE)) { install.packages( "digitalDLSorteRmodels", repos = "https://diegommcc.github.io/digitalDLSorteRmodelsRepo/" ) } suppressMessages(library(digitalDLSorteRmodels)) # data for example if (!requireNamespace("digitalDLSorteRdata", quietly = TRUE)) { install.packages( "digitalDLSorteRdata", repos = "https://diegommcc.github.io/digitalDLSorteRdataRepo/" ) } suppressMessages(library(digitalDLSorteRdata)) # disable eager execution for compatibility issues tensorflow::tf$compat$v1$disable_eager_execution() # loading model and example data from digitalDLSorteRdata data(breast.chung.generic) data(TCGA.breast.small)
breast.chung.generic
is a DigitalDLSorterDNN-like list (list with the same structure as a DigitalDLSorterDNN
object) that can be inspected to check which cell types are considered by the model, how well it performed on test data, etc. As the digitalDLSorteRmodels package stores all these objects as DigitalDLSorterDNN-like lists in order to remove its dependency on digitalDLSorteR, you have to transform these lists into actual DigitalDLSorterDNN
objects if you want to inspect the models. This is done with the listToDDLSDNN
function.
breast.chung.generic <- listToDDLSDNN(breast.chung.generic) breast.chung.generic
## Trained model: 20 epochs ## Training metrics (last epoch): ## loss: 0.0222 ## accuracy: 0.9051 ## mean_absolute_error: 0.0186 ## categorical_accuracy: 0.9051 ## Evaluation metrics on test data: ## loss: 0.0154 ## accuracy: 0.9629 ## mean_absolute_error: 0.0112 ## categorical_accuracy: 0.9629
cell.types(breast.chung.generic)
## [1] "Tumor" "Stromal" "Monocyte" "TcellCD4" "Bcell" "DCs" "TcellCD8"
Now, we can use it to deconvolute TCGA.breast.small
samples as follows:
# deconvolution deconvResults <- deconvDigitalDLSorter( data = TCGA.breast.small, model = breast.chung.generic, normalize = TRUE )
## === Filtering 32876 features in data that are not present in trained model
## === Setting 11305 features that are not present in trained model to zero
## === Normalizing and scaling data
## === Predicting cell types present in the provided samples
## DONE
rownames(deconvResults) <- paste("Sample", seq(nrow(deconvResults)), sep = "_") head(deconvResults)
## Tumor Stromal Monocyte TcellCD4 Bcell DCs TcellCD8 ## Sample_1 0.5716878 0.009549672 0.02705323 0.0009312485 2.085090e-05 0.2735238 0.11723338 ## Sample_2 0.4737065 0.015726041 0.01532314 0.0008934758 1.881223e-05 0.3624099 0.13192214 ## Sample_3 0.5632827 0.011810658 0.02118549 0.0009122477 2.213673e-05 0.3083327 0.09445395 ## Sample_4 0.4798905 0.013469697 0.01989893 0.0007664507 4.999202e-05 0.3453780 0.14054649 ## Sample_5 0.6283218 0.015287977 0.07622542 0.0022742257 3.257442e-06 0.1749347 0.10295264 ## Sample_6 0.4960882 0.015261916 0.02924263 0.0010657815 1.409765e-05 0.3112173 0.14710997
deconvDigitalDLSorter
returns a data frame with the samples as rows ($k$) and the cell types considered by the model as columns ($j$). Each entry corresponds to the proportion of cell type $k$ in sample $i$. To visually evaluate these results using a bar chart, you can use the barplotCellTypes
function as follows:
barPlotCellTypes( deconvResults, color.line = "black", title = "Results of deconvolution of TCGA breast samples" )
Finally, deconvDigitalDLSorterObj
also offers two parameters in case you want to simplify the results by aggregating cell proportions of similar cell types: simplify.set
and simplify.majority
. For instance, we can summarize dendritic cells (DCs) and monocytes in the new class mononuclear phagocyte system (MPS) using simplify.set
as follows:
# deconvolution deconvResultsSum <- deconvDigitalDLSorter( data = TCGA.breast.small, model = breast.chung.generic, normalize = TRUE, simplify.set = list(MPS = c("DCs", "Monocyte")) )
## === Filtering 32876 features in data that are not present in trained model
## === Setting 11305 features that are not present in trained model to zero
## === Normalizing and scaling data
## === Predicting cell types present in the provided samples
## DONE
rownames(deconvResultsSum) <- paste("Sample", seq(nrow(deconvResults)), sep = "_") barPlotCellTypes( deconvResultsSum, rm.x.text = FALSE, color.line = "black", title = "Results of deconvolution of TCGA breast samples (simplified)" )
On the other hand, simplify.majority
does not create new classes but sums the proportions to the most abundant cell type from those provided in each sample. See the documentation and examples for more details.
New models are planned to be published soon, but you can contribute your own models by making them available to other users. Please do not hesitate to contact us! We will check the models and include them in the digitalDLSorteRmodels package.
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