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EPIC.Rmd
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EPIC.Rmd
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---
title: "EPIC package"
author: "Julien Racle and David Gfeller"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{EPIC package}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Description
Package implementing EPIC method to estimate the proportion of immune, stromal,
endothelial and cancer or other cells from bulk gene expression data.
It is based on reference gene expression profiles for the main non-malignant
cell types and it predicts the proportion of these cells and of the remaining
"other cells" (that are mostly cancer cells) for which no reference profile is
given.
This method is described in the publication from *Racle et al., 2017* available
at <https://elifesciences.org/articles/26476>.
EPIC is also available as a web application: <http://epic.gfellerlab.org>.
## Usage
The main function in this package is `EPIC`. It needs as input a matrix of the
TPM (or RPKM) gene expression from the samples for which to estimate cell
proportions. One can also define the reference cells to use
```{r, eval = FALSE}
# library(EPIC) ## If the package isn't loaded (or use EPIC::EPIC and so on).
out <- EPIC(bulk = bulkSamplesMatrix)
out <- EPIC(bulk = bulkSamplesMatrix, reference = referenceCellsList)
```
`out` is a list containing the various mRNA and cell fractions in each samples as well as some *data.frame* of the goodness of fit.
Values of mRNA per cell and signature genes to use can also be changed:
```{r, eval = FALSE}
out <- EPIC(bulk = bulkSamplesMatrix, reference = referenceCellsList, mRNA_cell = mRNA_cell_vector, sigGenes = sigGenes_vector)
out <- EPIC(bulk = bulkSamplesMatrix, reference = referenceCellsList, mRNA_cell_sub = mRNA_cell_sub_vector)
```
Various other options are available and are well documented in the help pages
from EPIC:
```{r, eval = FALSE}
?EPIC::EPIC
?EPIC::EPIC.package
```
## Installation
```{r, eval = FALSE}
install.packages("devtools")
devtools::install_github("GfellerLab/EPIC", build_vignettes=TRUE)
```
## Web application
EPIC is also available as a web application: <http://epic.gfellerlab.org>.
## Python wrapper
A pyhton wrapper has been written by Stephen C. Van Nostrand from MIT and is
available at <https://github.com/scvannost/epicpy>.
## License
EPIC can be used freely by academic groups for non-commercial purposes. The
product is provided free of charge, and, therefore, on an "*as is*" basis,
without warranty of any kind. Please read the file "*LICENSE*" for details.
If you plan to use EPIC (version 1.1) in any for-profit application, you are
required to obtain a separate license.
To do so, please contact Nadette Bulgin
([nbulgin@lcr.org](mailto:nbulgin@lcr.org)) at the Ludwig Institute for
Cancer Research Ltd.
## Contact information
Julien Racle ([julien.racle@unil.ch](mailto:julien.racle@unil.ch)),
and David Gfeller ([david.gfeller@unil.ch](mailto:david.gfeller@unil.ch)).
## FAQ
##### Which proportions returned by EPIC should I use?
* EPIC is returning two proportion values: *mRNAProportions* and *cellFractions*,
where the 2nd represents the true proportion of cells coming from the different
cell types when considering differences in mRNA expression between cell types.
So in principle, it is best to consider these *cellFractions*.
However, please note, that when the goal is to benchmark EPIC predictions, if
the 'bulk samples' correspond in fact to in silico samples reconstructed for
example from single-cell RNA-seq data, then it is usually better to compare the
'true' proportions against the *mRNAProportions* from EPIC. Indeed, when
building such in silico samples, the fact that different cell types express
different amount of mRNA is usually not taken into account. On the other side,
if working with true bulk samples, then you should compare the true cell
proportions (measured e.g., by FACS) against the *cellFractions*.
##### What do the "*other cells*" represent?
* EPIC predicts the proportions of the various cell types for which we have
gene expression reference profiles (and corresponding gene signatures). But,
depending on the bulk sample, it is possible that some other cell types are
present for which we don't have any reference profile. EPIC returns the
proportion of these remaining cells under the name "*other cells*". In the
case of tumor samples, most of these other cells would certainly correspond
to the cancer cells, but it could be that there are also some stromal cells or
epithelial cells for example.
##### I receive an error message "*attempt to set 'colnames' on an object with less than two dimensions*". What can I do?
* This is certainly that some of your data is a vector instead of a matrix.
Please make sure that your bulk data is in the form of a matrix (and also
your reference gene expression profiles if using custom ones).
##### Is there some caution to consider about the *cellFractions* and *mRNA_cell* values?
* As described in our manuscript, EPIC first estimates the proportion of mRNA
per cell type in the bulk and then it uses the fact that some cell types have
more mRNA copies per cell than other to normalize this and obtain an estimate of
the proportion of cells instead of mRNA (EPIC function returns both information
if you need the one or the other). For this normalization we had either measured
the amount of mRNA per cell or found it in the literature (fig. 1 – fig.
supplement 2 of our paper). However we don’t currently have such values for the
endothelial cells and CAFs. Therefore for these two cell types, we use an average
value, which might not reflect their true value and this could bias a bit the
predictions, especially for these cell types. If you have some values for these
mRNA/cell abundances, you can also add them into EPIC, with help of the parameter
"*mRNA_cell*" or “*mRNA_cell_sub*” (and that would be great to share these values).
If the mRNA proportions of these cell types are low, then even if you don't
correct the results with their true mRNA/cell abundances, it would not really
have a big impact on the results. On the other side, if there are many of these
cells in your bulk sample, the results might be a little bit biased, but the
effect should be similar for all samples and thus not have a too big importance
(maybe you wouldn’t be fully able to tell if there are more CAFs than Tcells for
example, but you should still have a good estimate of which sample has more CAFs
(or Tcells) than which other sample for example).
##### I receive a warning message that "*the optimization didn't fully converge for some samples*". What does it mean?
* When estimating the cell proportions EPIC performs a least square regression between the observed expression of the signature genes and the expression of these genes predicted based on the estimated proportions and gene expression reference profiles of the various cell types.
When such a warning message appears, it means that the optimization didn’t manage to fully converge for this regression, for some of the samples. You can then check the "*fit.gof\$convergeCode*" (and possibly also "*fit.gof\$convergeMessage*") that is outputted by EPIC alongside the cell proportions. This will tell you which samples had issue with the convergence (a value of 0 means it converged ok, while other values are errors/warnings, their meaning can be found in the help of "*optim*" (or "*constrOptim*") function from R (from "*stats*" package) which is used during the optimization and we simply forward the message it returns).
The error code that usually comes is a "1" which means that the maximum number of iterations has been reached in the optimization. This could mean there is an issue with the bulk gene expression data that maybe don’t completely follow the assumption of equation (1) from our manuscript. From our experience, it seems in practice that even when there was such a warning message the proportions were predicted well, it is maybe that the optimization just wants to be *too precise*, or maybe few of the signature genes didn’t match well but the rest of signature genes could be used to have a good estimate of the proportions.
If you have some samples that seem to have strange results, it could however be useful to check that the issue is not that these samples didn’t converge well. To
be more conservative you could also remove all the samples that didn't converge
well as these are maybe outliers, if it is only a small fraction from your original samples. Another possibility would be to change the parameters of the optim/constrOptim function to allow for more iterations or maybe a weaker tolerance for the convergence, but for this you would need to tweak it directly in the code of EPIC, I didn't implement such option for EPIC.
##### Who should I contact in case of a technical or other issue?
* Julien Racle ([julien.racle@unil.ch](mailto:julien.racle@unil.ch)). Please
provide as much details as possible and ideally send also an example input file (and/or reference profiles) that is causing the issue.