EPIC: Estimate the proportion of immune and cancer cells.
In GfellerLab/EPIC: Estimate the Proportion of Immune and Cancer cells

EPIC	R Documentation

Estimate the proportion of immune and cancer cells.

Description

EPIC takes as input bulk gene expression data (RNA-seq) and returns the proportion of mRNA and cells composing the various samples.

Usage

EPIC(
  bulk,
  reference = NULL,
  mRNA_cell = NULL,
  mRNA_cell_sub = NULL,
  sigGenes = NULL,
  scaleExprs = TRUE,
  withOtherCells = TRUE,
  constrainedSum = TRUE,
  rangeBasedOptim = FALSE
)

Arguments

`bulk`	A matrix (`nGenes` x `nSamples`) of the genes expression from each bulk sample (the counts should be given in TPM, RPKM or FPKM when using the prebuilt reference profiles). This matrix needs to have rownames telling the gene names (corresponds to the gene symbol in the prebuilt reference profiles (e.g. CD8A, MS4A1) - no conversion of IDs is performed at the moment). It is advised to keep all genes in the bulk instead of a subset of signature genes (except if `scaleExprs = FALSE` in which case it doesn't make any difference).
`reference`	(optional): A string or a list defining the reference cells. It can take multiple formats, either: `NULL`: to use the default reference profiles and genes signature `TRef`. a char: either "BRef" or "TRef" to use the reference cells and genes signature of the corresponding datasets (see `BRef` and `TRef`). a list. When a list it should include: `$refProfiles` a matrix (`nGenes` x `nCellTypes`) of the reference cells genes expression (don't include a column of the 'other cells' (representing usually the cancer cells for which such a profile is usually not conserved between samples); the rownames needs to be defined as well as the colnames giving the names of each gene and reference cell types respectively. It is advised to keep all genes in this `refProfiles` matrix instead of a subset of signature genes; `$sigGenes` a character vector of the gene names to use as signature - sigGenes can also be given as a direct input to EPIC function; `$refProfiles.var` (optional): a matrix (`nGenes` x `nCellTypes`) of the variability of each gene expression for each cell type, which is used to define weights on each gene for the optimization (if this is absent, we assume an identical variability for all genes in all cells) - it needs to have the same dimnames than refProfiles;
`mRNA_cell`	(optional): A named numeric vector: tells (in arbitrary units) the amount of mRNA for each of the reference cells and of the other uncharacterized (cancer) cell. Two names are of special meaning: "otherCells" - used for the mRNA/cell value of the "other cells" from the sample (i.e. the cell type that don't have any reference gene expression profile) ; and default - used for the mRNA/cell of the cells from the reference profiles for which no specific value is given in mRNA_cell (i.e. if mRNA_cell=c(Bcells=2, NKcells=2.1, otherCells=3.5, default=1), then if the refProfiles described Bcells, NKcells and Tcells, we would use a value of 3.5 for the "otherCells" that didn't have any reference profile and a default value of 1 for the Tcells when computing the cell fractions). To note: if data is in tpm, this mRNA per cell would ideally correspond to some number of transcripts per cell.
`mRNA_cell_sub`	(optional): This can be given instead of `mRNA_cell` (or in addition to it). It is also a named numeric vector, used to replace only the mRNA/cell values from some cell types (or to add values for new cell types). The values given in mRNA_cell_sub will overwrite the default values as well as those that might have been given by mRNA_cell.
`sigGenes`	(optional): a character vector of the gene names to use as signature for the deconvolution. In principle this is given with the reference as the "reference$sigGenes" but if we give a value for this input variable, it is these signature genes that will be used instead of the ones given with the reference profile.
`scaleExprs`	(optional, default is TRUE): boolean telling if the bulk samples and reference gene expression profiles should be rescaled based on the list of genes in common between the them (such a rescaling is recommanded).
`withOtherCells`	(optional, default is TRUE): if EPIC should allow for an additional cell type for which no gene expression reference profile is available or if the bulk is assumed to be composed only of the cells with reference profiles.
`constrainedSum`	(optional, default is TRUE): tells if the sum of all cell types should be constrained to be < 1. When `withOtherCells=FALSE`, there is additionally a constrain the the sum of all cell types with reference profiles must be > 0.99.
`rangeBasedOptim`	(optional): when this is FALSE (the default), the least square optimization is performed as described in Racle et al., 2017, eLife, which is recommanded. When this variable is TRUE, EPIC uses the variability of each gene from the reference profiles in another way: instead of defining weights (based on the variability) for the fit of each gene, we define a range of values accessible for each gene (based on the gene expression value in the reference profile +/- the variability values). The error that the optimization tries to minimize is by how much the predicted gene expression is outside of this allowed range of values.

Details

This function uses a constrained least square minimization to estimate the proportion of each cell type with a reference profile and another uncharacterized cell type in bulk gene expression samples.

The names of the genes in the bulk samples, the reference samples and in the gene signature list need to be the same format (gene symbols are used in the predefined reference profiles). The full list of gene names don't need to be exactly the same between the reference and bulk samples: EPIC will use the intersection of the genes. In case of duplicate gene names, EPIC will use the median value per duplicate - if you want to consider these cases differently, you can remove the duplicates before calling EPIC.

Value

A list of 3 matrices:

mRNAProportions: (nSamples x (nCellTypes+1)) the proportion of mRNA coming from all cell types with a ref profile + the uncharacterized other cell. Please note that if working with reconstructed in silico bulk samples built for example from single-cell RNA-seq data, then you should compare the 'true' proportions against these 'mRNAProportions', while if working with true bulk samples, then you should compare the cell proportions against the 'cellFractions'.
cellFractions: (nSamples x (nCellTypes+1)) this gives the proportion of cells from each cell type after accounting for the mRNA / cell value.
fit.gof: (nSamples x 12) a matrix telling the quality for the fit of the signature genes in each sample. It tells if the minimization converged, and other info about this fit comparing the measured gene expression in the sigGenes vs predicted gene expression in the sigGenes.

Examples

res1 <- EPIC(melanoma_data$counts)
res1$cellFractions
res2 <- EPIC(melanoma_data$counts, TRef)
res3 <- EPIC(bulk=melanoma_data$counts, reference=TRef)
res4 <- EPIC(melanoma_data$counts, reference="TRef")
res5 <- EPIC(melanoma_data$counts, mRNA_cell_sub=c(Bcells=1, otherCells=5))
# Various possible ways of calling EPIC function. res 1 to 4 should
# give exactly the same outputs, and the elements res1$cellFractions
# should be equal to the example predictions found in
# melanoma_data$cellFractions.pred for these first 4 results.
# The values of cellFraction for res5 will be different due to the use of
# other mRNA per cell values for the B and other cells.

GfellerLab/EPIC documentation built on July 22, 2023, 2:06 a.m.