Detailed Usage Example - Mouse data • immunedeconv

library(dplyr)
library(ggplot2)
library(tidyr)
library(immunedeconv)
library(tibble)

As previously done for the human deconvolution methods, Immunedeconv includes an example dataset with samples from mouse blood and spleen from (Petitprez et al. 2020). It is available from immunedeconv::dataset_petitprez. It contains a gene expression matrix (dataset_petitprez$expr_mat) generated using bulk RNA-seq and ‘gold standard’ estimates of immune cell contents profiled with FACS (dataset_petitprez$ref). We are going to use the bulk RNA-seq data to run the deconvolution methods and will compare the results to the FACS data later on.

# show the first 5 lines of the gene expression matrix
knitr::kable(dataset_petitprez$expr_mat[1:5, ])

	MCP2_BL1	MCP2_BL2	MCP2_Sp1	MCP2_Sp2	MCP4_BL1	MCP4_BL2
Rn18s	10126.783	20242.132	5804.110	7848.710	16926.178	14015.107
mt-Co1	7412.649	7498.994	21293.790	18879.589	6666.412	7417.677
Eef1a1	4309.191	4917.897	9011.338	9093.948	4353.079	3924.303
Gm10925	3646.178	3456.046	13329.570	11212.637	4333.629	5104.390
Tpt1	4762.629	4677.535	8284.424	7918.483	4471.157	4006.737

Deconvolution with mouse-based methods

To estimate immune cell fractions, we simply have to invoke the deconvolute_mouse function. It requires the specification of one of the following methods for deconvolution:

deconvolution_methods_mouse

##    mMCPcounter      seqImmuCC            DCQ           BASE 
## "mmcp_counter"    "seqimmucc"          "dcq"         "base"

For this example, we use mMCPcounter. As a result, we obtain a cell_type x sample data frame with cell-type scores for each sample.

res_mMCPcounter <- deconvolute_mouse(dataset_petitprez$expr_mat, "mmcp_counter")

Similarly to its human counterpart, mMCP-counter provides scores in arbitrary units that are only comparable between samples, but not between cell-types.

res_mMCPcounter <- res_mMCPcounter[res_mMCPcounter$cell_type %in% colnames(dataset_petitprez$ref), ]


res_mMCPcounter %>%
  gather(sample, score, -cell_type) %>%
  ggplot(aes(x = sample, y = score, color = cell_type)) +
  geom_point(size = 4) +
  facet_wrap(~cell_type, scales = "free_x", ncol = 3) +
  scale_color_brewer(palette = "Paired", guide = FALSE) +
  coord_flip() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Deconvolution with human-based methods

Human-based methods can still be used to deconvolve mouse data through the use of orthologous genes. The function mouse_genes_to_human does that by retrieving the correspondent gene names with biomaRt. Since the gene names are retrieved from the Ensembl database, it can happen that the command has to be run with different Emsembl mirrors (see the documentation)

dataset_petitprez_humanGenes <- convert_human_mouse_genes(dataset_petitprez$expr_mat, convert_to = 'human')
res_MCPcounter <- deconvolute(dataset_petitprez_humanGenes, 'mcp_counter')

Comparison with FACS data

Let’s now compare the results with ‘gold standard’ FACS data obtained for the four samples. This is, of course, not a representative benchmark, but it gives a notion about what magnitude of predictive accuracy we can expect.

# construct a single dataframe containing all data
#
# re-map the cell-types to common names.
# only include the cell-types that are measured using FACS
cell_types <- c("B cell", "T cell CD8+", "T cell", "NK cell", "Monocyte")

tmp_res <- res_mMCPcounter %>%
  gather("sample", "estimate", -cell_type)

reference_facs <- dataset_petitprez$ref %>%
  gather("cell_type", "true_fraction", -"Sample Name") %>%
  set_colnames(., c("sample", "cell_type", "true_fraction"))

result <- tmp_res %>%
  inner_join(reference_facs)

Plot the true vs. estimated values:

result %>%
  ggplot(aes(x = true_fraction, y = estimate)) +
  geom_point(aes(color = cell_type)) +
  facet_wrap(. ~ cell_type, scales = "free_y", ncol = 2) +
  theme_bw()

References

Petitprez, Florent, Sacha Levy, Cheng-Ming Sun, Maxime Meylan, Christophe Linhard, Etienne Becht, Nabila Elarouci, et al. 2020. “The Murine Microenvironment Cell Population Counter Method to Estimate Abundance of Tissue-Infiltrating Immune and Stromal Cell Populations in Murine Samples Using Gene Expression.” Genome Medicine 12 (1): 1–15.