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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6890ab3 | Dave Tang | 2026-03-31 | Cell Type Enrichment Analysis with xCell2 |
xCell2 provides methods for cell type enrichment analysis using cell type signatures. Cell type enrichment analysis and cellular deconvolution are computational techniques aimed at deciphering the cellular heterogeneity in bulk transcriptomics samples.
xCell2 is the successor to the original xCell package (Aran et al., 2017). It leverages the Cell Ontology to map hierarchical relationships between cell types and applies spillover correction to address overlapping signature genes.
Install xCell2.
options(repos = c(CRAN = "https://cloud.r-project.org"))
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("xCell2")Use pre-trained references from the xCell2 References Repository.
bpe_github_pages <- "https://dviraran.github.io/xCell2refs/references/BlueprintEncode.xCell2Ref.rds"
bpe_github <- "https://github.com/dviraran/xCell2refs/raw/refs/heads/main/references/BlueprintEncode.xCell2Ref.rds"
BlueprintEncode.xCell2Ref <- readRDS(url(bpe_github_pages, 'rb'))
test <- readRDS(url(bpe_github, 'rb'))
identical(BlueprintEncode.xCell2Ref, test)[1] TRUEClass.
class(BlueprintEncode.xCell2Ref)[1] "xCell2Object"
attr(,"package")
[1] "xCell2"Structure.
str(BlueprintEncode.xCell2Ref, max.level = 2)Formal class 'xCell2Object' [package "xCell2"] with 5 slots
..@ signatures :List of 14265
..@ dependencies:List of 43
..@ params : tibble [43 × 5] (S3: tbl_df/tbl/data.frame)
..@ spill_mat : num [1:43, 1:43] 1 0.25 0 0 0 0 0 0 0 0 ...
.. ..- attr(*, "dimnames")=List of 2
..@ genes_used : chr [1:19859] "TSPAN6" "TNMD" "DPM1" "SCYL3" ...Check what identifiers are used.
data("DICE_demo.xCell2Ref", package = "xCell2")
genes_ref <- getGenesUsed(DICE_demo.xCell2Ref)
genes_ref[1:10] [1] "CD28" "S100B" "IL2RB" "CD3E" "KRT72" "CTLA4" "CD3D" "GNLY" "IL7R"
[10] "MAL" Demo data.
data("mix_demo", package = "xCell2")
head(mix_demo) F0303 F0304 F0305
CD28 26.900 31.988 15.948
S100B 36.400 22.165 28.784
IL2RB 58.092 208.761 67.207
CD3E 182.613 251.774 156.480
KRT72 13.938 29.051 13.267
CTLA4 8.349 15.686 6.010Calculate the percentage of overlapping genes
genes_mix <- rownames(mix_demo)
overlap_percentage <- round(length(intersect(genes_mix, genes_ref)) / length(genes_ref) * 100, 2)
print(paste0("Overlap between mixture and reference genes is: ", overlap_percentage, "%"))[1] "Overlap between mixture and reference genes is: 100%"Ensure the overlap is sufficient (default minimum: 90%). You can
adjust this threshold in xCell2Analysis using the
minSharedGenes parameter:
xcell2_results <- xCell2Analysis(
mix = mix_demo,
xcell2object = DICE_demo.xCell2Ref,
minSharedGenes = 0.8 # Allow for a lower overlap threshold
)Starting xCell2 Analysis...Calculating enrichment scores for all cell types...Performing spillover correction...xCell2 Analysis completed successfully.class(xcell2_results)[1] "matrix" "array" Key Parameters of xCell2Analysis:
mix: The bulk mixture of gene expression matrix to
analyze, with genes in rows and samples in columns. The gene
nomenclature must match the reference, see Ensuring Gene
Compatibility.xcell2object: A pre-trained reference object of class
xCell2Object, created using the xCell2Train function. Pre-trained
references are also available for common use cases. See Using
Pre-trained xCell2 References.minSharedGenes: The minimum fraction of shared genes
required between the bulk mixture (mix) and the reference
(xcell2object). The default value is 0.9. If the overlap is
insufficient, the function will terminate with an error. Adjust this
threshold if necessary, but note that higher overlap ensures more
reliable results. To check gene overlap, see Ensuring Gene
Compatibility.rawScores: A Boolean indicating whether to return raw
enrichment scores (default: FALSE). Raw enrichment scores are computed
directly from gene signatures without applying linear transformation or
spillover correction. This option can be useful for debugging or
advanced workflows.spillover: A Boolean indicating whether to apply
spillover correction on the enrichment scores (default: TRUE). Spillover
correction addresses signature genes overlaps between closely related
cell types, improving specificity. For details, see How Does xCell2
Correct Spillover?.spilloverAlpha: A numeric value (default: 0.5)
controlling the strength of spillover correction. Lower values apply
weaker correction, while higher values apply stronger correction. Adjust
this parameter based on the similarity of cell types in your reference.
See Spillover Correction in xCell2.BPPARAM: A BiocParallelParam instance that determines
the parallelization strategy. This parameter allows you to leverage
multi-core processing for faster computation. For more details, see
Parallelization in xCell2.The xCell2Analysis function return a matrix of
cell type enrichment scores with the following structure:
xcell2_results F0303 F0304 F0305
B cells 0.006400000 0.000000000 0.00648
Monocytes 0.097510000 0.000000000 0.11778
NK cells 0.000000000 0.018030000 0.00200
T cells, CD8+ 0.006057754 0.001442649 0.00000
T cells, CD4+ 0.009153069 0.020716882 0.00000
T cells, CD4+, memory 0.001950000 0.009530000 0.00000
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xCell2_1.2.3 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] DBI_1.3.0 httr2_1.2.2
[3] GSEABase_1.72.0 rlang_1.1.7
[5] magrittr_2.0.4 git2r_0.36.2
[7] otel_0.2.0 matrixStats_1.5.0
[9] compiler_4.5.2 RSQLite_2.4.6
[11] getPass_0.2-4 reshape2_1.4.5
[13] png_0.1-9 callr_3.7.6
[15] vctrs_0.7.2 quadprog_1.5-8
[17] stringr_1.6.0 pkgconfig_2.0.3
[19] crayon_1.5.3 fastmap_1.2.0
[21] dbplyr_2.5.2 XVector_0.50.0
[23] promises_1.5.0 rmarkdown_2.31
[25] tzdb_0.5.0 pracma_2.4.6
[27] graph_1.88.1 ps_1.9.1
[29] purrr_1.2.1 bit_4.6.0
[31] xfun_0.57 Rfast_2.1.5.2
[33] cachem_1.1.0 jsonlite_2.0.0
[35] progress_1.2.3 blob_1.3.0
[37] later_1.4.8 DelayedArray_0.36.0
[39] BiocParallel_1.44.0 parallel_4.5.2
[41] prettyunits_1.2.0 singscore_1.30.0
[43] R6_2.6.1 RColorBrewer_1.1-3
[45] bslib_0.10.0 stringi_1.8.7
[47] limma_3.66.0 GenomicRanges_1.62.1
[49] jquerylib_0.1.4 Rcpp_1.1.1
[51] Seqinfo_1.0.0 SummarizedExperiment_1.40.0
[53] knitr_1.51 readr_2.2.0
[55] IRanges_2.44.0 httpuv_1.6.17
[57] Matrix_1.7-4 tidyselect_1.2.1
[59] rstudioapi_0.18.0 abind_1.4-8
[61] yaml_2.3.12 codetools_0.2-20
[63] minpack.lm_1.2-4 curl_7.0.0
[65] processx_3.8.6 plyr_1.8.9
[67] lattice_0.22-7 tibble_3.3.1
[69] S7_0.2.1 Biobase_2.70.0
[71] KEGGREST_1.50.0 evaluate_1.0.5
[73] ontologyIndex_2.12 RcppParallel_5.1.11-2
[75] BiocFileCache_3.0.0 Biostrings_2.78.0
[77] pillar_1.11.1 BiocManager_1.30.27
[79] filelock_1.0.3 MatrixGenerics_1.22.0
[81] whisker_0.4.1 stats4_4.5.2
[83] generics_0.1.4 rprojroot_2.1.1
[85] ggplot2_4.0.2 BiocVersion_3.22.0
[87] S4Vectors_0.48.0 hms_1.1.4
[89] scales_1.4.0 xtable_1.8-8
[91] glue_1.8.0 tools_4.5.2
[93] AnnotationHub_4.0.0 locfit_1.5-9.12
[95] annotate_1.88.0 fs_2.0.1
[97] XML_3.99-0.23 grid_4.5.2
[99] tidyr_1.3.2 edgeR_4.8.2
[101] AnnotationDbi_1.72.0 SingleCellExperiment_1.32.0
[103] cli_3.6.5 zigg_0.0.2
[105] rappdirs_0.3.4 S4Arrays_1.10.1
[107] dplyr_1.2.0 gtable_0.3.6
[109] sass_0.4.10 digest_0.6.39
[111] BiocGenerics_0.56.0 SparseArray_1.10.9
[113] farver_2.1.2 memoise_2.0.1
[115] htmltools_0.5.9 lifecycle_1.0.5
[117] httr_1.4.8 statmod_1.5.1
[119] bit64_4.6.0-1