README.md
In lahuuki/DeconvoBuddies: Helper Functions for LIBD Deconvolution

DeconvoBuddies

The goal of DeconvoBuddies is to provide helper functions for the deconvolution process

Get the latest stable R release from CRAN. Then install DeconvoBuddies using from Bioconductor the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("DeconvoBuddies")

And the development version from GitHub with:

BiocManager::install("LieberInstitute/DeconvoBuddies")

#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: 'matrixStats'
#> The following object is masked from 'package:dplyr':
#> 
#>     count
#> 
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:dplyr':
#> 
#>     combine, intersect, setdiff, union
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
#>     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
#>     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
#>     table, tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: 'S4Vectors'
#> The following objects are masked from 'package:dplyr':
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#>     first, rename
#> The following object is masked from 'package:utils':
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#>     findMatches
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#>     expand.grid, I, unname
#> Loading required package: IRanges
#> 
#> Attaching package: 'IRanges'
#> The following objects are masked from 'package:dplyr':
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#>     collapse, desc, slice
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
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#>     rowMedians
#> The following objects are masked from 'package:matrixStats':
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#>     anyMissing, rowMedians

Get mean ratios for each gene x cell type

ratios <- get_mean_ratio2(sce.test)
fc <- findMarkers_1vAll(sce.test)
#> Inhib.2 - '2024-04-25 13:04:18.112684
#> Inhib.1 - '2024-04-25 13:04:18.658684
#> OPC - '2024-04-25 13:04:18.863432
#> Astro - '2024-04-25 13:04:19.091077
#> Excit.2 - '2024-04-25 13:04:19.313
#> Oligo - '2024-04-25 13:04:19.496721
#> Micro - '2024-04-25 13:04:19.681585
#> Excit.1 - '2024-04-25 13:04:19.866671
#> Building Table - 2024-04-25 13:04:20.061211
#> ** Done! **

(marker_stats <- left_join(ratios, fc, by = c("gene", "cellType.target")))
#> # A tibble: 1,778 × 15
#>    gene       cellType.target mean.target cellType  mean ratio rank_ratio Symbol
#>    <chr>      <fct>                 <dbl> <fct>    <dbl> <dbl>      <int> <chr> 
#>  1 ENSG00000… Inhib.2               1.00  Excit.2  0.239  4.20          1 AL139…
#>  2 ENSG00000… Inhib.2               1.71  Astro    0.512  3.35          2 SDC3  
#>  3 ENSG00000… Inhib.2               0.950 Astro    0.413  2.30          3 IFI44 
#>  4 ENSG00000… Inhib.2               3.32  Astro    1.47   2.26          4 COL11…
#>  5 ENSG00000… Inhib.2               3.55  Astro    1.62   2.19          5 NTNG1 
#>  6 ENSG00000… Inhib.2               1.22  Excit.1  0.560  2.18          6 TRIM62
#>  7 ENSG00000… Inhib.2               3.37  Excit.2  1.96   1.72          7 USP24 
#>  8 ENSG00000… Inhib.2               3.42  Inhib.1  2.44   1.40          8 SPATA6
#>  9 ENSG00000… Inhib.2               1.21  Astro    0.914  1.33          9 ABCD3 
#> 10 ENSG00000… Inhib.2               1.21  Astro    0.920  1.32         10 GNG12 
#> # ℹ 1,768 more rows
#> # ℹ 7 more variables: anno_ratio <chr>, logFC <dbl>, log.p.value <dbl>,
#> #   log.FDR <dbl>, std.logFC <dbl>, rank_marker <int>, anno_logFC <chr>

cell_types <- levels(sce.test$cellType)
cell_colors <- create_cell_colors(cell_types = cell_types, pallet = "classic", split = "\\.", preview = TRUE)

sce_symbol <- sce.test
rownames(sce_symbol) <- rowData(sce.test)$Symbol
plot_gene_express(sce = sce_symbol, genes = c("RNF220", "CSF3R"))

plot_marker_express(sce.test,
    marker_stats,
    "Astro",
    n_genes = 5,
    rank_col = "rank_ratio",
    anno_col = "anno_ratio",
    color_pal = cell_colors
)

pd <- SummarizedExperiment::colData(rse_bulk_test) %>%
    as.data.frame()

est_prop_long <- est_prop %>%
    tibble::rownames_to_column("RNum") %>%
    tidyr::pivot_longer(!RNum, names_to = "cell_type", values_to = "prop") %>%
    dplyr::left_join(pd %>% dplyr::select(RNum, Dx)) %>%
    dplyr::mutate(a = "a")
#> Joining with `by = join_by(RNum)`

plot_composition_bar(est_prop_long)

plot_composition_bar(est_prop_long, x_col = "Dx")

plot_composition_bar(est_prop_long, x_col = "Dx", min_prop_text = 0.1)

Below is the citation output from using citation('DeconvoBuddies') in R. Please run this yourself to check for any updates on how to cite DeconvoBuddies.

print(citation("DeconvoBuddies"), bibtex = TRUE)
#> To cite package 'DeconvoBuddies' in publications use:
#> 
#>   Huuki-Myers LA, Maynard KR, Hicks SC, Zandi P, Kleinman JE, Hyde TM,
#>   Goes FS, Collado-Torres L (2024). _DeconvoBuddies: a R/Bioconductor
#>   package with deconvolution helper functions_.
#>   doi:10.18129/B9.bioc.DeconvoBuddies
#>   <https://doi.org/10.18129/B9.bioc.DeconvoBuddies>,
#>   https://github.com/LieberInstitute/DeconvoBuddies/DeconvoBuddies - R
#>   package version 0.99.0,
#>   <http://www.bioconductor.org/packages/DeconvoBuddies>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {DeconvoBuddies: a R/Bioconductor package with deconvolution helper functions},
#>     author = {Louise A. Huuki-Myers and Kristen R. Maynard and Stephanie C. Hicks and Peter Zandi and Joel E. Kleinman and Tom M. Hyde and Fernando S. Goes and Leonardo Collado-Torres},
#>     year = {2024},
#>     url = {http://www.bioconductor.org/packages/DeconvoBuddies},
#>     note = {https://github.com/LieberInstitute/DeconvoBuddies/DeconvoBuddies - R package version 0.99.0},
#>     doi = {10.18129/B9.bioc.DeconvoBuddies},
#>   }

Please note that the DeconvoBuddies was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Please note that the DeconvoBuddies project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Continuous code testing is possible thanks to GitHub actions through usethis, remotes, and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
Code coverage assessment is possible thanks to codecov and covr.
The documentation website is automatically updated thanks to pkgdown.
The code is styled automatically thanks to styler.
The documentation is formatted thanks to devtools and roxygen2.