Getting started


Authors: Brian M. Schilder, Alan Murphy, Julien Bryois & Nathan Skene


Updated: Feb-25-2025

Source: vignettes/MAGMA.Celltyping.Rmd

Intro

MAGMA.Celltyping is a software package that facilitates conducting cell-type-specific enrichment tests on GWAS summary statistics.

Setup

Specify where you want the large files to be downloaded to.

NOTE: Make sure you change storage_dir to somewhere other than tempdir() if you want to make sure the results aren’t deleted after this R session closes!

storage_dir <- tempdir()

Prepare data

GWAS

  • We need to have a summary statistics file to analyse as input.
  • As an example, you can download UK Biobank summary statistics for ‘fluid_intelligence’ using get_example_gwas().

Here we provide a pre-munged version of the above file.

Munging

Our lab have created MungeSumstats, a robust Bioconductor package for formatting multiple types of summary statistics files. We highly recommend processing your GWAS summary statistics with MungeSumstats before continuing. See the full_workflow vignette for more details.

The minimum info needed after munging is:
- “SNP”, “CHR”, and “BP” as first three columns. - It has at least one of these columns: “Z”,“OR”,“BETA”,“LOG_ODDS”,“SIGNED_SUMSTAT”

path_formatted <- MAGMA.Celltyping::get_example_gwas(
  trait = "prospective_memory")

Map SNPs to Genes

Note you can input the genome build of your summary statistics for this step or it can be inferred if left NULL:

genesOutPath <- MAGMA.Celltyping::map_snps_to_genes(
  path_formatted = path_formatted,
  genome_build = "GRCh37")

MAGMA_Files_Public

Rather than preprocessing the GWAS yourself, you can instead use the MAGMA_Files_Public database we have created. It contains pre-computed MAGMA SNP-to-genes mapping files for hundreds of GWAS.

You can browse which GWAS traits are available by looking at the provided metadata.csv file.

magma_dirs <- MAGMA.Celltyping::import_magma_files(ids = "ieu-a-298")
## Using built-in example files: ieu-a-298.tsv.gz.35UP.10DOWN
## Returning MAGMA directories.

CellTypeDataset

ewceData provides a number of CellTypeDatasets (CTD) to be used a cell-type transcriptomic signature reference files.

If you want to create your own single-cell transcriptomic reference, you’ll need to first convert it to CTD using the instructions found in the EWCE package documentation here.

ctd <- ewceData::ctd()
## see ?ewceData and browseVignettes('ewceData') for documentation
## loading from cache

Note that the cell type dataset loaded in the code above is the Karolinksa cortex/hippocampus data only. For the full Karolinska dataset with hypothalamus and midbrain instead use the following:

ctd <- MAGMA.Celltyping::get_ctd("ctd_allKI")

Or for the DRONC seq or AIBS datasets use:

ctd <- get_ctd("ctd_Tasic")
ctd <- get_ctd("ctd_DivSeq")
ctd <- get_ctd("ctd_AIBS")
ctd <- get_ctd("ctd_DRONC_human")
ctd <- get_ctd("ctd_DRONC_mouse")
ctd <- get_ctd("ctd_BlueLake2018_FrontalCortexOnly")
ctd <- get_ctd("ctd_BlueLake2018_VisualCortexOnly")
ctd <- get_ctd("ctd_Saunders")

Run cell-type enrichment analyses

MAGMA.Celltyping offers a suite of functions for conducting various types of cell-type-specific enrichment tests on GWAS summary statistics.

The celltype_associations_pipeline wraps several functions that in previous versions of MAGMA.Celltyping had to be set up and run separately. These include:

  • Linear enrichment: calculate_celltype_associations(EnrichmentMode = "linear") internally. Activated when run_linear=TRUE.
  • Top 10% enrichment: Uses calculate_celltype_associations(EnrichmentMode = "Top 10%") internally. Activated when run_top10=TRUE.
  • Conditional enrichment: Uses calculate_conditional_celltype_associations internally. Activated when run_conditional=TRUE.

Thus, celltype_associations_pipeline is designed to make these analyses easier to run.

MAGMA_results <- MAGMA.Celltyping::celltype_associations_pipeline(
  magma_dirs = magma_dirs,
  ctd = ctd,
  ctd_species = "mouse", 
  ctd_name = "Zeisel2015", 
  run_linear = TRUE, 
  run_top10 = TRUE)

We’ve also saved a pre-computed version of these results as a dataset:

MAGMA_results <- MAGMA.Celltyping::enrichment_results

Plot results

Merge results

merge_results imports each of the MAGMA enrichment results files and merges them into one so that they can easily be plotted and further analysed.

merged_results <- MAGMA.Celltyping::merge_results(
  MAGMA_results = MAGMA_results)
## Saving full merged results to ==> /tmp/RtmpCnpa8x/MAGMA_celltyping./.lvl1.csv
knitr::kable(merged_results)
GWAS Celltype TYPE OBS_GENES BETA BETA_STD SE P log10p level Method EnrichmentMode GCOV_FILE CONTROL CONTROL_label genesOutCOND analysis_name FDR Celltype_id
ieu-a-298.tsv.gz.35UP.10DOWN o l i g o d e n d r o c y t e s COVAR 962 0.0016776 0.0199640 0.0024613 0.24785 -0.6058111 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.867475 oligodendrocytes
ieu-a-298.tsv.gz.35UP.10DOWN a s t r o c y t e s _ e p e n d y m a l SET 104 0.0671670 0.0178860 0.0853000 0.21562 -0.6663110 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 astrocytes_ependymal
ieu-a-298.tsv.gz.35UP.10DOWN e n d o t h e l i a l _ m u r a l SET 98 0.0831670 0.0215500 0.0962320 0.19384 -0.7125566 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 endothelial_mural
ieu-a-298.tsv.gz.35UP.10DOWN o l i g o d e n d r o c y t e s SET 94 0.1124000 0.0285690 0.1017200 0.13472 -0.8705679 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 oligodendrocytes
ieu-a-298.tsv.gz.35UP.10DOWN a s t r o c y t e s _ e p e n d y m a l COVAR 962 -0.0003729 -0.0045268 0.0022566 0.56561 -0.2474829 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 astrocytes_ependymal
ieu-a-298.tsv.gz.35UP.10DOWN e n d o t h e l i a l _ m u r a l COVAR 962 0.0002391 0.0029144 0.0023811 0.46002 -0.3372233 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 endothelial_mural
ieu-a-298.tsv.gz.35UP.10DOWN i n t e r n e u r o n s COVAR 962 -0.0033333 -0.0393200 0.0024009 0.91728 -0.0374981 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 interneurons
ieu-a-298.tsv.gz.35UP.10DOWN m i c r o g l i a COVAR 962 -0.0002839 -0.0036131 0.0022446 0.55030 -0.2594005 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 microglia
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ C A 1 COVAR 962 -0.0037493 -0.0440450 0.0023802 0.94218 -0.0258661 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 pyramidal_CA1
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ S S COVAR 962 -0.0023657 -0.0282810 0.0023306 0.84480 -0.0732461 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 pyramidal_SS
ieu-a-298.tsv.gz.35UP.10DOWN i n t e r n e u r o n s SET 106 -0.1554800 -0.0417660 0.0911160 0.95587 -0.0196012 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.981580 interneurons
ieu-a-298.tsv.gz.35UP.10DOWN m i c r o g l i a SET 91 -0.0341170 -0.0085425 0.1093000 0.62250 -0.2058606 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.981580 microglia
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ C A 1 SET 111 -0.1930200 -0.0529550 0.0923320 0.98158 -0.0080743 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.981580 pyramidal_CA1
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ S S SET 98 -0.0750110 -0.0194370 0.0965900 0.78120 -0.1072378 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.981580 pyramidal_SS

Heatmap

Now we’ll construct a heatmap visualizing the enrichment results, such that each GWAS is shown on the y-axis and each cell-type is shown on the x-axis. Results can be further facetted by what kind of test was run (linear, top10%, and/or conditional).

heat <- MAGMA.Celltyping::results_heatmap(
  merged_results = merged_results, 
  title = "Alzheimer's Disease (ieu-a-298) vs.\nnervous system cell-types (Zeisel2015)",
  fdr_thresh = 1)
## 14 results @ FDR < 1
## Warning: The `facets` argument of `facet_grid()` is deprecated as of ggplot2 2.2.0.
## ℹ Please use the `rows` argument instead.
## ℹ The deprecated feature was likely used in the MAGMA.Celltyping package.
##   Please report the issue at
##   <https://github.com/neurogenomics/MAGMA_Celltyping/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Bar plot

Now we can also plot the p-value results (-log10 p-values) based on a significance threshold (for either linear and top10%) together in a bar plot (sorted by top10% results).

barplot_ggplot <- MAGMA.Celltyping::results_barplot(
  merged_results = merged_results,
  title="Alzheimer's Disease (ieu-a-298) vs.\nnervous system cell-types (Zeisel2015)",
  fdr_thresh = 1,
  horz_line_p = .2
)
## 14 results @ FDR < 1

Top results

Top phenotypes

Get the phenotypes with the greatest number of significant cell-type enrichment results.

top_phenos <- merged_results %>% 
  dplyr::group_by(EnrichmentMode, GWAS) %>%
  dplyr::summarise(Celltype=dplyr::n_distinct(Celltype)) %>%
  dplyr::arrange(dplyr::desc(Celltype))
## `summarise()` has grouped output by 'EnrichmentMode'. You can override using
## the `.groups` argument.
knitr::kable(top_phenos)
EnrichmentMode GWAS Celltype
Linear ieu-a-298.tsv.gz.35UP.10DOWN 7
Top 10% ieu-a-298.tsv.gz.35UP.10DOWN 7

Top enrichments

Get the phenotypes-celltype enrichment results with the most significant p-values (per phenotype).

top_enrich <- merged_results %>% 
  dplyr::group_by(EnrichmentMode, GWAS) %>%
  dplyr::slice_min(FDR, n = 2)
knitr::kable(top_enrich)
GWAS Celltype TYPE OBS_GENES BETA BETA_STD SE P log10p level Method EnrichmentMode GCOV_FILE CONTROL CONTROL_label genesOutCOND analysis_name FDR Celltype_id
ieu-a-298.tsv.gz.35UP.10DOWN o l i g o d e n d r o c y t e s COVAR 962 0.0016776 0.0199640 0.0024613 0.24785 -0.6058111 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.867475 oligodendrocytes
ieu-a-298.tsv.gz.35UP.10DOWN a s t r o c y t e s _ e p e n d y m a l COVAR 962 -0.0003729 -0.0045268 0.0022566 0.56561 -0.2474829 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 astrocytes_ependymal
ieu-a-298.tsv.gz.35UP.10DOWN e n d o t h e l i a l _ m u r a l COVAR 962 0.0002391 0.0029144 0.0023811 0.46002 -0.3372233 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 endothelial_mural
ieu-a-298.tsv.gz.35UP.10DOWN i n t e r n e u r o n s COVAR 962 -0.0033333 -0.0393200 0.0024009 0.91728 -0.0374981 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 interneurons
ieu-a-298.tsv.gz.35UP.10DOWN m i c r o g l i a COVAR 962 -0.0002839 -0.0036131 0.0022446 0.55030 -0.2594005 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 microglia
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ C A 1 COVAR 962 -0.0037493 -0.0440450 0.0023802 0.94218 -0.0258661 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 pyramidal_CA1
ieu-a-298.tsv.gz.35UP.10DOWN p y r a m i d a l _ S S COVAR 962 -0.0023657 -0.0282810 0.0023306 0.84480 -0.0732461 1 MAGMA Linear ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_linear.gsa.out BASELINE BASELINE NA Zeisel2015_linear 0.981580 pyramidal_SS
ieu-a-298.tsv.gz.35UP.10DOWN a s t r o c y t e s _ e p e n d y m a l SET 104 0.0671670 0.0178860 0.0853000 0.21562 -0.6663110 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 astrocytes_ependymal
ieu-a-298.tsv.gz.35UP.10DOWN e n d o t h e l i a l _ m u r a l SET 98 0.0831670 0.0215500 0.0962320 0.19384 -0.7125566 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 endothelial_mural
ieu-a-298.tsv.gz.35UP.10DOWN o l i g o d e n d r o c y t e s SET 94 0.1124000 0.0285690 0.1017200 0.13472 -0.8705679 1 MAGMA Top 10% ieu-a-298.tsv.gz.35UP.10DOWN.level1.Zeisel2015_top10.gsa.out BASELINE BASELINE NA Zeisel2015_top10 0.867475 oligodendrocytes

Session Info 

utils::sessionInfo()
## R Under development (unstable) (2025-02-15 r87726)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ewceData_1.15.0         ExperimentHub_2.15.0    AnnotationHub_3.15.0   
##  [4] BiocFileCache_2.15.1    dbplyr_2.5.0            BiocGenerics_0.53.6    
##  [7] generics_0.1.3          dplyr_1.1.4             MAGMA.Celltyping_2.0.15
## [10] BiocStyle_2.35.0       
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.5.0               BiocIO_1.17.1              
##   [3] bitops_1.0-9                ggplotify_0.1.2            
##   [5] filelock_1.0.3              tibble_3.2.1               
##   [7] R.oo_1.27.0                 XML_3.99-0.18              
##   [9] lifecycle_1.0.4             Rdpack_2.6.2               
##  [11] rstatix_0.7.2               lattice_0.22-6             
##  [13] MASS_7.3-64                 backports_1.5.0            
##  [15] magrittr_2.0.3              limma_3.63.4               
##  [17] plotly_4.10.4               sass_0.4.9                 
##  [19] rmarkdown_2.29              jquerylib_0.1.4            
##  [21] yaml_2.3.10                 HGNChelper_0.8.15          
##  [23] DBI_1.2.3                   minqa_1.2.8                
##  [25] abind_1.4-8                 GenomicRanges_1.59.1       
##  [27] purrr_1.0.4                 R.utils_2.13.0             
##  [29] RCurl_1.98-1.16             yulab.utils_0.2.0          
##  [31] VariantAnnotation_1.53.1    rappdirs_0.3.3             
##  [33] GenomeInfoDbData_1.2.13     IRanges_2.41.3             
##  [35] S4Vectors_0.45.4            tidytree_0.4.6             
##  [37] pkgdown_2.1.1               codetools_0.2-20           
##  [39] DelayedArray_0.33.6         tidyselect_1.2.1           
##  [41] aplot_0.2.4                 UCSC.utils_1.3.1           
##  [43] farver_2.1.2                lme4_1.1-36                
##  [45] matrixStats_1.5.0           stats4_4.5.0               
##  [47] GenomicAlignments_1.43.0    jsonlite_1.9.0             
##  [49] Formula_1.2-5               systemfonts_1.2.1          
##  [51] tools_4.5.0                 treeio_1.31.0              
##  [53] ragg_1.3.3                  Rcpp_1.0.14                
##  [55] glue_1.8.0                  SparseArray_1.7.6          
##  [57] xfun_0.51                   MatrixGenerics_1.19.1      
##  [59] GenomeInfoDb_1.43.4         RNOmni_1.0.1.2             
##  [61] withr_3.0.2                 BiocManager_1.30.25        
##  [63] fastmap_1.2.0               boot_1.3-31                
##  [65] digest_0.6.37               mime_0.12                  
##  [67] R6_2.6.1                    gridGraphics_0.5-1         
##  [69] textshaping_1.0.0           colorspace_2.1-1           
##  [71] RSQLite_2.3.9               R.methodsS3_1.8.2          
##  [73] tidyr_1.3.1                 data.table_1.17.0          
##  [75] rtracklayer_1.67.1          httr_1.4.7                 
##  [77] htmlwidgets_1.6.4           S4Arrays_1.7.3             
##  [79] pkgconfig_2.0.3             gtable_0.3.6               
##  [81] blob_1.2.4                  SingleCellExperiment_1.29.1
##  [83] XVector_0.47.2              htmltools_0.5.8.1          
##  [85] carData_3.0-5               bookdown_0.42              
##  [87] scales_1.3.0                Biobase_2.67.0             
##  [89] png_0.1-8                   reformulas_0.4.0           
##  [91] ggfun_0.1.8                 ggdendro_0.2.0             
##  [93] knitr_1.49                  reshape2_1.4.4             
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##  [97] curl_6.2.1                  nloptr_2.1.1               
##  [99] cachem_1.1.0                stringr_1.5.1              
## [101] BiocVersion_3.21.1          parallel_4.5.0             
## [103] AnnotationDbi_1.69.0        restfulr_0.0.15            
## [105] desc_1.4.3                  pillar_1.10.1              
## [107] grid_4.5.0                  vctrs_0.6.5                
## [109] ggpubr_0.6.0                car_3.1-3                  
## [111] evaluate_1.0.3              orthogene_1.13.0           
## [113] GenomicFeatures_1.59.1      cli_3.6.4                  
## [115] compiler_4.5.0              Rsamtools_2.23.1           
## [117] rlang_1.1.5                 crayon_1.5.3               
## [119] grr_0.9.5                   ggsignif_0.6.4             
## [121] labeling_0.4.3              gprofiler2_0.2.3           
## [123] EWCE_1.15.1                 ieugwasr_1.0.1             
## [125] plyr_1.8.9                  fs_1.6.5                   
## [127] stringi_1.8.4               viridisLite_0.4.2          
## [129] BiocParallel_1.41.2         babelgene_22.9             
## [131] munsell_0.5.1               Biostrings_2.75.4          
## [133] lazyeval_0.2.2              gh_1.4.1                   
## [135] homologene_1.4.68.19.3.27   Matrix_1.7-2               
## [137] MungeSumstats_1.15.12       BSgenome_1.75.1            
## [139] patchwork_1.3.0             bit64_4.6.0-1              
## [141] ggplot2_3.5.1               KEGGREST_1.47.0            
## [143] statmod_1.5.0               SummarizedExperiment_1.37.0
## [145] rbibutils_2.3               broom_1.0.7                
## [147] memoise_2.0.1               bslib_0.9.0                
## [149] ggtree_3.15.0               bit_4.5.0.1                
## [151] splitstackshape_1.4.8       ape_5.8-1