Getting started

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

Updated: Jan-26-2026

library(MAGMA.Celltyping) 
library(dplyr)

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/RtmpiTiKA6/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

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) (2026-01-25 r89330)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 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_1.19.0         ExperimentHub_3.1.0     AnnotationHub_4.1.0    
##  [4] BiocFileCache_3.1.0     dbplyr_2.5.1            BiocGenerics_0.57.0    
##  [7] generics_0.1.4          dplyr_1.1.4             MAGMA.Celltyping_2.0.16
## [10] BiocStyle_2.39.0       
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.6.0               BiocIO_1.21.0              
##   [3] bitops_1.0-9                ggplotify_0.1.3            
##   [5] filelock_1.0.3              tibble_3.3.1               
##   [7] R.oo_1.27.1                 XML_3.99-0.20              
##   [9] lifecycle_1.0.5             httr2_1.2.2                
##  [11] Rdpack_2.6.5                rstatix_0.7.3              
##  [13] lattice_0.22-7              MASS_7.3-65                
##  [15] backports_1.5.0             magrittr_2.0.4             
##  [17] limma_3.67.0                plotly_4.12.0              
##  [19] sass_0.4.10                 rmarkdown_2.30             
##  [21] jquerylib_0.1.4             yaml_2.3.12                
##  [23] otel_0.2.0                  HGNChelper_0.8.15          
##  [25] minqa_1.2.8                 DBI_1.2.3                  
##  [27] RColorBrewer_1.1-3          abind_1.4-8                
##  [29] GenomicRanges_1.63.1        purrr_1.2.1                
##  [31] R.utils_2.13.0              RCurl_1.98-1.17            
##  [33] yulab.utils_0.2.3           VariantAnnotation_1.57.1   
##  [35] rappdirs_0.3.4              gdtools_0.4.4              
##  [37] IRanges_2.45.0              S4Vectors_0.49.0           
##  [39] tidytree_0.4.7              pkgdown_2.2.0              
##  [41] codetools_0.2-20            DelayedArray_0.37.0        
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##  [45] UCSC.utils_1.7.1            farver_2.1.2               
##  [47] lme4_1.1-38                 matrixStats_1.5.0          
##  [49] stats4_4.6.0                Seqinfo_1.1.0              
##  [51] GenomicAlignments_1.47.0    jsonlite_2.0.0             
##  [53] Formula_1.2-5               systemfonts_1.3.1          
##  [55] tools_4.6.0                 treeio_1.35.0              
##  [57] ragg_1.5.0                  Rcpp_1.1.1                 
##  [59] glue_1.8.0                  SparseArray_1.11.10        
##  [61] xfun_0.56                   MatrixGenerics_1.23.0      
##  [63] GenomeInfoDb_1.47.2         RNOmni_1.0.1.2             
##  [65] withr_3.0.2                 BiocManager_1.30.27        
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##  [69] digest_0.6.39               R6_2.6.1                   
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## [107] BiocVersion_3.23.1          parallel_4.6.0             
## [109] AnnotationDbi_1.73.0        restfulr_0.0.16            
## [111] desc_1.4.3                  pillar_1.11.1              
## [113] grid_4.6.0                  vctrs_0.7.1                
## [115] ggpubr_0.6.2                car_3.1-3                  
## [117] evaluate_1.0.5              orthogene_1.17.2           
## [119] GenomicFeatures_1.63.1      cli_3.6.5                  
## [121] compiler_4.6.0              Rsamtools_2.27.0           
## [123] rlang_1.1.7                 crayon_1.5.3               
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## [139] gh_1.5.0                    homologene_1.4.68.19.3.27  
## [141] fontquiver_0.2.1            Matrix_1.7-4               
## [143] MungeSumstats_1.19.5        BSgenome_1.79.1            
## [145] patchwork_1.3.2             bit64_4.6.0-1              
## [147] ggplot2_4.0.1               KEGGREST_1.51.1            
## [149] statmod_1.5.1               SummarizedExperiment_1.41.0
## [151] rbibutils_2.4.1             broom_1.0.11               
## [153] memoise_2.0.1               bslib_0.10.0               
## [155] ggtree_4.1.1                bit_4.6.0                  
## [157] splitstackshape_1.4.8       ape_5.8-1

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