Gemma.R contains a large number of datasets representing a wide array of conditions. These datasets are manually annotated by our curators to facilitate discovery and categorization making it a useful tool for meta-analysis.

In this example we search Gemma for datasets comparing healthy controls and patients with Parkinson’s Disease. Our curators use ontology terms to annotate datasets when possible. This allows us to use the MONDO Disease Ontology term “MONDO:0005180” to search for datasets and samples annotated with the term to make sure we are accessing datasets relevant for our purposes with minimal effort.

1 Querying datasets of interest

The get_datasets function can be used to find datasets of interest, either using ontology terms or plain text. Since Parkinson’s Disease has a unambiguous ontology term in the Disease Ontology, we will be using it to avoid acquiring datasets that tangentially mention the disease in their descriptions. Here we also limit our results to only include human samples since we are not interested in models from other species and samples that are explicitly from the brain using the ontology term for brain. See documentation for the function for a detailed explanation of available options

# getting all resulting datasets using limit and offset arguments
results <- get_datasets(filter = "allCharacteristics.valueUri = and allCharacteristics.valueUri =",
                       taxa ='human') %>% get_all_pages()

results %>% select(experiment.shortName, %>% head
1:              GSE7621
2:              GSE7307
3:              GSE8397
4:             GSE20168
5:             GSE20333
6:             GSE20146
1: Expression data of substantia nigra from postmortem human brain of Parkinson's disease patients (PD)
2:                                                         Human body index - transcriptional profiling
3:                                                       Expression profiling of the Parkinsonian Brain
4:                                 Transcriptional analysis of prefrontal area 9 in Parkinson's disease
5:                                           Gene expression profiling of parkinsonian substantia nigra
6:                                          Expression analysis of dissected GPi in Parkinson's disease

2 Filtering the datasets for suitability

While we know that all the resulting datasets were annotated for the term for Parkinson’s Disease, we currently do not know how many of them are comparisons of healthy controls and patients with Parkinson’s Disease. We also do not know if the datasets have batch effects that may affect our findings.

For this example, we decided we should not include data sets that have a batch confound (though that is up to the user). Gemma internally handles batch correction if batch information is available for the dataset. We will be looking at experiment.batchEffect column. As explained in the get_datasets documentation, this column will be set to -1 for datasets where batch confound is detected, 0 for datasets without available batch information and to 1 if the data is clear of batch confounds.

results <- results %>% filter(experiment.batchEffect == 1)

We now want to ensure that the differential expressions we analyze compare control and Parkinson’s Disease patients. This information is available via get_dataset_differential_expression_analyses which returns the experimental groups for differential expression analyses performed for the dataset. The columns we are primarily interested in are baseline.factors which typically records the control group of the differential expression analysis and experimental.factors which typically records the test case

experiment_contrasts <- results$experiment.shortName %>% 
        out = get_dataset_differential_expression_analyses(x)
        }) %>%,.)

The contrasts we are interested in should have a factor.category of “disease” and it should have “” in it’s experimental.factors column as the URI for a factor.

The factor we are interested in is Parkinson’s Disease in experimental.factorValue or “” in experimental.factors’s value.URI. We also need to make sure that the baseline that the sample is compared against is a control experiment, samples annotated with “reference subject role” or “

parkin_contrasts <- experiment_contrasts %>% 
    filter(factor.category == 'disease') %>% 
        "" %in% x$value.URI
        }) &
               "" %in% x$value.URI

In the next steps we will be downloading expression data in bulk. If you are here to just try the code out, you can speed the process a bit by limiting the number of experiments to deal with

# arbitrarily select a few datasets
# not executed in this example
parkin_contrasts <- parkin_contrasts[1:5,]

Now that we have our relevant contrasts, we can download them using get_differential_expression_values. This function can be used to download differential expression fold change and p values, either using the experiment name/ids or more specifically using the result.IDs

differentials <- parkin_contrasts$result.ID %>% lapply(function(x){
    # take the first and only element of the output. the function returns a list 
    # because single experiments may have multiple resultSets. Here we use the 
    # resultSet argument to directly access the results we need
    get_differential_expression_values(resultSets = x)[[1]]

# some datasets might not have all the advertised differential expression results
# calculated due to a variety of factors. here we remove the empty differentials
missing_contrasts <- differentials %>% sapply(nrow) %>% {.==0}
differentials <- differentials[!missing_contrasts]
parkin_contrasts <- parkin_contrasts[!missing_contrasts,]

3 Getting the p-values for the condition comparison

differentials is now a list of data frames containing the differential expression information. To run a simple meta-analysis, we need the p values for the genes from the relevant contrasts.

condition_diffs <- seq_along(differentials) %>% lapply(function(i){
    # iterate over the differentials
    diff = differentials[[i]]
    # get the contrast information about the differential
    contrast = parkin_contrasts[i,]
    p_vals = diff[[paste0('contrast_',contrast$contrast.ID,"_pvalue")]]
    log2fc = diff[[paste0('contrast_',contrast$contrast.ID,"_log2fc")]]
    genes = diff$GeneSymbol

# we can use result.IDs and contrast.IDs to uniquely name this. 
# we add the for readability
names(condition_diffs) = paste0(parkin_contrasts$experiment.ID,'.',

condition_diffs[[1]] %>% head
      genes    p_vals  log2fc
1     ALMS1 9.500e-03  0.2934
2    PRSS36 3.924e-05 -0.5074
3    SLC5A3 4.354e-06  1.8432
4    AGPAT4 6.912e-01 -0.1074
5 NUDT16-DT 5.978e-01  0.0418
6       DR1 1.389e-01  0.2875

4 Combining the acquired p values

Now that we have acquired the values we need for a meta-analysis from Gemma, we can proceed with any methodology we deem suitable for our analysis.

In this example we will use a very simple approach, Fisher’s combined p-value test. This is implemented in the fisher function from poolr package. Fisher’s method has the advantage that it operates on the p-values, which are in the processed differential expression results in Gemma. It has some disadvantages like ignoring the direction of the expression change (Gemma’s p-values are two-tailed) and is very sensitive to a single ‘outlier’ p-value, but for this demonstration we’ll go with it.

The first step is to identify which genes are available in our results:

all_genes <- condition_diffs %>% lapply(function(x){
    x$genes %>% unique
}) %>% unlist %>% table

# we will remove any gene that doesn't appear in all of the results
# while this criteria is too strict, it does help this example to run 
# considerably faster
all_genes <- all_genes[all_genes==max(all_genes)]
all_genes <- names(all_genes)

# remove any probesets matching multiple genes. gemma separates these by using "|"
all_genes <- all_genes[!grepl("|",all_genes,fixed = TRUE)]

# remove the "". This comes from probesets not aligned to any genes
all_genes <- all_genes[all_genes != ""]
all_genes %>% head
[1] "A2M"   "AAAS"  "AACS"  "AAGAB" "AAK1"  "AAMP" 

Now we can run the test on every gene, followed by a multiple testing correction.

fisher_results <- all_genes %>% lapply(function(x){
    p_vals <- condition_diffs %>% sapply(function(y){
        # we will resolve multiple probesets by taking the minimum p value for
        # this example
        out = y[y$genes == x,]$p_vals
        if(length(out) == 0 ||all({
        } else{
    fold_changes <- condition_diffs %>% sapply(function(y){
        pv = y[y$genes == x,]$p_vals
        if(length(pv) == 0 ||all({
        } else{
            return(y[y$genes == x,]$log2fc[which.min(pv)])
    median_fc = fold_changes %>% na.omit() %>% median
    names(median_fc) = 'Median FC'
    combined = p_vals %>% na.omit() %>% fisher() %>% {.$p}
    names(combined) = 'Combined'
}) %>%,.)
fisher_results <-
rownames(fisher_results) = all_genes

fisher_results[,'Adjusted'] <- p.adjust(fisher_results[,'Combined'],
                                        method = 'fdr')

fisher_results %>%
    arrange(Adjusted) %>% 
    select(Combined,Adjusted,`Median FC`) %>% 

We end up with quite a few differentially expressed genes in the meta-analysis (Fisher’s method is very sensitive)

sum(fisher_results$Adjusted<0.05) # FDR<0.05
[1] 5316
nrow(fisher_results) # number of all genes
[1] 6235

Next we look at markers of dopaminergic cell types and how they rank compared to other genes. Parkinson’s Disease is a neurodegenerative disorder, leading to death of dopaminergic cells. We should expect them to show up in our results.

# markers are taken from
dopa_markers <-  c("ADCYAP1", "ATP2B2", "CACNA2D2", 
"CADPS2", "CALB2", "CD200", "CDK5R2", "CELF4", "CHGA", "CHGB", 
"CHRNA6", "CLSTN2", "CNTNAP2", "CPLX1", "CYB561", "DLK1", "DPP6", 
"ELAVL2", "ENO2", "GABRG2", "GRB10", "GRIA3", "KCNAB2", "KLHL1", 
"LIN7B", "MAPK8IP2", "NAPB", "NR4A2", "NRIP3", "HMP19", "NTNG1", 
"PCBP3", "PCSK1", "PRKCG", "RESP18", "RET", "RGS8", "RNF157", 
"SCG2", "SCN1A", "SLC12A5", "SLC4A10", "SLC6A17", "SLC6A3", "SMS", 
"SNCG", "SPINT2", "SPOCK1", "SYP", "SYT4", "TACR3", "TENM1", 
"TH", "USP29")

fisher_results %>% 
    arrange(Combined) %>% 
    rownames %>%
    {.%in% dopa_markers} %>%
    which %>% 
    hist(breaks=20, main = 'Rank distribution of dopaminergic markers')

In agreement with our hypothesis, we can see that the dopaminergic markers tend to have high ranks in our results.

5 Acquiring the expression data for a top gene

Now that we have our results, we can take a look at how the expression of one of our top genes in these experiments. To do this we will use the get_dataset_expression_for_genes function to get the expression data. Once we get the expression data for our genes of interest, we will use the get_dataset_samples function to identify which samples belongs to which experimental group.

Note: as of this writing (early 2023), the Gemma.R methods used in this section are not yet released in Bioconductor; install via devtools to try it out.

For starters, lets look at our top pick and the its p values in the individual datasets, on a log scale:

# the top gene from our results
gene <- fisher_results %>% arrange(Adjusted) %>% .[1,] %>% rownames

p_values <- fisher_results %>%
    arrange(Adjusted) %>% 
    .[1,] %>% 
    select(-Combined,-`Median FC`,-Adjusted) %>% 
    unlist %>%


# p values of the result in individual studies
p_values %>% log10() %>% 
    data.frame(`log p value` = .,dataset = 1:length(.),check.names = FALSE) %>% 
    ggplot(aes(y = `log p value`,x = dataset)) +
    geom_point() + 
    geom_hline(yintercept = log10(0.05),color = 'firebrick') + 
    geom_text(y = log10(0.05), x = 0, label = 'p < 0.05',vjust =-1,hjust = -0.1) + 
    theme_bw() + ggtitle(paste("P-values for top gene (", gene, ") in the data sets")) +
    theme(axis.text.x = element_blank())

This shows that the gene is nominally significant in some but not all of the data sets.

To examine this further, our next step is to acquire gene expression data from the relevant datasets using get_dataset_object. We will use both dataset ids and resultSet ids when using this function since some of the returned analysis are only performed on a subset of the data. Providing resultSet ids allows us to harmonize the differential expression results with expression data by returning the relevant subset.

# we need the NCBI id of the gene in question, lets get that from the original
# results
NCBIid <- differentials[[1]] %>% filter(GeneSymbol == gene) %>% .$NCBIid %>% unique

expression_frame <- get_dataset_object(datasets = parkin_contrasts$experiment.ID,
                                       resultSets = parkin_contrasts$result.ID,
                                       contrasts = parkin_contrasts$contrast.ID,
                                       genes = NCBIid,type = 'tidy',consolidate = 'pickvar')

# get the contrast names for significance markers 
signif_contrasts <- which(p_values < 0.05) %>% names

Finally, we’ll make a plot showing the expression of the gene of interest in all the data sets. This helps us see the extent to which there is evidence of consistent differential expression.

expression_frame <- expression_frame %>%
    filter(! %>% 
    # add a column to represent the contrast 
    dplyr::mutate(contrasts = paste0(experiment.ID,'.',
                                    contrast.ID)) %>%
    # simplify the labels
    dplyr::mutate(disease = ifelse(disease == 'reference subject role','Control','PD'))

# for adding human readable labels on the plot
labeller <- function(x){
    x %>% mutate(contrasts = contrasts %>% 
                     strsplit('.',fixed = TRUE) %>%
                     purrr::map_chr(1) %>%

# pass it all to ggplot
expression_frame %>% 
    ggplot(aes(x = disease,y = expression)) + 
    facet_wrap(~contrasts,scales = 'free',labeller = labeller) + 
    theme_bw() + 
    geom_boxplot(width = 0.5) + 
    geom_point() + ggtitle(paste("Expression of", gene, " per study")) + 
    geom_text(data = data.frame(contrasts = signif_contrasts,
                             expression_frame %>%
                             group_by(contrasts) %>%
                             summarise(expression = max(expression)) %>%
              x = 1.5, label = '*',size=5,vjust= 1)

6 Session info

R Under development (unstable) (2024-03-18 r86148)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/ 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB              LC_COLLATE=C              
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] stringr_1.5.1               poolr_1.1-1                
 [3] listviewer_4.0.0            viridis_0.6.5              
 [5] viridisLite_0.4.2           pheatmap_1.0.12            
 [7] SummarizedExperiment_1.33.3 Biobase_2.63.1             
 [9] GenomicRanges_1.55.4        GenomeInfoDb_1.39.13       
[11] IRanges_2.37.1              S4Vectors_0.41.6           
[13] BiocGenerics_0.49.1         MatrixGenerics_1.15.0      
[15] matrixStats_1.3.0           ggrepel_0.9.5              
[17] ggplot2_3.5.0               dplyr_1.1.4                
[19] data.table_1.15.4           gemma.R_2.99.2             
[21] BiocStyle_2.31.0           

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        farver_2.1.1            fastmap_1.1.1          
 [4] mathjaxr_1.6-0          digest_0.6.35           timechange_0.3.0       
 [7] lifecycle_1.0.4         magrittr_2.0.3          compiler_4.4.0         
[10] rlang_1.1.3             sass_0.4.9              tools_4.4.0            
[13] utf8_1.2.4              yaml_2.3.8              knitr_1.46             
[16] labeling_0.4.3          S4Arrays_1.3.7          htmlwidgets_1.6.4      
[19] bit_4.0.5               curl_5.2.1              DelayedArray_0.29.9    
[22] xml2_1.3.6              RColorBrewer_1.1-3      abind_1.4-5            
[25] withr_3.0.0             purrr_1.0.2             grid_4.4.0             
[28] fansi_1.0.6             colorspace_2.1-0        scales_1.3.0           
[31] tinytex_0.50            cli_3.6.2               rmarkdown_2.26         
[34] crayon_1.5.2            generics_0.1.3          rstudioapi_0.16.0      
[37] httr_1.4.7              cachem_1.0.8            zlibbioc_1.49.3        
[40] assertthat_0.2.1        BiocManager_1.30.22     XVector_0.43.1         
[43] vctrs_0.6.5             Matrix_1.7-0            jsonlite_1.8.8         
[46] bookdown_0.38           bit64_4.0.5             magick_2.8.3           
[49] systemfonts_1.0.6       tidyr_1.3.1             jquerylib_0.1.4        
[52] glue_1.7.0              lubridate_1.9.3         stringi_1.8.3          
[55] gtable_0.3.4            UCSC.utils_0.99.6       munsell_0.5.1          
[58] tibble_3.2.1            pillar_1.9.0            rappdirs_0.3.3         
[61] htmltools_0.5.8.1       GenomeInfoDbData_1.2.12 R6_2.5.1               
[64] evaluate_0.23           kableExtra_1.4.0        lattice_0.22-6         
[67] highr_0.10              memoise_2.0.1           bslib_0.7.0            
[70] Rcpp_1.0.12             svglite_2.1.3           gridExtra_2.3          
[73] SparseArray_1.3.5       xfun_0.43               pkgconfig_2.0.3