A guide to metadata for samples and differential expression analyses
11 August 2024
Package
gemma.R 3.0.14
Contents
library(gemma.R)
library(dplyr)
library(pheatmap)
library(purrr)1 Introduction
The data in Gemma are manually annotated by curators with terms, often using an ontology term on both dataset and sample level. In Gemma.R three primary functions allow access to these annotations for a given dataset.
get_dataset_annotations: This function returns annotations associated with a dataset. These try to serve as tags describing the dataset as a whole and they characteristics that samples within the datasets have while also including some additional terms.get_dataset_samples: This function returns samples and associated annotations related to their experimental groups for an experimentget_dataset_differential_expression_analyses: This function returns information about differential expression analyses automatically performed by Gemma for a given experiment. Each row of the output is a contrast where a specific property or an interaction of properties are described.
In the examples below we will be referring to GSE48962 experiment, where striatum and cerebral cortex samples from control mice and mice belonging to a Huntington model (R6/2) were taken from 8 week and 12 week old mice.
3 Factor values
Samples and differential expression contrasts in Gemma are annotated with factor values. These values contain statements that describe these samples and which samples belong to which experimental in a differential expression analysis respectively.
3.1 Sample factor values
In gemma.R these values are stored in nested data.tables and can be
found by accessing the relevant columns of the outputs. Annotations for samples
can be accessed using get_dataset_samples. sample.factorValues column contains
the relevant information
samples <- get_dataset_samples('GSE48962')
samples$sample.factorValues[[
which(samples$sample.name == "TSM490")
]] %>%
gemma_kable()| category | category.URI | value | value.URI | predicate | predicate.URI | object | object.URI | summary | ID | factor.ID | factor.category | factor.category.URI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| organism part | http://www.ebi…/EFO_0000635 | striatum | http://purl.obolibrary…/UBERON_0002435 | NA | NA | NA | NA | striatum | 120172 | 20540 | organism part | http://www.ebi…/EFO_0000635 |
| genotype | http://www.ebi…/EFO_0000513 | HTT [human] huntingtin | http://purl.org/../3064 | has_genotype | http://purl.obolibrary…/GENO_0000222 | CAG repeats | NA | HTT [human] huntingtin has_… | 120175 | 20541 | genotype | http://www.ebi…/EFO_0000513 |
| genotype | http://www.ebi…/EFO_0000513 | HTT [human] huntingtin | http://purl.org/../3064 | has_genotype | http://purl.obolibrary…/GENO_0000222 | Overexpression | http://gemma.msl…/TGEMO_00004 | HTT [human] huntingtin has_… | 120175 | 20541 | genotype | http://www.ebi…/EFO_0000513 |
| block | http://www.ebi…/EFO_0005067 | Device=HWUSI-EAS1563_0073_F… | NA | NA | NA | NA | NA | Device=HWUSI-EAS1563_0073_F… | 163476 | 32643 | block | http://www.ebi…/EFO_0005067 |
| timepoint | http://www.ebi…/EFO_0000724 | 12 weeks | NA | NA | NA | NA | NA | 12 weeks | 120179 | 20543 | timepoint | http://www.ebi…/EFO_0000724 |
The example above shows a single factor value object for one sample. The rows of this
data.table are statements that belong to a factor value. Below each column of this
nested table is described. If a given field is filled by an ontology term, the corresponding
URI column will contain the ontology URI for the field.
category/category.URI: Category of the individual statement, such as treatment, phenotype or strainvalue/value.URI: The subject of the statement.predicate/predicate.URI: When a subject alone is not enough to describe all details, a statement can contain a predicate and an object. The predicate describes the relationship between the subject of the statement and the object. In the example above, these are used to denote properties of the human HTT in the mouse modelsobject/object.URI: The object of a statement is a property further describing it’s value. In this example these describe the properties of the HTT gene in the mouse model, namely that it has CAG repeats and it is overexpressed. If the value was a drug this could be dosage or timepoint.summary: A plain text summary of the factorValue. Different statements will have the same summary if they are part of the same factor valueID: An integer identifier for the specific factor value. In the example above, the genotype of the mouse is defined as a single factor value made up of two statements stating the HTT gene has CAG repeats and that it is overexpressed. This factor value has the ID of 120175 which is shared by both rows containing the statements describing it. This ID will repeat for every other patient that has the same genotype or differential expression results using that factor as a part of their contrast. For instance we can see which samples that was subjected to this condition using this ID instead of trying to match the other columns describing the statements
id <- samples$sample.factorValues[[
which(samples$sample.name == "TSM490")
]] %>% filter(value == "HTT [human] huntingtin") %>% {.$ID} %>% unique
# count how many patients has this phenotype
samples$sample.factorValues %>% sapply(\(x){
id %in% x$ID
}) %>% sum## [1] 12factor.ID: An integer identifier for the factor. A factor holds specific factor values. For the example above whether or not the mouse is a wild type mouse or if it has a wild type genotype is stored under the id 20541
We can use this to fetch all distinct genotypes
id <- samples$sample.factorValues[[
which(samples$sample.name == "TSM490")
]] %>%
filter(value == "HTT [human] huntingtin") %>% {.$factor.ID} %>% unique
samples$sample.factorValues %>% lapply(\(x){
x %>% filter(factor.ID == id) %>% {.$summary}
}) %>% unlist %>% unique## [1] "wild type genotype"
## [2] "HTT [human] huntingtin has_genotype CAG repeats and has_genotype Overexpression"This shows us the dataset has control mice and Huntington Disease model mice.. This ID can be used to match the factor between samples and between samples
and differential expression experiments
- factor.category/factor.category.URI: The category of the whole factor. Usually
this is the same with the category of the statements making up the factor value.
However in cases like the example above, where the value describes a treatment while
the factor overall represents a phenotype, they can differ.
gemma.R includes a convenience function to create a simplified design matrix out of these factor values for a given experiment. This will unpack the nested data.frames and provide a more human readable output, giving each available factor it’s own column.
design <- make_design(samples)
design[,-1] %>% head %>% # first column is just a copy of the original factor values
gemma_kable()| block | organism part | genotype | timepoint | |
|---|---|---|---|---|
| ESW176 | Device=HWUSI-EAS1563_0071_F… | striatum | wild type genotype | 8 weeks |
| TCW9469 | Device=HWUSI-EAS1563_0053:L… | cerebral cortex | wild type genotype | 12 weeks |
| ECM175 | Device=HWUSI-EAS1563_0071_F… | cerebral cortex | HTT [human] huntingtin has_… | 8 weeks |
| ESW183 | Device=HWUSI-EAS1563_0072_F… | striatum | wild type genotype | 8 weeks |
| ECW178 | Device=HWUSI-EAS1563_0071_F… | cerebral cortex | wild type genotype | 8 weeks |
| TSW479 | Device=HWUSI-EAS1563_0073_F… | striatum | wild type genotype | 12 weeks |
Using this output, here we look at the sample sizes for different experimental groups.
design %>%
group_by(`organism part`,timepoint,genotype) %>%
summarize(n= n()) %>%
arrange(desc(n)) %>%
gemma_kable()## `summarise()` has grouped output by 'organism part', 'timepoint'. You can
## override using the `.groups` argument.| organism part | timepoint | genotype | n |
|---|---|---|---|
| cerebral cortex | 12 weeks | HTT [human] huntingtin has_… | 3 |
| cerebral cortex | 12 weeks | wild type genotype | 3 |
| cerebral cortex | 8 weeks | HTT [human] huntingtin has_… | 3 |
| cerebral cortex | 8 weeks | wild type genotype | 3 |
| striatum | 12 weeks | HTT [human] huntingtin has_… | 3 |
| striatum | 12 weeks | wild type genotype | 3 |
| striatum | 8 weeks | HTT [human] huntingtin has_… | 3 |
| striatum | 8 weeks | wild type genotype | 3 |
3.2 Differential expression analysis factor values
For most experiments it contains, Gemma performs automated differential expression analyses. The kinds of analyses that will be performed is informed by the factor values belonging to the samples.
# removing columns containing factor values and URIs for brevity
remove_columns <- c('baseline.factors','experimental.factors','subsetFactor','factor.category.URI')
dea <- get_dataset_differential_expression_analyses("GSE48962")
dea[,.SD,.SDcols = !remove_columns] %>%
gemma_kable()| result.ID | contrast.ID | experiment.ID | factor.category | factor.ID | isSubset | probes.analyzed | genes.analyzed |
|---|---|---|---|---|---|---|---|
| 492856 | 120175_120178 | 8972 | genotype,timepoint | 20541,20543 | TRUE | 18969 | 18972 |
| 492855 | 120175 | 8972 | genotype | 20541 | TRUE | 18968 | 18971 |
| 492854 | 120178 | 8972 | timepoint | 20543 | TRUE | 18968 | 18971 |
| 492853 | 120175_120178 | 8972 | genotype,timepoint | 20541,20543 | TRUE | 20621 | 20623 |
| 492852 | 120175 | 8972 | genotype | 20541 | TRUE | 20621 | 20623 |
| 492851 | 120178 | 8972 | timepoint | 20543 | TRUE | 20621 | 20623 |
The example above shows the differential expression analyses results. Each row of this data.table
represents a differential expression contrast connected to a fold change and a p value in the output of
get_differential_expression_values function.
If we look at the contrast.ID
we will see the factor value identifiers returned in the ID column of our
sample.factorValues. These represent which factor value is used as the
experimental factor. Note that some rows will have two IDs appended together. These
represent the interaction effects of multiple factors. For simplicity, we will start
from a contrast without an interaction.
contrast <- dea %>%
filter(
factor.category == "genotype" &
subsetFactor %>% map_chr('value') %>% {.=='cerebral cortex'} # we will talk about subsets in a moment
)# removing URIs for brevity
uri_columns = c('category.URI',
'object.URI',
'value.URI',
'predicate.URI',
'factor.category.URI')
contrast$baseline.factors[[1]][,.SD,.SDcols = !uri_columns] %>%
gemma_kable()| category | value | predicate | object | summary | ID | factor.ID | factor.category |
|---|---|---|---|---|---|---|---|
| genotype | wild type genotype | NA | NA | wild type genotype | 120174 | 20541 | genotype |
contrast$experimental.factors[[1]][,.SD,.SDcols = !uri_columns] %>%
gemma_kable()| category | value | predicate | object | summary | ID | factor.ID | factor.category |
|---|---|---|---|---|---|---|---|
| genotype | HTT [human] huntingtin | has_genotype | CAG repeats | HTT [human] huntingtin has_… | 120175 | 20541 | genotype |
| genotype | HTT [human] huntingtin | has_genotype | Overexpression | HTT [human] huntingtin has_… | 120175 | 20541 | genotype |
Here, we can see the baseline is the wild type mouse, being compared to the Huntington Disease models
If we examine a factor with interaction, both baseline and experimental factor value columns will contain two factor values.
contrast <- dea %>%
filter(
factor.category == "genotype,timepoint" &
subsetFactor %>% map_chr('value') %>% {.=='cerebral cortex'} # we're almost there!
)contrast$baseline.factors[[1]][,.SD,.SDcols = !uri_columns] %>%
gemma_kable()| category | value | predicate | object | summary | ID | factor.ID | factor.category |
|---|---|---|---|---|---|---|---|
| genotype | wild type genotype | NA | NA | wild type genotype | 120174 | 20541 | genotype |
| timepoint | 12 weeks | NA | NA | 12 weeks | 120179 | 20543 | timepoint |
contrast$experimental.factors[[1]][,.SD,.SDcols = !uri_columns] %>%
gemma_kable()| category | value | predicate | object | summary | ID | factor.ID | factor.category |
|---|---|---|---|---|---|---|---|
| genotype | HTT [human] huntingtin | has_genotype | CAG repeats | HTT [human] huntingtin has_… | 120175 | 20541 | genotype |
| genotype | HTT [human] huntingtin | has_genotype | Overexpression | HTT [human] huntingtin has_… | 120175 | 20541 | genotype |
| timepoint | 8 weeks | NA | NA | 8 weeks | 120178 | 20543 | timepoint |
A third place that can contain factorValues is
the subsetFactor. Certain differential expression analyses exclude certain samples
based on a given factor. In this example we can see that this analysis were only performed
on samples from the cerebral cortex.
contrast$subsetFactor[[1]][,.SD,.SDcols = !uri_columns] %>%
gemma_kable()| category | value | predicate | object | summary | ID | factor.ID | factor.category |
|---|---|---|---|---|---|---|---|
| organism part | cerebral cortex | NA | NA | cerebral cortex | 120173 | 20540 | NA |
The ids of the factor values included in baseline.factors and experimental.factors along
with subsetFactor can be used to determine which samples represent a given contrast.
For convenience, get_dataset_object function which is used to compile metadata
and expression data of an experiment in a single object, includes resultSets and contrasts
argument which will return the data already composed of samples representing a particular contrast.
obj <- get_dataset_object("GSE48962",resultSets = contrast$result.ID,contrasts = contrast$contrast.ID,type = 'list')
obj[[1]]$design[,-1] %>%
head %>% gemma_kable()| block | organism part | genotype | timepoint | |
|---|---|---|---|---|
| TCW9469 | Device=HWUSI-EAS1563_0053:L… | cerebral cortex | wild type genotype | 12 weeks |
| ECM175 | Device=HWUSI-EAS1563_0071_F… | cerebral cortex | HTT [human] huntingtin has_… | 8 weeks |
| ECW178 | Device=HWUSI-EAS1563_0071_F… | cerebral cortex | wild type genotype | 8 weeks |
| TCW9451 | Device=HWI-EAS413_0047:Lane=2 | cerebral cortex | wild type genotype | 12 weeks |
| TCW9457 | Device=HWUSI-EAS1563_0053:L… | cerebral cortex | wild type genotype | 12 weeks |
| TCM9450 | Device=HWI-EAS413_0047:Lane=1 | cerebral cortex | HTT [human] huntingtin has_… | 12 weeks |
We suggested that the contrast.ID of a contrast also corresponded to a column
in the differential expression results, acquired by get_differential_expression_values.
We can use what we have learned to take a look at the expression of genes at the top of the
phenotype, treatment interaction. Each result.ID returns its separate table when accessing differential expression values.
dif_vals <- get_differential_expression_values('GSE48962')
dif_vals[[as.character(contrast$result.ID)]] %>% head %>%
gemma_kable()| Probe | NCBIid | GeneSymbol | GeneName | pvalue | corrected_pvalue | rank | contrast_120175_120178_coefficient | contrast_120175_120178_log2fc | contrast_120175_120178_tstat | contrast_120175_120178_pvalue |
|---|---|---|---|---|---|---|---|---|---|---|
| 100502959 | 100502959 | AV051173 | expressed sequence AV051173 | 1.60e-06 | 0.0163 | 9.70e-05 | 3.7586 | 3.7586 | 12.4896 | 1.60e-06 |
| 67993 | 67993 | Nudt12 | nudix hydrolase 12 | 1.10e-06 | 0.0163 | 4.85e-05 | -1.1088 | -1.1088 | -13.0595 | 1.10e-06 |
| 108096 | 108096 | Slco1a5 | solute carrier organic anio… | 1.00e-04 | 0.2082 | 6.00e-04 | -3.6495 | -3.6495 | -6.7998 | 1.00e-04 |
| 101883 | 101883 | Igflr1 | IGF-like family receptor 1 | 1.00e-04 | 0.2082 | 6.00e-04 | 2.3595 | 2.3595 | 6.7859 | 1.00e-04 |
| 51795 | 51795 | Srpx | sushi-repeat-containing pro… | 1.00e-04 | 0.2082 | 5.00e-04 | -4.4992 | -4.4992 | -6.8665 | 1.00e-04 |
| 16969 | 16969 | Zbtb7a | zinc finger and BTB domain … | 7.81e-05 | 0.2082 | 2.00e-04 | 1.1581 | 1.1581 | 7.3738 | 7.81e-05 |
To get the top genes found associated with this interaction we access the columns with
the correct contrast.ID.
# getting the top 10 genes
top_genes <- dif_vals[[as.character(contrast$result.ID)]] %>%
arrange(across(paste0('contrast_',contrast$contrast.ID,'_pvalue'))) %>%
filter(GeneSymbol!='' | grepl("|",GeneSymbol,fixed = TRUE)) %>% # remove blank genes or probes with multiple genes
{.[1:10,]}
top_genes %>% select(Probe,NCBIid,GeneSymbol) %>%
gemma_kable()| Probe | NCBIid | GeneSymbol |
|---|---|---|
| 67993 | 67993 | Nudt12 |
| 100502959 | 100502959 | AV051173 |
| 12153 | 12153 | Bmp1 |
| 58212 | 58212 | Srrm3 |
| 16969 | 16969 | Zbtb7a |
| 19732 | 19732 | Rgl2 |
| 108096 | 108096 | Slco1a5 |
| 101883 | 101883 | Igflr1 |
| 51795 | 51795 | Srpx |
| 108168973 | 108168973 | Gm12828 |
We can then use the expression data returned by get_dataset_object to
examine the expression values for these genes.
exp_subset<- obj[[1]]$exp %>%
filter(Probe %in% top_genes$Probe)
genes <- top_genes$GeneSymbol
# ordering design file
design <- obj[[1]]$design %>% arrange(genotype,timepoint)
# shorten the resistance label a bit
design$genotype[grepl('HTT',design$genotype)] = "Huntington Model"
exp_subset[,.SD,.SDcols = rownames(design)] %>% t %>% scale %>% t %>%
pheatmap(cluster_rows = FALSE,cluster_cols = FALSE,labels_row = genes,
annotation_col =design %>% select(genotype,timepoint))
Session info
## R version 4.4.1 (2024-06-14)
## 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/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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