Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single-sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through The Cancer Genome Atlas we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within specific subsets of cancers, and as demonstrated, singscore has the ability to identify samples where alternative mutations/genetic lesions appear to drive transcriptional programs.
R version: R version 4.4.1 (2024-06-14)
Bioconductor version: 3.20
Package version: 1.22.0
The development of microarrays and more recently the rapid uptake of RNA-sequencing technologies have provided a platform to examine the transcriptional profile (or transcriptome) of biological samples (Cieślik and Chinnaiyan 2018). Transcriptomic analyses have traditionally focused on ‘differential expression’ of genes between sets of samples, however with the rapid growth of publicly available RNA data there has been increasing usage of ‘relative approaches’, which quantify the relative concordance of a sample or samples with a specific gene signature (Cieślik and Chinnaiyan 2018). While sequencing of genomic mutations has been important for classifying different tumour subsets based upon the presence of mutations or fusion genes, and identifying genetic lesions which may act as drivers of cancer progression, transcriptomic profiling can provide further information on the state or phenotype of cells carrying these mutations. Cancers are a heterogeneous set of diseases with a number of clinical and pathological subtypes. In diseases such as breast cancer the primary clinical classifications relate to the expression of hormone receptors (estrogen receptor: ER; and progesterone receptor: PR) or the overexpression of Erb-B2 receptor tyrosine kinase (HER2), as these features can be directly targeted with therapeutic agents. A common example of transcriptomic or gene expression data informing clinical practice is the use of prediction analysis of microarray 50 (PAM50) signatures for distinguishing the intrinsic breast cancer subtypes (Parker et al. 2009, @cieslik18). For many other cancers, subtype classification has largely relied upon identifying sets of recurrent mutations across large patient cohorts, with whole genome or whole exome sequencing studies helping to resolve the clinically significant subtypes (Cancer Genome Atlas Research Network 2013, @papaemmanuil16).
Perhaps the most well-known ‘relative approach’ is single-sample gene set enrichment analysis (ssGSEA) (Barbie et al. 2009), often used through the GenePattern web-tool. Another common approach is gene set variation analysis (GSVA) (Hänzelmann, Castelo, and Guinney 2013) which is available as an R/Bioconductor package that also includes functionality for ssGSEA, an alternative approach known as PLAGE (Tomfohr, Lu, and Kepler 2005), and a z-score based approach (Lee et al. 2008). Both ssGSEA and GSVA use a Kolmogorov-Smirnov like random-walk statistic to convert normalised gene ranks to the resulting score, however this normalisation procedure means that the scores are not truly ‘single-sample’, and variations in the overall sample composition for a study (e.g. variations in the presence or relative frequency of different cancer subtypes) can lead to unexpected changes in sample scores. Furthermore, the resultant scores from these methods can vary in their range and absolute value, making them difficult to interpret without further processing. To overcome this, we have developed a single-sample gene set scoring method singscore (Foroutan et al. 2018) which simply uses the ranks of genes within a given set, normalised relative to the maximum and minimum theoretical scores for a gene set of a given size.
Through large scale efforts such as The Cancer Genome Atlas (TCGA), transcriptomic data are available for thousands of clinical samples, often together with corresponding genomic or epigenomic (often DNA methylation) data. These transcriptomic data can help to characterise the functional effects of corresponding mutations, and provide a window to study the heterogeneity which arises within different subtypes of cancer due to epigenetic and transcriptional regulatory programs which can also influence cell behaviour. Here, we demonstrate that the single-sample gene set scoring method singscore (Foroutan et al. 2018) can be used to classify TCGA AML samples using transcriptional ‘gene signatures’ for the NPM1c mutation, KMT2A (MLL) gene fusions, and PML-RARA gene fusions that were derived from independent studies. Without any need for parameter fitting or estimation, we show that gene set scoring with singscore can distinguish samples carrying these mutations. The case studies we present demonstrate the application of gene set scoring to examine not only the differences, but also the relative similarities between established subtypes of AML that impact clinical outcome.
As with most cancers, acute myeloid leukemia (AML) is a heterogeneous disease with a number of classified subtypes. Analysis of TCGA AML genomic data identified a number of subtypes based upon the presence or absence of specific ‘driver mutations’; recapitulating and expanding upon previously identified clinical subsets (Cancer Genome Atlas Research Network 2013). A more recent study which focused primarily on genomic data has further refined the clinically significant AML subtypes (Papaemmanuil et al. 2016), highlighting a number of co-occurring as well as mutually exclusive mutations. As the identification of putative driver fusions/mutations continues, work has also been directed towards how these lesions interact with each other and other features (e.g. cellular proliferation, changes due to phenotypic plasticity, or variation in post-transcriptional regulators such as microRNAs) to drive transcriptional changes as discussed in a recent review (Handschuh 2019).
Of note for this work, one of the most common mutations in clinical AML samples is a frameshift mutation within exon 12 of the nucleophosmin (NPM1) gene (Papaemmanuil et al. 2016). This mutation leads to aberrant localisation of nucleophosmin with cytoplasmic accumulation rather than localising to the nucleolus, and accordingly this mutation is often referred to as the NPM1c mutation (Brunetti et al. 2018). As noted by Verhaak et al. (2005), the NPM1c mutation is associated with dysregulated activity of the homeobox domain (Hox) family of transcription factors which are essential for developmental patterning. The effects of this mutation in disease progression have been further demonstrated in recent work which showed that loss of NPM1c leads to differentiation of AML cells (Brunetti et al. 2018).
Further recurrent genetic lesions in AML relevant for this work include lysine methyl transferase 2A (KMT2A; previously known as MLL) fusion genes, partial tandem duplications within KMT2A (KMT2A-PTD), and fusion genes between promyelocytic leukemia and retinoic acid receptor alpha (PML-RARA). Given the role of NPM1c in dysregulating the Hox gene family, it is interesting to note that AML samples with MLL fusion genes also show dysregulated expression of Hox family genes (Hess 2004, @ross04); however, samples with MLL-PTD appear to show a relatively distinct phenotype from MLL-fusion samples (Ross et al. 2004). While there is good evidence demonstrating the role of NPM1c mutations and other genetic lesions in blocking AML cell differentiation, the PML-RARA fusion subset is diagnostic for a specific subset of AML known as acute promyelocytic leukemia (APL). This clinically distinct subtype of AML is associated with a specific morphology under the French-American-British (FAB) classification of AML, FAB-M3, with cells showing a distinct morphology due to a differentiation block at the promyelocyte stage (Thé et al. 1991).
In this workflow we demonstrate the ability of the singscore method for single sample gene set scoring (Foroutan et al. 2018) to classify tumour ‘driver mutations’ from transcriptomic data. We use a previously identified gene signature for the NPM1c mutation (Verhaak et al. 2005). We also use signatures for PML-RARA gene fusions and MLL-fusions that were derived using pediatric AML samples but shown to work well for classifying adult AML samples with similar lesions (Ross et al. 2004), although we note that there is evidence of relatively large differences in the mutational profiles of adult and pediatric cancers (Ma et al. 2018). Using these signatures, which are included within the molecular signatures database (MSigDB) (Liberzon et al. 2015), we demonstrate that a bi-directional scoring approach can classify TCGA AML samples with corresponding mutations with a good precision and recall. A particularly useful feature of gene set scoring is the ability to project samples onto 2D or higher-order landscapes defined by corresponding phenotypic signatures. Accordingly, by comparing scores for both the NPM1c and KMT2A-/MLL-fusion signatures, we show that this classification likely arises through the shared downstream biological effects of Hox family dysregulation. We also compare the NPM1c mutation signature to the PML-RARA signature and show a clear separation of these subtypes reflecting their divergent phenotypes and the mutually exclusive nature of these mutations.
While we demonstrate that singscore is capable of inferring mutation status from the transcriptional profile of AML samples, we note that it is best used to supplement alternative data which can provide a more definitive resolution of these lesions. Processing of raw RNA seq data will directly identify the presence of gene fusion products or mutations within protein-coding regions, although for many large data sets the quantified transcript abundance data are much easier to obtain without access agreements. The method can also be applied to legacy microarray data sets where genome and RNA sequencing data are unavailable. As such singscore provides a useful approach to supplement established methods for the study of genetic lesions in cancer. By exploring associations between different genomic and phenotypically relevant signatures, it may also help to better characterise true driver mutations which exert consistent effects on the transcriptome of AML samples and other cancers.
Data from TCGA project is made available through the Genomic Data Commons (GDC). Open access data from the project can be accessed in multiple pre-processed formats. Transcriptomic data can be downloaded either at the count level or as FPKM transformed abundance, before or after upper quantile normalisation. Other pre-processed version can be found from sources such as the cBioPortal and FireBrowse. The GDC data used STAR to perform a two-pass alignment followed by quantification. Data from the GDC can be downloaded using the GDC data transfer tool which allows users to select the specific files of interest using the GDC portal. These files then have to be read, merged, annotated and processed into a data structure that simplifies downstream analysis. Alternatively, all the above mentioned steps, including the download, can be performed using the R package TCGAbiolinks
(Colaprico et al. 2015, @R-TCGAbiolinks). The package supports data download using the GDC API and the GDC client. We will use the TCGAbiolinks package to download, annotate and process the data into a SummarizedExperiment R object.
The following steps need to be performed to prepare the data:
The first step in any analysis should be to determine and report the data version and the service used to download the data. The getGDCInfo()
function returns the release date of all data on the GDC along with a version.
library(SingscoreAMLMutations)
library(TCGAbiolinks)
#get GDC version information
gdc_info = getGDCInfo()
gdc_info
## $commit
## [1] "0e2e9efeeb76ba1554f1ac03dbf112bcb584215b"
##
## $data_release
## [1] "Data Release 41.0 - August 28, 2024"
##
## $data_release_version
## $data_release_version$major
## [1] 41
##
## $data_release_version$minor
## [1] 0
##
## $data_release_version$release_date
## [1] "2024-08-28"
##
##
## $status
## [1] "OK"
##
## $tag
## [1] "7.6.1"
##
## $version
## [1] 1
A query then needs to be run, using the GDC to identify the specific files for download. This step is similar to generating a MANIFEST file using the GDC portal. The first parameter of the query specifies the project - available projects can be accessed using getGDCprojects()
or from https://portal.gdc.cancer.gov/projects. The TCGA acute myeloid leukemia data is part of the TCGA-LAML project. Following this, the data category, data type and workflow type need to be specified. The query formed below selects files containing the count level transcriptomic measurements. Values for different parameters of the query can be identified from “searching arguments” section of the “query” vignette: vignette("query", package = "TCGAbiolinks")
. The result of this query will be a dataframe containing filenames and additional annotations related to the files.
Read count level data are selected instead of the processed FPKM data as one of the downstream pre-processing analysis results in filtering out of genes. A general recommendation is to compute FPKM values after filtering genes out so as to ensure counts are normalised by the corresponding library sizes. In cases where count-level data is not available, filtering can be performed directly on FPKM values, provided that the library size is large enough.
#form a query for the RNAseq data
query_rna = GDCquery(
#getGDCprojects()
project = 'TCGA-LAML',
#TCGAbiolinks:::getProjectSummary('TCGA-LAML')
data.category = 'Transcriptome Profiling',
data.type = 'Gene Expression Quantification',
workflow.type = 'STAR - Counts'
)
#extract results of the query
rnaseq_res = getResults(query_rna)
dim(rnaseq_res)
## [1] 151 30
colnames(rnaseq_res)
## [1] "id" "data_format"
## [3] "cases" "access"
## [5] "file_name" "submitter_id"
## [7] "data_category" "type"
## [9] "platform" "file_size"
## [11] "created_datetime" "md5sum"
## [13] "updated_datetime" "file_id"
## [15] "data_type" "state"
## [17] "experimental_strategy" "version"
## [19] "data_release" "project"
## [21] "analysis_id" "analysis_state"
## [23] "analysis_submitter_id" "analysis_workflow_link"
## [25] "analysis_workflow_type" "analysis_workflow_version"
## [27] "sample_type" "is_ffpe"
## [29] "cases.submitter_id" "sample.submitter_id"
The GDCdownload
function then executes the query on the GDC database and begins downloading the data using the GDC API. The download method should be changed to “client”, if the size of the data is expected to be large, e.g for RNA-seq read data or methylation data. It is good practice to specify the directory for data storage - we store all the data in the “GDCdata” directory in the temporary directory. Users should store their data in a permanent storage to retain the data. The function downloads the data and organises it into the folder based on parameters specified in the query. This allows multiple different levels and types of data to be stored in the same directory structure. Files with counts are stored at TEMPDIR/GDCdata/TCGA-LAML/harmonized/Transcriptome_Profiling/Gene_Expression_Quantification/.
datapath = file.path(tempdir(), 'GDCdata')
GDCdownload(query_rna, directory = datapath) #(size: 170MB)
The GDCprepare
function reads and processes the downloaded data into a RangedSummarizedExperiment
object from the SummarizedExperiment
package (Morgan et al. 2024) which allows patient annotations, gene annotations and count data to be stored in one object. Patient annotations are downloaded upon calling this function and subsequently mapped and attached to the resulting object. A RangedSummarizedExperiment object is similar to an ExpressionSet object but provides added functionality such as indexing with genomic coordinates and storing multiple data matrices with the same structure. Feature annotations used to annotate the data are stored in an RDA/RDATA file.
aml_se = GDCprepare(query_rna, directory = datapath)
The object contains data for 60,660 features and 150 samples. Feature and sample annotations can be accessed using rowData(se)
and colData(se)
, respectively, and the counts data can be accessed using assay(se)
. TCGA data usually contains some formalin-fixed paraffin-embedded (FFPE) samples which should be discarded from the analysis as the protocol introduces biological artefacts. This procedure is only performed on solid tumours and not leukemias, therefore, no filtering is required for this data set.
aml_se
## class: RangedSummarizedExperiment
## dim: 60660 151
## metadata(1): data_release
## assays(6): unstranded stranded_first ... fpkm_unstrand fpkm_uq_unstrand
## rownames(60660): ENSG00000000003.15 ENSG00000000005.6 ...
## ENSG00000288674.1 ENSG00000288675.1
## rowData names(10): source type ... hgnc_id havana_gene
## colnames(151): TCGA-AB-2908-03A-01T-0740-13
## TCGA-AB-2956-03A-01T-0740-13 ... TCGA-AB-2847-03A-01T-0736-13
## TCGA-AB-2911-03A-01T-0734-13
## colData names(53): barcode patient ... released sample.aux
The edgeR
package (Chen et al. 2024) contains methods that assist in the data normalisation and transformation required for filtering and subsequent steps. The methods require a DGEList object therefore we begin by creating a DGEList for the AML data from the SummarizedExperiment.
library(SummarizedExperiment)
library(edgeR)
#remove ENSEMBL ID version numbers
rownames(aml_se) <- gsub('\\.[0-9]*', '', rownames(aml_se))
aml_dge = DGEList(counts = assay(aml_se), genes = rowData(aml_se))
Genes with low counts across most samples are discarded from the analysis. This is a standard step in differential expression analysis as inclusion of such genes in the analysis could skew estimates of dispersion. It is also motivated in rank-based analysis, such as with singscore, to avoid rank duplication. Rank duplication reduces the discriminant power of scores as the number of unique ranks is reduced. A commonly used filter is to select only those genes that have CPMs above a certain threshold across a proportion of samples. Filtering is performed on the CPMs rather than raw counts as the former accounts for variation in library sizes, therefore, is unbiased. For instance, a CPM of 1 would equate to read counts between 19 and 50 for samples in the AML data where library sizes vary between 18.6 and 49.7 million reads. Here, we retain genes that have a CPM > 1 across more than 50% of the samples. Other methods to filter out genes with low counts exist and may be preferable in specific applications. Chen, Lun, and Smyth (2016) and Law et al. (2016) filter genes based on the experimental design whereby the proportion of samples with enough read counts are evaluated per experimental group. As the AML data have many samples, filtering is performed across all samples rather than within sub-groups. Group specific filtering would be recommended for the study of rare groups. The distribution of logCPMs is much closer to the expected log-normal distribution after filtering out genes with low counts as seen in Figure 1.
prop_expressed = rowMeans(cpm(aml_dge) > 1)
keep = prop_expressed > 0.5
op = par(no.readonly = TRUE)
par(mfrow = c(1, 2))
hist(cpm(aml_dge, log = TRUE), main = 'Unfiltered', xlab = 'logCPM')
abline(v = log(1), lty = 2, col = 2)
hist(cpm(aml_dge[keep, ], log = TRUE), main = 'Filtered', xlab = 'logCPM')
abline(v = log(1), lty = 2, col = 2)
par(op)
#subset the data
aml_dge = aml_dge[keep, , keep.lib.sizes = FALSE]
aml_se = aml_se[keep, ]
Singscore requires gene expression measurements to be comparable between genes within a sample, therefore, correction for gene length bias needs to be performed (Oshlack and Wakefield 2009). Transformations such as transcripts per million (TPM) and reads/fragments per kilobase per million (RPKM/FPKM), that normalise by gene length, may be used. Both- TPM and RPKM/FPKM values should produce similar results when applying singscore provided that the library size is large enough, which they are here. RPKM values are generally computed after correcting for compositional biases. The calcNormFactors
function in edgeR provides three methods to do so, TMM normalisation being the default. Chen, Lun, and Smyth (2016) and Law et al. (2016) discuss the implications of normalisation prior to down-stream processing such as differential expression analysis. Normlisation is generally performed for cross-sample analysis where samples need to be comparable. Singscores are invariant to data normalisation as the analysis is contained within the sample of interest. The idea extends to any transformation that preserves the relative ranks of genes within a sample such as a log transformation. Here, we use TMM normalisation solely for visualisation purposes.
Data transformation to TPM or RPKM/FPKM requires the lengths for all genes to be calculated. Gene lengths need to be computed based on the alignment and quantification parameters. The TCGA transcriptomic data has been aligned using STAR and quantified (details of the pipeline available at https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/). STAR quantifies reads mapping to the exons of each gene, therefore, effective gene lengths can be calculated as the sum of all exons spanning the gene. The GENCODE v36 annotation file was used for quantification therefore the same file needs to be used to compute gene lengths.
#download v36 of the GENCODE annotation
library(BiocFileCache)
gencode_file = 'gencode.v36.annotation.gtf.gz'
gencode_link = paste(
'ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_36',
gencode_file,
sep = '/'
)
bfc <- BiocFileCache()
gencode_path <- bfcrpath(bfc, gencode_link)
The rtracklayer
R package (Lawrence, Carey, and Gentleman 2024) provides functions to help parse GTF files.
library(rtracklayer)
library(plyr)
gtf = import.gff(gencode_path, format = 'gtf', genome = 'GRCm38.71', feature.type = 'exon')
#split records by gene to group exons of the same gene
grl = reduce(split(gtf, elementMetadata(gtf)$gene_id))
gene_lengths = ldply(grl, function(x) {
#sum up the length of individual exons
return(c('gene_length' = sum(width(x))))
}, .id = 'ensembl_gene_id')
Genes are also annotated with their biotype for further analysis. The annotation file uses Ensembl IDs with versions as keys to records, which then need to be converted to Ensembl IDs. This is simply achieved by truncating the trailing version number.
#extract information on gene biotype
genetype = unique(elementMetadata(gtf)[, c('gene_id', 'gene_type')])
colnames(genetype)[1] = 'ensembl_gene_id'
gene_lengths = merge(genetype, gene_lengths)
#remove ENSEMBL ID version numbers
gene_lengths$ensembl_gene_id = gsub('\\.[0-9]*', '', gene_lengths$ensembl_gene_id)
saveRDS(gene_lengths, file = 'gene_lengths_gencode_v36.rds')
gene_lengths
## DataFrame with 60660 rows and 3 columns
## ensembl_gene_id gene_type gene_length
## <character> <character> <integer>
## 1 ENSG00000000003 protein_coding 4536
## 2 ENSG00000000005 protein_coding 1476
## 3 ENSG00000000419 protein_coding 1207
## 4 ENSG00000000457 protein_coding 6883
## 5 ENSG00000000460 protein_coding 5970
## ... ... ... ...
## 60656 ENSG00000288669 protein_coding 3987
## 60657 ENSG00000288670 lncRNA 1807
## 60658 ENSG00000288671 protein_coding 213
## 60659 ENSG00000288674 protein_coding 9620
## 60660 ENSG00000288675 protein_coding 1680
The SummarizedExperiment object allows feature annotations to be stored, therefore, information on gene length and biotypes should be added to the existing annotations. Similarly, annotations need to be added to the DGEList object. The column containing lengths should include “length” in its name.
#allocate rownames for ease of indexing
rownames(gene_lengths) = gene_lengths$ensembl_gene_id
rowData(aml_se)$gene_length = gene_lengths[rownames(aml_se), 'gene_length']
rowData(aml_se)$gene_biotype = gene_lengths[rownames(aml_se), 'gene_type']
#annotate gene lengths for the DGE object
aml_dge$genes$length = gene_lengths[rownames(aml_dge), 'gene_length']
RPKM/FPKM values can now be calculated with the computed gene lengths after computing the normalisation factors. The SummarizedExperiment object can store multiple levels of the data simultaneously, provided that the number of features and samples remains the same across measurements. As such, FPKM values are appended to the existing object.
aml_dge_tmm = calcNormFactors(aml_dge, method = 'TMM')
#compute FPKM values and append to assays
assay(aml_se, 'logFPKM_TMM') = rpkm(aml_dge_tmm, log = TRUE)
aml_se
## class: RangedSummarizedExperiment
## dim: 17146 151
## metadata(1): data_release
## assays(7): unstranded stranded_first ... fpkm_uq_unstrand logFPKM_TMM
## rownames(17146): ENSG00000000419 ENSG00000000457 ... ENSG00000288663
## ENSG00000288670
## rowData names(12): source type ... gene_length gene_biotype
## colnames(151): TCGA-AB-2908-03A-01T-0740-13
## TCGA-AB-2956-03A-01T-0740-13 ... TCGA-AB-2847-03A-01T-0736-13
## TCGA-AB-2911-03A-01T-0734-13
## colData names(53): barcode patient ... released sample.aux
For this analysis we have used the curated mutation list from the original TCGA AML publication (Cancer Genome Atlas Research Network 2013) (Supplemental Table 01 at https://gdc.cancer.gov/node/876) rather than variant calls from the standard TCGA pipeline (available through the National Cancer Institute Genomic Data Commons) and readers should note that there are some discrepancies between these. For genetic lesions of interest (NPM1c, KMT2A-MLL, KMT2A-PTD and PML-RARA), patients were identified by the following criteria:
data(AMLPatientMutationsTCGA)
patient_mutations = AMLPatientMutationsTCGA
patient_mutations = patient_mutations[colnames(aml_se), ] # order samples
aml_mutations = colnames(patient_mutations) # get mutation labels
colData(aml_se) = cbind(colData(aml_se), patient_mutations)
colData(aml_se)[, aml_mutations]
## DataFrame with 151 rows and 4 columns
## NPM1c.Mut KMT2A.Fusion KMT2A.PTD PML.RARA
## <logical> <logical> <logical> <logical>
## TCGA-AB-2908-03A-01T-0740-13 FALSE FALSE FALSE FALSE
## TCGA-AB-2956-03A-01T-0740-13 FALSE TRUE FALSE FALSE
## TCGA-AB-2937-03A-01T-0734-13 FALSE FALSE FALSE FALSE
## TCGA-AB-2896-03B-01T-0751-13 TRUE FALSE FALSE FALSE
## TCGA-AB-2823-03A-01T-0736-13 FALSE FALSE FALSE TRUE
## ... ... ... ... ...
## TCGA-AB-2885-03A-01T-0735-13 FALSE FALSE FALSE FALSE
## TCGA-AB-2811-03B-01T-0760-13 TRUE FALSE FALSE FALSE
## TCGA-AB-2873-03A-01T-0735-13 FALSE FALSE FALSE FALSE
## TCGA-AB-2847-03A-01T-0736-13 FALSE FALSE FALSE FALSE
## TCGA-AB-2911-03A-01T-0734-13 FALSE TRUE FALSE FALSE
Ensembl annotations (Ensembl IDs) have higher coverage of the genome which may be useful in applications such as variant calling and similar exploratory analysis (Zhao and Zhang 2015). However, RefSeq annotations (Entrez IDs) may be better suited to RNA-seq analyses which require a stable reference annotation for comparison (Wu, Phan, and Wang 2013). As such, we choose to map Ensembl IDs to Entrez IDs and discard any unmapped features.
Mapping can be performed using the Ensembl Biomart service, which can be queried using the biomaRt bioconductor package. This would provide the most up to date annotations. Alternatively, mapping could be performed with the bi-annually updated org.Hs.eg.db
annotation R package (Carlson 2024) which provides a stable set of annotations, thereby enhancing reproducibility. Mapping is performed with the mapIds
function in the AnnotationDbi
R package (Pagès et al. 2024).
library(org.Hs.eg.db)
rowData(aml_se)$entrezgene = mapIds(
org.Hs.eg.db,
keys = rownames(aml_se),
keytype = 'ENSEMBL',
column = 'ENTREZID',
multiVals = 'asNA'
)
gene_annot = rowData(aml_se)
Multimapped Ensembl IDs are replaced by NAs
, then discarded to enforce unique mapping. Similarly, Entrez IDs that map to multiple Ensembl IDs are identified from the mapping, and discarded. Only features with unique Ensembl ID to Entrez ID mappings remain.
#select genes with mapped Entrez IDs
keep = !is.na(gene_annot$entrezgene)
#select genes with unique Entrez IDs
dup_entrez = gene_annot$entrezgene[duplicated(gene_annot$entrezgene)]
keep = keep & !gene_annot$entrezgene %in% dup_entrez
#Biotype of discarded genes (due to non-unique mapping)
head(sort(table(gene_annot[!keep, 'gene_biotype']), decreasing = TRUE), n = 10)
##
## lncRNA processed_pseudogene
## 1725 511
## TEC transcribed_unprocessed_pseudogene
## 231 212
## protein_coding transcribed_processed_pseudogene
## 144 78
## unprocessed_pseudogene transcribed_unitary_pseudogene
## 57 30
## misc_RNA Mt_tRNA
## 23 6
#subset the data
aml_se = aml_se[keep, ]
The signature by Verhaak et al. (2005) is now used to predict the mutation status of the NPM1c mutation. This is done by quantifying the concordance of genes in the signature with their expression in each sample. As such, high expression of up-regulated genes and low expression of down-regulated genes would result in higher scores. This single value can then be used to predict the mutation status of individual samples if these data were unavailable.
The signatures of interest are first downloaded from the MSigDB and read into GeneSet
objects from the GSEABase
R package (Morgan, Falcon, and Gentleman 2024). We then use the singscore
R/Bioconductor package to quantify each sample for the Verhaak signature. Some of the visualisation and diagnostic tools within the singscore
package are used to interpret the signatures and scores. Finally, we use a simple logistic regression model on the scores to predict the mutation status.
The Verhaak et al. (2005) signature is composed of an up-regulated and a down-regulated gene set. Many signatures are developed in such a manner to improve discrimination of samples. MSigDB stores such signatures using names with suffixes "_UP" and "_DN" representing the independent components of the signature. Here, we form the download links for the signature with the base name “VERHAAK_AML_WITH_NPM1_MUTATED”.
#create signature names
verhaak_names = paste('VERHAAK_AML_WITH_NPM1_MUTATED', c('UP', 'DN'), sep = '_')
verhaak_names
## [1] "VERHAAK_AML_WITH_NPM1_MUTATED_UP" "VERHAAK_AML_WITH_NPM1_MUTATED_DN"
The signatures are then downloaded using the links, resulting in an XML file for each component of the signature. The mapply
function is used to run the download function on all pairs of link-output arguments.
#generate URLs
verhaak_links = paste0(
'http://software.broadinstitute.org/gsea/msigdb/download_geneset.jsp?geneSetName=',
verhaak_names,
'&fileType=xml'
)
#download files
verhaak_files = paste0(verhaak_names, '.xml')
verhaak_path <- bfcrpath(bfc, verhaak_links)
Functions in the GSEABase
package help with reading, parsing and processing the signatures. Signatures from an MSigDB XML file can be read using the getBroadSets
function which results in a GeneSet
object. Gene symbols, Entrez IDs and affymetrix chip IDs from the original experiment (HG-U133A in this case) are stored in the XML file. Entrez IDs are read from the file as these can be mapped directly to our data. Conversions to other identifiers can be achieved using the mapIdentifiers
function from GSEABase
and an annotation package that contains the mappings. The advantage of using this function instead of the mapIds
function from the AnnotationDbi
package is that the former retains the GeneSet
object after conversion of IDs.
library(GSEABase)
verhaak_sigs = getBroadSets(verhaak_path, membersId = 'MEMBERS_EZID')
verhaak_sigs
## GeneSetCollection
## names: VERHAAK_AML_WITH_NPM1_MUTATED_UP, VERHAAK_AML_WITH_NPM1_MUTATED_DN (2 total)
## unique identifiers: 10051, 10135, ..., 9828 (437 total)
## types in collection:
## geneIdType: SymbolIdentifier (1 total)
## collectionType: BroadCollection (1 total)
To make data indexing easier during signature scoring, row names of the SummarisedExperiment
object are changed to Entrez IDs which are already part of the row annotations.
rownames(aml_se) = rowData(aml_se)$entrezgene
Singscore is a rank based metric of gene set enrichment in single samples. Scores for multiple signatures make use of the same ranked expression per sample. As such, it makes sense to compute the ranks only once and re-use them for scoring different signatures. The implementation separates these two phases of the analysis to reduce the computational cost of scoring. The rankGenes
function will compute ranks from expression data in the form of either a numeric matrix, numeric data frame, ExpressionSet object, DGEList object or a SummarizedExperiment object. Users also have to specify what method should be used to break ties. The default is ‘min’ and we recommend this be used for RNA-seq data which may have many genes with zero counts. This will reduce the effect of zeros in the scores, however, appropriate pre-filtering of genes with low counts will still be required.
library(singscore)
#apply the rankGenes method to each version of the dataset, excluding counts
aml_ranked = rankGenes(assay(aml_se, 'logFPKM_TMM'))
Singscores can be computed using three modes, depending on the properties of the gene signature. The first mode of operation is applied when two directed gene sets (expected up- and down-regulated gene sets) form the transcriptomic signature. Many signatures in the MSigDB, including the Verhaak et al. (2005) signature come in such pairs. This mode can be invoked by passing the up- and down-regulated gene sets to the arguments upSet
and downSet
respectively. In some cases, only one set of genes forms the signature. If all genes in the gene set are up-regulated or all down-regulated, the second mode of operation applies and is invoked by passing the gene set to the upSet
argument. For sets of down-regulated genes, the score would simply be inverted (-score if scores are centered, 1 - score otherwise). Finally, if the user is unsure of the composition of genes in the gene-set, such that, the gene set may contain both up- and down- regulated genes, the final mode of singscore applies. The gene set is passed to the upSet
argument similar to the previous mode with the additional argument knownDirection
set to FALSE
.
By default, singscores are centered such that the range of scores is \([-1, 1]\) and \([-0.5, 0.5]\) for the first two modes respectively. Negative scores indicate an inverse enrichment of signatures, that is, expected up-regulated genes are in fact down-regulated and vice-versa. Scores from the last mode can not be centered and have the range \([0, 1]\). In this mode, high scores are obtained when ranks of genes are distant from the median and low scores obtained when ranks converge to the median rank. If scores are centered in this scenario, it would lead to the conclusion that a negative score shows inverted enrichment, which is not the case. Score centering only serves the purpose of easing interpretation for users, a simple linear transformation is applied to achieve it.
Scores for the NPM1c mutation signature are computed using the default settings, with the first mode of operation being used due to the presence of an up- and down- regulated gene set. The function returns a data frame reporting the score and dispersion of ranks for the up-regulated gene set, down-regulated gene set and the combination of both. Dispersion of the combined gene set in this mode is simply the mean of the independent dispersion estimates. If any gene names/IDs are present in the signature but missing in the expression data, a warning will be reported.
#apply the scoring function
verhaak_scores = simpleScore(aml_ranked,
upSet = verhaak_sigs[[1]],
downSet = verhaak_sigs[[2]])
## Warning in checkGenes(upSet, rownames(rankData)): 29 genes missing: 10265, 108,
## 10924, 11025, 11026, 1672, 200315, 2215, 3201, 3215, 3216, 3569, 3627, 3759,
## 50486, 6346, 6348, 6364, 643332, 6648, 6966, 717, 8337, 8351, 861, 8843, 9518,
## 9627, 9997
## Warning in checkGenes(downSet, rownames(rankData)): 28 genes missing: 10232,
## 10267, 2122, 221981, 2258, 23532, 24141, 25907, 2697, 28526, 3047, 3386, 3848,
## 3934, 4070, 445, 4680, 4681, 5457, 5790, 6091, 653067, 653145, 7102, 7441,
## 8277, 862, 8788
It should be noted that singscores are composed of two components, an enrichment score and a dispersion estimate of ranks. The quantity of interest in gene set enrichment is the distribution of the expression or ranks of genes in the signature. In an ideal scenario, all expected up-regulated genes would have high expression therefore higher values of ranks. As such, ranks would be distributed on the higher end of the entire rank spectrum. Singscore aims to quantify this distribution of ranks, therefore, computes and reports the average and dispersion of ranks of genes in the signature relative to all other genes. The first quantity is similar to scores computed from all other single sample scoring methods. We determined a two component score to be a more appropriate and accurate representation of the distribution of ranks of signature genes. The default and recommended measure of dispersion is the median absolute deviation (MAD) due to its non-parametric property. Other appropriate measure of dispersion could be the inter-quartile range (IQR) and can be used by passing the IQR
function as an argument to the dispersionFun
argument.
head(verhaak_scores)
## TotalScore TotalDispersion UpScore
## TCGA-AB-2908-03A-01T-0740-13 -0.08922270 5118.676 0.01563106
## TCGA-AB-2956-03A-01T-0740-13 0.21991367 4738.390 0.10515441
## TCGA-AB-2937-03A-01T-0734-13 -0.23172026 5059.372 -0.15202398
## TCGA-AB-2896-03B-01T-0751-13 0.17887794 5458.933 0.08599781
## TCGA-AB-2823-03A-01T-0736-13 -0.04473132 6294.749 -0.06376717
## TCGA-AB-2858-03A-01T-0736-13 -0.23346497 5314.750 -0.17338015
## UpDispersion DownScore DownDispersion
## TCGA-AB-2908-03A-01T-0740-13 4856.256 -0.10485376 5381.097
## TCGA-AB-2956-03A-01T-0740-13 4581.234 0.11475925 4895.545
## TCGA-AB-2937-03A-01T-0734-13 4094.200 -0.07969628 6024.545
## TCGA-AB-2896-03B-01T-0751-13 5406.301 0.09288014 5511.565
## TCGA-AB-2823-03A-01T-0736-13 6475.997 0.01903585 6113.501
## TCGA-AB-2858-03A-01T-0736-13 4395.168 -0.06008483 6234.333
The singscore
package provides a set of visualisation tools that enable diagnostics of the gene signature. For instance, these tools may be used to determine the importance of each component for a bidirectional signature (up- and down-regulated gene sets) to the total score, determine the importance of each gene of a signature in discriminating between the classes of interest, and to investigate the relationship between the final score and the dispersion of signature gene ranks. Sample annotations of interest (e.g. clinical annotations) can be colour coded on each plot. Singscore supports both continuous and categorical annotations, which can either be input as a vector, or as a string specifying a column within the score data frame. We begin by investigating the relationship between the score and dispersion of ranks for the up-regulated gene signature, down-regulated gene signature and the full signature. The plotDispersion
functions generates a diagnostic plot with annotations overlaid. Annotations can be discrete or continuous, and can be passed as independent variables, or as a column name when the data is appended to the score data frame. It should be noted that all plotting functions in singscore
can be made interactive by setting the isInteractive
argument to TRUE
.
#relative size of text in the figure
relSize = 1.2
#create annotation
mutated_gene = rep('Other', ncol(aml_se))
mutated_gene[aml_se$NPM1c.Mut] = 'NPM1c Mut'
mutated_gene[aml_se$KMT2A.Fusion | aml_se$KMT2A.PTD] = 'MLL Fusion/PTD'
p1 = plotDispersion(verhaak_scores, annot = mutated_gene, textSize = relSize)
p1