Public repositories of biological omics data contain thousands of experiments. While these resources are extrenely useful, those data are difficult to mine. The annotation of the associated metadata with controlled vocabularies or ontology terms can facilitate the retrieval of the datasets of interest (Galeota and Pelizzola 2016). OnASSiS (Ontology Annotations and Semantic Similarity software) is a package aimed at matching metadata associated with biological experiments with concepts from ontologies, allowing the construction of semantically structured omics datasets, possibly representing various data types from independent studies. The recognition of entities specific for a domain allows the retrieval of samples related to a given cell type or experimental condition, but also allows unravelling previously unanticipated relationships between experiments. Onassis applies Natural Language Processing tools to annotate sample’s and experiments’ descriptions, recognizing concepts from a multitude of biomedical ontologies and quantifying the similarities between pairs or groups of query studies. Moreover, it assists the semantically-driven analysis of the corresponding omics data. In particular the software includes modules to enable:
the retrieval of samples’ metadata from repositories of large scale biologial data
the annotation of these data with concepts belonging to Open Biomedical Ontologies (OBO)
the organization of the annotated samples in structured groups based on semantic similarity measures
the comparison of omics data (e.g. gene expression or ChIP enrichment) based on the entities associated to a set of samples and their relationship
Onassis relies on Conceptmapper, an Apache UIMA (Unstructured Information Management Architecture) dictionary lookup tool to retrieve dictionary terms in a given text. https://uima.apache.org/downloads/sandbox/ConceptMapperAnnotatorUserGuide/ConceptMapperAnnotatorUserGuide.html
In particular, the ccp-nlp Conceptmapper wrapper, specific for the biomedical domain, implements a pipeline through which it is possible to retrieve concepts from OBO ontologies in any given text with different adjustable options (Verspoor et al. 2009).
Onassis features can be easily accessed through a main class named Onassis, having as slots ‘dictionary’, ‘entities’, ‘similarity’ and ‘scores’. In the following sections we first show details on the usage of classes and methods constituting the building blocks of a semantically-driven integrative analysis workflow. Next, in Section 6 we show how the Onassis class wraps all these functions for a simplified access and usage. Regarding the input data, Onassis can handle any type of text, but is particularly well suited for the analysis of the metadata from Gene Expression Omnibus (GEO). Indeed, it allows associating concepts from any OBO ontology to GEO metadata retrieved using GEOmetadb. In general, any table or database (such as Sequence Read Archive (SRA) (Zhu et al. 2013) or Cistrome (Mei et al. 2017)) containing textual descriptions that can be easily imported in R as a data frame can be used as input for Onassis. Regarding the dictionary module, gene/protein symbols or epigenetic modifications can also be recognized in the text, in addition to ontology concepts. This can be particularly important, especially when dealing with experiments directed to specific factors or marks (such as ChIP-seq experiments). The similarity module uses different semantic similarity measures to determine the semantic similarity of concepts in a given ontology. This module has been developed on the basis of the Java slib http://www.semantic-measures-library.org/sml. The score module applies statistical tests to determine if omics data from samples annotated with different concepts, belonging to one or more ontologies, are significantly different.
To run Onassis Java (>= 1.8) is needed. To install the package please run the following code
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Onassis")Onassis can be loaded with the following code
library(Onassis)Some of the optional functions, which will be described in the following parts of the vignette, require additional libraries. These include:
One of the most straightforward ways to retrieve metadata of samples provided in GEO is through the GEOmetadb package. In order to use GEOmetadb through Onassis, the corresponding SQLite database should be available. This can be downloaded by Onassis (see below), and this step has to be performed only once. As described below, Onassis provides functions to facilitate the retrieval of specific GEO metadata without the need of explicitly making SQL queries to the database. Additionally, an example on how to access SRAdb metadata, is also provided.
First, it is necessary to obtain a connection to the GEOmetadb SQLite database. If this were already downloaded, connectToGEODB returns a connection to the database given the full path to the SQLite database file. Alternatively, by setting download to TRUE the database is downloaded. The getGEOmetadata function can be used to retrieve the metadata related to specific GEO samples, taking as minimal parameters the connection to the database and one of the experiment types available. Optionally it is possible to specify the organism and the platform. The following code illustrates how to download the metadata corresponding to expression arrays, or DNA methylation sequencing experiments. The meth_metadata object, containing the results for the latter, was stored within Onassis. Therefore, the queries illustrated here can be skipped.
require('GEOmetadb')
## Running this function might take some time if the database (6.8GB) has to be downloaded.
geo_con <- connectToGEODB(download=TRUE)
#Showing the experiment types available in GEO
experiments <- experiment_types(geo_con)
#Showing the organism types available in GEO
species <- organism_types(geo_con)
#Retrieving Human gene expression metadata, knowing the GEO platform identifier, e.g. the Affymetrix Human Genome U133 Plus 2.0 Array
expression <- getGEOMetadata(geo_con, experiment_type='Expression profiling by array', gpl='GPL570')
#Retrieving the metadata associated to experiment type "Methylation profiling by high througput sequencing"
meth_metadata <- getGEOMetadata(geo_con, experiment_type='Methylation profiling by high throughput sequencing', organism = 'Homo sapiens')Some of the experiment types available are the following:
| experiments |
|---|
| Expression profiling by MPSS |
| Expression profiling by RT-PCR |
| Expression profiling by SAGE |
| Expression profiling by SNP array |
| Expression profiling by array |
| Expression profiling by genome tiling array |
| Expression profiling by high throughput sequencing |
| Genome binding/occupancy profiling by SNP array |
| Genome binding/occupancy profiling by array |
| Genome binding/occupancy profiling by genome tiling array |
Some of the organisms available are the following:
| species |
|---|
| Homo sapiens |
| Drosophila melanogaster |
| Mus musculus |
| Zea mays |
| Arabidopsis thaliana |
| Caenorhabditis elegans |
| Helicobacter pylori |
| Escherichia coli |
| Rattus norvegicus |
| Saccharomyces cerevisiae |
As specified above, meth_metadata was previously saved and can be loaded from the Onassis package external data (hover on the table to view additional rows and columns):
meth_metadata <- readRDS(system.file('extdata', 'vignette_data', 'GEOmethylation.rds', package='Onassis'))| series_id | gsm | title | gpl | source_name_ch1 | organism_ch1 | characteristics_ch1 | description | experiment_title | experiment_summary | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1251 | GSE42590 | GSM1045538 | 2316_DLPFC_Control | GPL10999 | Brain (dorsolateral prefrontal cortex) | Homo sapiens | tissue: Heterogeneous brain tissue | NA | Genome-wide DNA methylation profiling of human dorsolateral prefrontal cortex | Reduced representation bisulfite sequencing (RRBS) |
| 511 | GSE27432 | GSM678217 | hEB16d_H9_p65_RRBS | GPL9115 | embryoid body from hES H9 p65 | Homo sapiens | cell type: hEB16d_H9_p65 | reduced representation bisulfite sequencing | Genomic distribution and inter-sample variation of non-CG methylation across human cell types | DNA methylation plays an important role in develop |
| 2731 | GSE58889 | GSM1421876 | Normal_CD19_11 | GPL11154 | Normal CD19+ cells | Homo sapiens | cell type: Normal CD19+ cells; disease status: healthy | NA | Methylation disorder in CLL | We performed RRBS and WGBS on primary human chroni |
| 1984 | GSE50761 | GSM1228607 | Time Course Off-target Day 7 1 HBB133 | GPL15520 | K562 cells | Homo sapiens | cell line: K562 cells; target loci: Time Course Off-target Day 7 1 | 2013.03.16._MM364_analysis.csv | Targeted DNA demethylation using TALE-TET1 fusion proteins | Recent large-scale studies have defined genomewide |
| 851 | GSE36173 | GSM882245 | H1 human ES cells | GPL10999 | H1 human ES cells | Homo sapiens | cell line: H1 | 5-hmC whole genome bisulfite sequencing | Base Resolution Analysis of 5-Hydroxymethylcytosine in the Mammalian Genome | The study of 5-hydroxylmethylcytosines (5hmC), the |
| 1966 | GSE50761 | GSM1228589 | Time Course HB-6 Day 4 1 HBB115 | GPL15520 | K562 cells | Homo sapiens | cell line: K562 cells; target loci: Time Course HB-6 Day 4 1 | 2013.03.16._MM364_analysis.csv | Targeted DNA demethylation using TALE-TET1 fusion proteins | Recent large-scale studies have defined genomewide |
| 1827 | GSE50761 | GSM1228450 | Off target -650 to -850 3 RHOX117 | GPL15520 | 293 cells | Homo sapiens | cell line: 293 cells; target loci: Off target -650 to -850 3 | 2013-07-23-MM195-288-394_analysis.csv | Targeted DNA demethylation using TALE-TET1 fusion proteins | Recent large-scale studies have defined genomewide |
| 378 | GSE26592 | GSM655200 | Endometrial Recurrent 5 | GPL9052 | Human endometrial specimen | Homo sapiens | tissue: Human endometrial specimen; cell type: primary tissues; disease status: Recurrent; chromatin selection: MBD protein | MBDCap using MethylMiner Methylated DNA Enrichment Kit (Invitrogen, ME 10025); library strategy: Endometrial samples: MBDCao-seq. Breast cells: MBDCap-seq.; library selection: Endometrial samples: MBDCap. Breast cells: MBDCap-seq. | Neighboring genomic regions influence differential methylation patterns of CpG islands in endometrial and breast cancers | We report the global methylation patterns by MBDCa |
| 1754 | GSE50761 | GSM1228377 | Initial Screen RH-3 -250-+1 2 RHOX44 | GPL15520 | HeLa cells | Homo sapiens | cell line: HeLa cells; target loci: Initial Screen RH-3 -250-+1 2 | 2013-07-12-MM564_analysis.csv | Targeted DNA demethylation using TALE-TET1 fusion proteins | Recent large-scale studies have defined genomewide |
| 2371 | GSE54961 | GSM1327281 | Healthy Control | GPL9052 | Healthy Control | Homo sapiens | etiology: Healthy Control; tissue: Peripheral venous blood; molecule subtype: serum cell-free DNA | Sample 1 | Epigenome analysis of serum cell-free circulating DNA in progression of HBV-related Hepatocellular carcinoma | Purpose: Aberrantly methylated DNA are hallmarks |
In this section we provide an example showing how it is possible to retrieve metadata from other sources such as SRA. This database is not directly supported by Onassis, since it is not available for Windows platforms. Hence, the code reported below is slightly more complicated, and exemplifies how to query the SRA database provided by the SRAdb package and store metadata of human ChIP-seq experiments within a data frame. Due to the size of the SRA database (2.4 GB for the compressed file, 36 GB for the .sqlite file), a sample of the results of the query is available within Onassis as external data (see below), and the example code illustrated here can be skipped.
# Optional download of SRAdb and connection to the corresponding sqlite file
require(SRAdb)
sqliteFileName <- '/pathto/SRAdb.sqlite'
sra_con <- dbConnect(SQLite(), sqliteFileName)
# Query for the ChIP-Seq experiments contained in GEO for human samples
library_strategy <- 'ChIP-Seq' #ChIP-Seq data
library_source='GENOMIC'
taxon_id=9606 #Human samples
center_name='GEO' #Data from GEO
# Query to the sample table
samples_query <- paste0("select sample_accession, description, sample_attribute, sample_url_link from sample where taxon_id='", taxon_id, "' and sample_accession IS NOT NULL", " and center_name='", center_name, "'" )
samples_df <- dbGetQuery(sra_con, samples_query)
samples <- unique(as.character(as.vector(samples_df[, 1])))
experiment_query <- paste0("select experiment_accession, center_name, title, sample_accession, sample_name, experiment_alias, library_strategy, library_layout, experiment_url_link, experiment_attribute from experiment where library_strategy='", library_strategy, "'" , " and library_source ='", library_source,"' ", " and center_name='", center_name, "'" )
experiment_df <- dbGetQuery(sra_con, experiment_query)
#Merging the columns from the sample and the experiment table
experiment_df <- merge(experiment_df, samples_df, by = "sample_accession")
# Replacing the '_' character with white spaces
experiment_df$sample_name <- sapply(experiment_df$sample_name, function(value) {gsub("_", " ", value)})
experiment_df$experiment_alias <- sapply(experiment_df$experiment_alias, function(value) {gsub("_", " ", value)})
sra_chip_seq <- experiment_dfThe query returns a table with thousands of samples. Alternatively, as described above, a sample of this table useful for the subsequent examples can be retrieved in Onassis:
sra_chip_seq <- readRDS(system.file('extdata', 'vignette_data', 'GEO_human_chip.rds', package='Onassis'))| sample_accession | experiment_accession | center_name | title | library_strategy | library_layout | experiment_url_link | experiment_attribute | description | sample_attribute | sample_url_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 5904 | SRS421364 | SRX278504 | GEO | GSM1142700: p53 ChIP LCL nutlin-3 treated; Homo sapiens; ChIP-Seq | ChIP-Seq | SINGLE - | GEO Sample: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1142700 | GEO Accession: GSM1142700 | NA | source_name: lymphoblastoid cells || cell type: nutlin-3 treated lymphoblastoid cells || coriell id: GM12878 || chip antibody: mouse monoclonal anti-human p53 (BD Pharmingen, cat# 554294) || BioSampleModel: Generic | GEO Sample GSM1142700: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1142700 |
| 4981 | SRS371783 | SRX199902 | GEO | GSM1022674: UW_ChipSeq_A549_InputRep1 | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1022674 | GEO Accession: GSM1022674 | NA | source_name: A549 || biomaterial_provider: ATCC || lab: UW || lab description: Stamatoyannopoulous - University of Washington || datatype: ChipSeq || datatype description: Chromatin IP Sequencing || cell: A549 || cell organism: human || cell description: epithelial cell line derived from a lung carcinoma tissue. (PMID: 175022), “This line was initiated in 1972 by D.J. Giard, et al. through explant culture of lung carcinomatous tissue from a 58-year-old caucasian male.” - ATCC, newly promoted to tier 2: not in 2011 analysis || cell karyotype: cancer || cell lineage: endoderm || cell sex: M || antibody: Input || antibody description: Control signal which may be subtracted from experimental raw signal before peaks are called. || treatment: None || treatment description: No special treatment or protocol applies || control: std || control description: Standard input signal for most experiments. || controlid: wgEncodeEH001904 || labexpid: DS18301 || labversion: WindowDensity-bin20-win+/-75 || replicate: 1 || BioSampleModel: Generic | GEO Sample GSM1022674: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1022674 |
| 4619 | SRS365824 | SRX190055 | GEO | GSM945272: UW_ChipSeq_HRPEpiC_Input | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM945272 | GEO Accession: GSM945272 | NA | source_name: HRPEpiC || biomaterial_provider: ScienCell || lab: UW || lab description: Stamatoyannopoulous - University of Washington || datatype: ChipSeq || datatype description: Chromatin IP Sequencing || cell: HRPEpiC || cell organism: human || cell description: retinal pigment epithelial cells || cell karyotype: normal || cell lineage: ectoderm || cell sex: U || antibody: Input || antibody description: Control signal which may be subtracted from experimental raw signal before peaks are called. || treatment: None || treatment description: No special treatment or protocol applies || control: std || control description: Standard input signal for most experiments. || controlid: wgEncodeEH000962 || labexpid: DS16014 || labversion: Bowtie 0.12.7 || replicate: 1 || BioSampleModel: Generic | GEO Sample GSM945272: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM945272 |
| 911 | SRS117344 | SRX028649 | GEO | GSM608166: H3K27me3_K562_ChIP-seq_rep1 | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM608166 | GEO Accession: GSM608166 | NA | source_name: chronic myeloid leukemia cell line || cell line: K562 || harvest date: 2008-06-12 || chip antibody: CST monoclonal rabbit rabbit anti-H3K27me3 || BioSampleModel: Generic | GEO Sample GSM608166: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM608166 |
| 4244 | SRS362733 | SRX186665 | GEO | GSM1003469: Broad_ChipSeq_Dnd41_H3K79me2 | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1003469 | GEO Accession: GSM1003469 | NA | source_name: Dnd41 || biomaterial_provider: DSMZ || datatype: ChipSeq || datatype description: Chromatin IP Sequencing || antibody antibodydescription: Rabbit polyclonal antibody raised against a peptide containing K79 di-methylation. Antibody Target: H3K79me2 || antibody targetdescription: H3K79me2 is a mark of the transcriptional transition region - the region between the initiation marks (K4me3, etc) and the elongation marks (K36me3). || antibody vendorname: Active Motif || antibody vendorid: 39143 || controlid: wgEncodeEH002434 || replicate: 1,2 || softwareversion: ScriptureVPaperR3 || cell sex: M || antibody: H3K79me2 || antibody antibodydescription: Rabbit polyclonal antibody raised against a peptide containing K79 di-methylation. Antibody Target: H3K79me2 || antibody targetdescription: H3K79me2 is a mark of the transcriptional transition region - the region between the initiation marks (K4me3, etc) and the elongation marks (K36me3). || antibody vendorname: Active Motif || antibody vendorid: 39143 || treatment: None || treatment description: No special treatment or protocol applies || control: std || control description: Standard input signal for most experiments. || controlid: Dnd41/Input/std || softwareversion: ScriptureVPaperR3 || BioSampleModel: Generic | GEO Sample GSM1003469: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1003469 |
| 7502 | SRS494656 | SRX369112 | GEO | GSM1252315: CHG092; Homo sapiens; ChIP-Seq | ChIP-Seq | SINGLE - | GEO Sample GSM1252315: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1252315 | GEO Accession: GSM1252315 | NA | source_name: Gastric Primary Sample || tissuetype: Tumor || chip antibody: H3K4me1 || reads length: 101 || BioSampleModel: Generic | GEO Sample GSM1252315: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1252315 |
| 2127 | SRS266173 | SRX099863 | GEO | GSM808752: MCF7_CTCF_REP1 | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM808752 | GEO Accession: GSM808752: | NA | source_name: breast adenocarcinoma cells || cell type: breast adenocarcinoma cells || cell line: MCF7 || antibody: CTCF || BioSampleModel: Generic | GEO Sample GSM808752: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM808752 |
| 6299 | SRS468164 | SRX332680 | GEO | GSM1204476: Input DNA for ChIP; Homo sapiens; ChIP-Seq | ChIP-Seq | SINGLE - | GEO Sample GSM1204476: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1204476 | GEO Accession: GSM1204476 | NA | source_name: MDAMB231 || cell line: MDAMB231 || chip antibody: input || BioSampleModel: Generic | GEO Sample GSM1204476: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1204476 |
| 832 | SRS115184 | SRX027300 | GEO | GSM593367: H3K4me3_H3 | ChIP-Seq | SINGLE - | GEO Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM593367 | GEO Accession: GSM593367 | NA | source_name: LCL || chip antibody: H3K4me3 || cell type: lymphoblastoid cell line || BioSampleModel: Generic | GEO Sample GSM593367: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM593367 |
| 8638 | SRS598154 | SRX528309 | GEO | GSM1375207: H3_ChIPSeq_Human; Homo sapiens; ChIP-Seq | ChIP-Seq | SINGLE - | GEO Sample GSM1375207: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1375207 | GEO Accession: GSM1375207 | NA | source_name: H3_ChIPSeq_Human || donor age: adult || cell type: sperm || chip antibody: H3F3B || chip antibody vendor: Abnova || BioSampleModel: Generic | GEO Sample GSM1375207: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1375207 |
The Onassis EntityFinder class has methods for annotating any text with dictionary terms. More specifically, Onassis can take advantage of the OBO dictionaries (http://www.obofoundry.org/).
The findEntities method supports input text in the form of:
Alternatively, the annotateDF method supports input text in the form of a data frame. In this case each row represents a document; the first column has to be the document identifier; the remaining columns will be combined and contain the text to analyze. This option can be conveniently used with the metadata retrieved from GEOmetadb and SRAdb, possibly selecting a subset of the available columns.
Onassis handles the convertion of OBO dictionaries into a format suitable to Conceptmapper: XML files with a set of entries specified by the xml tag <token> with a canonical name (the name of the entry) and one or more variants (synonyms).
The constructor CMdictionary creates an instance of the class CMdictionary.
dictType option to “CMDICT”.
# If a Conceptmapper dictionary is already available the dictType CMDICT can be specified and the corresponding file loaded
sample_dict <- CMdictionary(inputFileOrDb=system.file('extdata', 'cmDict-sample.cs.xml', package = 'Onassis'), dictType = 'CMDICT')
#Creation of a dictionary from the file sample.cs.obo available in OnassisJavaLibs
obo <- system.file('extdata', 'sample.cs.obo', package='OnassisJavaLibs')
sample_dict <- CMdictionary(inputFileOrDb=obo, outputDir=getwd(), synonymType='ALL')
# Creation of a dictionary for human genes/proteins. This requires org.Hs.eg.db to be installed
require(org.Hs.eg.db)
targets <- CMdictionary(dictType='TARGET', inputFileOrDb = 'org.Hs.eg.db', synonymType='EXACT')The following XML markup code illustrates a sample of the Conceptmapper dictionary corresponding to the Brenda tissue ontology.
<?xml version="1.0" encoding="UTF-8" ?>
<synonym>
<token id="http://purl.obolibrary.org/obo/BTO_0005205" canonical="cerebral artery">
<variant base="cerebral artery"/>
</token>
<token id="http://purl.obolibrary.org/obo/BTO_0002179" canonical="184A1N4 cell">
<variant base="184A1N4 cell"/>
<variant base="A1N4 cell"/>
</token>
<token id="http://purl.obolibrary.org/obo/BTO_0003871" canonical="uterine endometrial cancer cell">
<variant base="uterine endometrial cancer cell"/>
<variant base="endometrial cancer cell"/>
<variant base="uterine endometrial carcinoma cell"/>
<variant base="endometrial carcinoma cell"/>
</token>
</synonym>Conceptmapper includes 7 different options controlling the annotation step. These are documented in detail in the documentation of the CMoptions function. They can be listed through the listCMOptions function. The CMoptions constructor instantiates an object of class CMoptions with the different parameters that will be required for the subsequent step of annotation. We also provided getter and setter methods for each of the 7 parameters.
#Creating a CMoptions object and showing hte default parameters
opts <- CMoptions()
show(opts)## CMoptions object to set ConceptMapper Options## SearchStrategy: CONTIGUOUS_MATCH
## CaseMatch: CASE_INSENSITIVE
## Stemmer: NONE
## StopWords: NONE
## OrderIndependentLookup: ON
## FindAllMatches: YES
## SynonymType: ALLTo obtain the list of all the possible combinations:
combinations <- listCMOptions()To create a CMoptions object having has SynonymType ‘EXACT_ONLY’, that considers only exact synonyms, rather than ‘ALL’ other types included in OBO (RELATED, NARROW, BROAD)
myopts <- CMoptions(SynonymType='EXACT_ONLY')
myopts## CMoptions object to set ConceptMapper Options## SearchStrategy: CONTIGUOUS_MATCH
## CaseMatch: CASE_INSENSITIVE
## Stemmer: NONE
## StopWords: NONE
## OrderIndependentLookup: ON
## FindAllMatches: YES
## SynonymType: EXACT_ONLYTo change a given parameter, for example to use a search strategy based on the Longest match of not-necessarily contiguous tokens where overlapping matches are allowed:
#Changing the SearchStrategy parameter
SearchStrategy(myopts) <- 'SKIP_ANY_MATCH_ALLOW_OVERLAP'
myopts## CMoptions object to set ConceptMapper Options## SearchStrategy: SKIP_ANY_MATCH_ALLOW_OVERLAP
## CaseMatch: CASE_INSENSITIVE
## Stemmer: NONE
## StopWords: NONE
## OrderIndependentLookup: ON
## FindAllMatches: YES
## SynonymType: EXACT_ONLYThe class EntityFinder defines a type system and runs the Conceptmapper pipeline. It can search for concepts of any OBO ontology in a given text. The findEntities and annotateDF methods accept text within files or data.frame, respectively, as described in Section 4.1.
The function EntityFinder automatically adapts to the provided input type, creates an instance of the EntityFinder class to initialize the type system and runs Conceptmapper with the provided options and dictionary.
For example, to annotate the metadata derived from ChIP-seq experiments obtained from SRA with tissue and cell type concepts belonging to the sample ontology available in Onassis and containing tissues and cell names, the following code can be used:
sra_chip_seq <- readRDS(system.file('extdata', 'vignette_data', 'GEO_human_chip.rds', package='Onassis'))
chipseq_dict_annot <- EntityFinder(sra_chip_seq[1:50, c('experiment_accession', 'title', 'experiment_attribute', 'sample_attribute', 'description')], dictionary=sample_dict, options=myopts)The resulting data.frame contains, for each row, a match to the provided dictionary for the document/sample indicated in the first column. The annotation is reported with the id of the concept (term_id), its canonical name (term name), its URL in the obo format, and the matching sentence of the document.
| sample_id | term_id | term_name | term_url | matched_sentence |
|---|---|---|---|---|
| SRX027300 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX028649 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | T lymphoma cells with 10 ug/ml DRB treatment, pol II ChIP || cell |
| SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | cell line: Jurkat || cell type: T |
| SRX033328 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | cell type: T |
| SRX047080 | UBERON_0002367 | prostate gland | http://purl.obolibrary.org/obo/UBERON_0002367 | Prostate |
| SRX047080 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell: HCPEpiC || cell organism: Human || cell description: Human Choroid Plexus Epithelial |
| SRX080398 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell organism: Human || cell description: Human Choroid Plexus Epithelial |
| SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell description: Human Choroid Plexus Epithelial |
| SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | Epithelial Cells || cell |
| SRX084599 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX092417 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | B cells || cell |
| SRX092417 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX092417 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | Cell |
| SRX096365 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
| SRX099863 | UBERON_0000310 | breast | http://purl.obolibrary.org/obo/UBERON_0000310 | breast |
| SRX099863 | CL_0000000 | cell | http://purl.obolibrary.org/obo/CL_0000000 | cell |
The function filterTerms can be used to remove all the occurrences of unwanted terms, for example very generic terms.
chipseq_dict_annot <- filterTerms(chipseq_dict_annot, c('cell', 'tissue'))| sample_id | term_id | term_name | term_url | matched_sentence | |
|---|---|---|---|---|---|
| 3 | SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | T lymphoma cells with 10 ug/ml DRB treatment, pol II ChIP || cell |
| 4 | SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | cell line: Jurkat || cell type: T |
| 6 | SRX033328 | CL_0000084 | T cell | http://purl.obolibrary.org/obo/CL_0000084 | cell type: T |
| 7 | SRX047080 | UBERON_0002367 | prostate gland | http://purl.obolibrary.org/obo/UBERON_0002367 | Prostate |
| 9 | SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell: HCPEpiC || cell organism: Human || cell description: Human Choroid Plexus Epithelial |
| 11 | SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell organism: Human || cell description: Human Choroid Plexus Epithelial |
| 12 | SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | cell description: Human Choroid Plexus Epithelial |
| 13 | SRX080398 | CL_0000066 | epithelial cell | http://purl.obolibrary.org/obo/CL_0000066 | Epithelial Cells || cell |
| 15 | SRX092417 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | B cells || cell |
| 19 | SRX099863 | UBERON_0000310 | breast | http://purl.obolibrary.org/obo/UBERON_0000310 | breast |
| 21 | SRX101132 | UBERON_0002367 | prostate gland | http://purl.obolibrary.org/obo/UBERON_0002367 | prostate |
| 28 | SRX129103 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | B-cell |
| 30 | SRX150687 | CL_0000222 | mesodermal cell | http://purl.obolibrary.org/obo/CL_0000222 | cell: GM12878 || cell organism: human || cell description: B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus || cell karyotype: normal || cell lineage: mesoderm |
| 31 | SRX150687 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | cell: GM12878 || cell organism: human || cell description: B |
| 33 | SRX150687 | CL_0000222 | mesodermal cell | http://purl.obolibrary.org/obo/CL_0000222 | cell organism: human || cell description: B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus || cell karyotype: normal || cell lineage: mesoderm |
| 34 | SRX150687 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | cell organism: human || cell description: B |
| 35 | SRX150687 | CL_0000222 | mesodermal cell | http://purl.obolibrary.org/obo/CL_0000222 | cell description: B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus || cell karyotype: normal || cell lineage: mesoderm |
| 36 | SRX150687 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | cell description: B |
| 37 | SRX150687 | CL_0000945 | lymphocyte of B lineage | http://purl.obolibrary.org/obo/CL_0000945 | B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus || cell karyotype: normal || cell lineage: mesoderm || cell sex: F || treatment: None || treatment description: No special treatment or protocol applies || antibody: Pol2(phosphoS2) || antibody antibodydescription: Rabbit polyclonal against peptide conjugated to KLH derived from within residues 1600 - 1700 of |
| 38 | SRX150687 | CL_0000236 | B cell | http://purl.obolibrary.org/obo/CL_0000236 | B-lymphocyte, lymphoblastoid, International HapMap Project - CEPH/Utah - European Caucasion, Epstein-Barr Virus || cell |
The function EntityFinder can also be used to identify the targeted entity of each ChIP-seq experiment, by retrieving gene names and epigenetic marks in the ChIP-seq metadata.
#Finding the TARGET entities
target_entities <- EntityFinder(input=sra_chip_seq[1:50, c('experiment_accession', 'title', 'experiment_attribute', 'sample_attribute', 'description')], options = myopts, dictionary=targets)| sample_id | term_id | term_name | term_url | matched_sentence |
|---|---|---|---|---|
| SRX027300 | H3K4me3 | H3K4me3 | NA | H3K4me3 |
| SRX028649 | H3K27me3 | H3K27me3 | NA | H3K27me3 |
| SRX080398 | 10664 | CTCF | NA | CTCF |
| SRX084599 | 604 | BCL6 | NA | BCL6 |
| SRX096365 | H3K4me2 | H3K4me2 | NA | H3K4me2 |
| SRX099863 | 10664 | CTCF | NA | CTCF |
| SRX109450 | H3K27me3 | H3K27me3 | NA | H3K27me3 |
| SRX113180 | H3K4me2 | H3K4me2 | NA | H3K4me2 |
| SRX114958 | 23133 | PHF8 | NA | PHF8 |
| SRX114963 | 23512 | SUZ12 | NA | SUZ12 |
| SRX116426 | 10013 | HDAC6 | NA | HDAC6 |
| SRX150687 | 1283 | CTD | NA | CTD |
| SRX155719 | 3297 | HSF1 | NA | HSF1 |
| SRX185917 | 2305 | FOXM1 | NA | FOXM1 |
| SRX186621 | 7975 | MAFK | NA | MAFK |
| SRX186621 | 4778 | NFE2 | NA | NFE2 |
| SRX186665 | H3K79me2 | H3K79me2 | NA | H3K79me2 |
| SRX186733 | 929 | CD14 | NA | CD14 |
| SRX186733 | H3K79me2 | H3K79me2 | NA | H3K79me2 |
| SRX190202 | 6938 | TCF12 | NA | TCF12 |
Once a set of samples is annotated, i.e. associated to a set of ontology concepts, Onassis allows the quantification of the similarity among these samples based on the semantic similarity between the corresponding concepts. Similarity is an Onassis class applying methods of the Java library slib (Harispe et al. 2014), which builds a semantic graph starting from OBO ontology concepts and their hierarchical relationships.
The following methods are available and are automatically chosen depending on the settings of the Similarity function. The sim and groupsim methods allow the computation of semantic similarity between single terms (pairwise measures) and between group of terms (groupwise measures), respectively. Pairwise measures can be edge based, if they rely only on the structure of the ontology, or information-content based if they also consider the information that each term in the ontology carries. Rather, groupwise measures can be indirect, if they compute the pairwise similarity between each couple of terms, or direct if they consider each set of concepts as a whole.
The samplesim method allows to determine the semantic similarity between two documents, each possibly associated to multiple concepts. Finally, the multisim method allows to determine the semantic similarity between documents annotated with two or more ontologies: first samplesim is run for each ontology, then a user defined function can be used to aggregate the resulting semantic similarities for each pair of documents.
The function listSimilarities shows all the measures supported by Onassis. For details about the measures run {?Similarity}.
#Instantiating the Similarity
similarities <- listSimilarities()The following example shows pairwise similarities between the individual concepts of previously annotated ChIP-seq experiments metadata. The lin similarity measure is used by default, which relies on a ratio between the Information content (IC) of the terms most specific common ancestor, and the sum of their IC (based on the information content of their most informative common ancestor). In particular, the seco information content is used by default, which determines the specificity of each concept based on the number of concepts it subsumes.
#Retrieving URLS of concepts
found_terms <- as.character(unique(chipseq_dict_annot$term_url))
# Creating a dataframe with all possible couples of terms and adding a column to store the similarity
pairwise_results <- t(combn(found_terms, 2))
pairwise_results <- cbind(pairwise_results, rep(0, nrow(pairwise_results)))
# Similarity computation for each couple of terms
for(i in 1:nrow(pairwise_results)){
pairwise_results[i, 3] <- Similarity(obo, pairwise_results[i,1], pairwise_results[i, 2])
}
colnames(pairwise_results) <- c('term1', 'term2', 'value')
# Adding the term names from the annotation table to the comparison results
pairwise_results <- merge(pairwise_results, chipseq_dict_annot[, c('term_url', 'term_name')], by.x='term2', by.y='term_url')
colnames(pairwise_results)[length(colnames(pairwise_results))] <- 'term2_name'
pairwise_results <- merge(pairwise_results, chipseq_dict_annot[, c('term_url', 'term_name')], by.x='term1', by.y='term_url')
colnames(pairwise_results)[length(colnames(pairwise_results))] <- 'term1_name'
pairwise_results <- unique(pairwise_results)
# Reordering the columns
pairwise_results <- pairwise_results[, c('term1', 'term1_name', 'term2', 'term2_name', "value")]Noteworthy, the terms ‘B-cell’ and ‘lymphocyte’ are closer (similarity 0.83) than ‘B cell’ and ‘epithelial cell’ (similarity 0.26). It is also possible to compute the semantic similarity between two groups of terms. For example, to determine a value of similarity for the combination of (‘non-terminally differentiated cell’, ‘epithelial cell’) and the combination of (‘lymphocyte’ , ‘B cell’) we can use the ui measure (set as default measure in Onassis), a groupwise direct measure combining the intersection and the union of the set of ancestors of the two groups of concepts.
oboprefix <- 'http://purl.obolibrary.org/obo/'
Similarity(obo, paste0(oboprefix, c('CL_0000055', 'CL_0000066')) , paste0(oboprefix, c('CL_0000542', 'CL_0000236')))## [1] 0.1764706The similarity between these two groups of terms is low (in the interval [0, 1]), while the addition of the term ‘lymphocyte of B lineage’ to the first group the group similarity increases.
Similarity(obo, paste0(oboprefix, c('CL_0000055', 'CL_0000236' ,'CL_0000236')), paste0(oboprefix, c('CL_0000542', 'CL_0000066')))## [1] 0.6470588Similarity also supports the computation of the similarity between annotated samples. Since each sample is typically associated tu multiple terms, Similarity runs in the groupwise mode when applied to samples. To this end, samples identifiers and a data frame with previously annotated concepts returned by EntityFinder are required.
# Extracting all the couples of samples
annotated_samples <- as.character(as.vector(unique(chipseq_dict_annot$sample_id)))
samples_couples <- t(combn(annotated_samples, 2))
# Computing the samples semantic similarity
similarity_results <- apply(samples_couples, 1, function(couple_of_samples){
Similarity(obo, couple_of_samples[1], couple_of_samples[2], chipseq_dict_annot)
})
#Creating a matrix to store the results of the similarity between samples
similarity_matrix <- matrix(0, nrow=length(annotated_samples), ncol=length(annotated_samples))
rownames(similarity_matrix) <- colnames(similarity_matrix) <- annotated_samples
# Filling the matrix with similarity values
similarity_matrix[lower.tri(similarity_matrix, diag=FALSE)] <- similarity_results
similarity_matrix <- t(similarity_matrix)
similarity_matrix[lower.tri(similarity_matrix, diag=FALSE)] <- similarity_results
# Setting the diagonal to 1
diag(similarity_matrix) <- 1
# Pasting the annotations to the sample identifiers
samples_legend <- aggregate(term_name ~ sample_id, chipseq_dict_annot, function(aggregation) paste(unique(aggregation), collapse=',' ))
rownames(similarity_matrix) <- paste0(rownames(similarity_matrix), ' (', samples_legend[match(rownames(similarity_matrix), samples_legend$sample_id), c('term_name')], ')')
# Showing a heatmap of the similarity between samples
heatmap.2(similarity_matrix, density.info = "none", trace="none", main='Samples\n semantic\n similarity', margins=c(12,50), cexRow = 2, cexCol = 2, keysize = 0.5)