Functions to add metadata to data.table objects.
add_ancestor(
phenos,
lvl = 2,
hpo = get_hpo(),
keep_descendants = NULL,
remove_descendants = NULL,
force_new = FALSE
)
add_death(
phenos,
keep_deaths = NULL,
all.x = TRUE,
allow.cartesian = TRUE,
agg_by = NULL
)
add_disease_genes(phenos, all.x = TRUE, verbose = TRUE)
add_evidence(
phenos,
evidence_score_threshold = NULL,
evidence_score_threshold_metric = "evidence_score_sum",
all.x = TRUE,
allow.cartesian = FALSE,
agg_by = c("disease_id", "gene_symbol"),
default_score = 1,
...
)
add_gene_frequency(
phenotype_to_genes = load_phenotype_to_genes(),
gene_frequency_threshold = NULL,
all.x = TRUE,
allow.cartesian = FALSE,
verbose = TRUE
)
add_genes(
phenos = NULL,
phenotype_to_genes = load_phenotype_to_genes(),
hpo = get_hpo(),
by = c("hpo_id", "hpo_name", "disease_id", "disease_name", "disease_description"),
gene_col = "gene_symbol",
all.x = FALSE,
allow.cartesian = FALSE
)
add_gpt_annotations(
phenos,
annot = gpt_annot_codify(reset_weights_dict = TRUE)$annot_weighted,
annot_cols = names(annot)[!names(annot) %in% c("hpo_id", "hpo_name")],
gpt_filters = `names<-`(rep(list(NULL), length(annot_cols)), annot_cols),
force_new = FALSE
)
add_hpo_definition(
phenos,
hpo = get_hpo(),
line_length = FALSE,
use_api = FALSE,
verbose = TRUE
)
add_hpo_id(phenos, hpo = get_hpo(), ignore_case = TRUE)
add_hpo_name(phenos, hpo = get_hpo())
add_info_content(phenos, hpo = get_hpo())
add_mondo(phenos, input_col = "disease_id", map_to = "hpo", ...)
add_ndisease(
phenos,
pheno_ndiseases_threshold = NULL,
all.x = TRUE,
allow.cartesian = FALSE,
verbose = TRUE
)
add_omop(
phenos,
input_col = "hpo_id",
all.x = TRUE,
allow.cartesian = FALSE,
force_new = FALSE,
verbose = TRUE
)
add_onset(
phenos,
keep_onsets = NULL,
agg_by = NULL,
all.x = TRUE,
allow.cartesian = FALSE
)
add_ont_lvl(
phenos,
hpo = get_hpo(),
absolute = TRUE,
keep_ont_levels = NULL,
...
)
add_pheno_frequency(
phenos,
pheno_frequency_threshold = NULL,
all.x = TRUE,
allow.cartesian = FALSE
)
add_prevalence(
phenos,
input_col = "disease_id",
drop_na = TRUE,
method = "orphanet"
)
add_severity(
phenos,
hpo = get_hpo(),
all.x = TRUE,
allow.cartesian = FALSE,
severity_threshold = NULL
)
add_tier(
phenos,
all.x = TRUE,
include_disease_characteristics = TRUE,
auto_assign = TRUE,
hpo = get_hpo(),
keep_tiers = NULL,
verbose = TRUE
)
A data.table containing HPO IDs and other metadata.
How many levels deep into the ontology to get ancestors from. For example:
1: "All"
2: "Phenotypic abnormality"
3: "Abnormality of the nervous system"
4: "Abnormality of nervous system physiology"
5: "Neurodevelopmental abnormality" or "Behavioral abnormality"
Human Phenotype Ontology object, loaded from get_ontology.
Terms whose descendants should be kept
(including themselves).
Set to NULL
(default) to skip this filtering step.
Terms whose descendants should be removed
(including themselves).
Set to NULL
(default) to skip this filtering step.
Force a new query to the OARD API instead of using pre-downloaded data.
The age of death associated with each HPO ID to keep. If >1 age of death is associated with the term, only the earliest age is considered. See add_death for details.
logical; if TRUE
, rows from x
which have no matching row
in y
are included. These rows will have 'NA's in the columns that are usually
filled with values from y
. The default is FALSE
so that only rows with
data from both x
and y
are included in the output.
See allow.cartesian
in [.data.table
.
Column to aggregate age of onset metadata by.
Print messages.
The minimum threshold of mean evidence scores of each gene-phenotype association to keep.
The metric to use for filtering with
evidence_score_threshold
.
Default evidence score to apply to gene-disease associations that are present in the HPO annotations but don't have evidence scores in the GenCC annotations.
Arguments passed on to KGExplorer::get_gencc
, KGExplorer::map_mondo
, KGExplorer::get_ontology_levels
save_dir
Directory to save a file to.
dict
A named vector of evidence score mappings. See here for more information.
dat
data.table with genes.
output_col
Column name of output IDs.
to
Character vector of database(s) to map IDs to.
When not "mondo"
, can supply multiple alternative databases to map to
(e.g. c("OMIM","Orphanet","DECIPHER")
).
map_types
Mapping types to include.
top_n
Top number of mappings to return per top_by
grouping.
Set to NULL
to skip this step.
add_name
Logical, if TRUE, add mondo name column.
add_definitions
logical, if TRUE, add mondo definition column.
ont
An ontology of class ontology_DAG.
terms
A vector of ontology term IDs.
remove_terms
Character vector of term IDs to exclude.
reverse
If TRUE
, ontology
level numbers with be revered such that the level of the parent terms
are larger than the child terms.
Output of load_phenotype_to_genes mapping phenotypes to gene annotations.
Only keep genes with frequency
above the set threshold. Frequency ranges from 0-100 where 100 is
a gene that occurs 100% of the time in a given phenotype.
Include NA
if you wish to retain genes that
do not have any frequency data.
See add_gene_frequency for details.
A vector of shared column names in x
and y
to merge on.
This defaults to the shared key columns between the two tables.
If y
has no key columns, this defaults to the key of x
.
Name of the gene column.
GPT annotation data.
Columns to add.
A named list of filters to apply to the GPT annotations.
The number of desired words per line \<int\>
Get definitions from the HPO API, as opposed to a static local dataset.
Ignore case when mapping terms.
Name of the column containing the disease or phenotype IDs.
Mapping outputs to include (from Mondo IDs to another database's IDs).
Filter phenotypes by the maximum number of diseases they are associated with.
The age of onset associated with each HPO ID to keep. If >1 age of onset is associated with the term, only the earliest age is considered. See add_onset for details.
Make the levels absolute in the sense that they consider
the entire ontology (TRUE
).
Otherwise, levels will be relative to only the terms that are in
the provided subset of terms
AND are directly adjacent (connected)
to a given cluster of terms (FALSE
).
Only keep phenotypes at certain absolute ontology levels to keep. See add_ont_lvl for details.
Only keep phenotypes with frequency
above the set threshold. Frequency ranges from 0-100 where 100 is
a phenotype that occurs 100% of the time in all associated diseases.
Include NA
if you wish to retain phenotypes that
do not have any frequency data.
See add_pheno_frequency for details.
Whether to drop rows with missing prevalence data.
One of "orphanet" or "oard".
Only keep phenotypes with a mean
severity score (averaged across multiple associated diseases) below the
set threshold. The severity score ranges from 1-4 where 1 is the MOST severe.
Include NA
if you wish to retain phenotypes that
do not have any severity score.
Include phenotypes
that
are also high-level include_disease_characteristics
.
Automatically assing HPO IDs to Tiers by conducting regex searches for keywords that appear in the term name, or the names of its descendants or ancestors.
Tiers from hpo_tiers to keep.
Include NA
if you wish to retain phenotypes that
do not have any Tier assignment.
Annotated data.
phenos data.table with extra columns:
"AgeOfDeath": AgeOfDeath HPO IDs of disease phenotypes associated with the target hpo_id phenotype.
"AgeOfDeath_names": AgeOfDeath HPO names of disease phenotypes associated with the target hpo_id phenotype.
"AgeOfDeath_counts": The number of times each term in "AgeOfDeath_names" appears across associated disease phenotypes.
"AgeOfDeath_score_mean": Mean age of death score.
"AgeOfDeath_score_min": Minimum age of death score.
"AgeOfDeath_top": The most common age of death term.
"AgeOfDeath_earliest": The earliest age of death.
"AgeOfDeath_latest": The latest age of death.
phenos data.table with extra columns:
"evidence_score_min": Minimum evidence score.
"evidence_score_max": Maximum evidence score.
"evidence_score_mean": Mean evidence score.
phenos data.table with extra column
A named vector of HPO term descriptions.
phenos data.table with extra column
phenos data.table with extra columns.
phenos data.table with extra columns
phenos data.table with extra columns:
"onset": onset HPO IDs of disease phenotypes associated with the target hpo_id phenotype.
"onset_names": onset HPO names of disease phenotypes associated with the target hpo_id phenotype.
"onset_counts": The number of times each term in "onset_names" appears across associated disease phenotypes.
"onset_score_mean": Mean onset score.
"onset_score_min": Minimum onset score.
"onset_top": The most common onset term.
"onset_earliest": The earliest age of onset.
"onset_latest": The latest age of onset.
phenos data.table with extra column
phenos data.table with extra column
phenos data.table with extra columns
phenos data.table with extra column
add_ancestor()
: add_
Add ancestor
Assign each HPO ID to the higher-order ancestral term that it is part of.
add_death()
: add_
Add age of death
Add age of death for each HPO ID. AgeOfDeath IDs and assigned "AgeOfDeath_score" values:
HP:0005268 "Miscarriage" (AgeOfDeath_score=1)
HP:0003826 "Stillbirth" (AgeOfDeath_score=1)
HP:0034241 "Prenatal death" (AgeOfDeath_score=1)
HP:0003811 "Neonatal death" (AgeOfDeath_score=2)
HP:0001522 "Death in infancy" (AgeOfDeath_score=3)
HP:0003819 "Death in childhood" (AgeOfDeath_score=4)
HP:0011421 "Death in adolescence" (AgeOfDeath_score=5)
HP:0100613 "Death in early adulthood" (AgeOfDeath_score=6)
HP:0033764 "Death in middle age" (AgeOfDeath_score=7)
HP:0033763 "Death in adulthood" (AgeOfDeath_score=7)
HP:0033765 "Death in late adulthood" (AgeOfDeath_score=8)
add_disease_genes()
: add_
Add disease genes
Add genes that overlap between an HPO ID and an associated phenotype.
add_evidence()
: add_
Add evidence
Add the strength of evidence supporting each gene-disease association. Delphi survey evidence classification IDs and assigned "evidence_score" values:
GENCC:100001 "Definitive" (evidence_score=6)
GENCC:100002 "Strong" (evidence_score=5)
GENCC:100003 "Moderate" (evidence_score=4)
GENCC:100009 "Supportive" (evidence_score=3)
GENCC:100004 "Limited" (evidence_score=2)
GENCC:100005 "Disputed Evidence" (evidence_score=1)
GENCC:100006 "Refuted Evidence" (evidence_score=0)
GENCC:100008 "No Known Disease Relationship" (evidence_score=0)
add_gene_frequency()
: add_
Add gene frequency
Add gene-level frequency, i.e. how often mutations in a given gene are associated with a given phenotype. Numeric frequency columns are on a 0-100% scale.
add_genes()
: add_
Add genes
Add genes associated with each phenotype (in the context of a particular disease).
add_gpt_annotations()
: add_
Add ancestor
Add annotations generated with a Large Language Model.
add_hpo_definition()
: add_
Get term definition
This function accesses the HPO API to get a description/definition of an
HPO term. If a line_length
\> 0 is passed to the function, it will add
newlines every nth word. This can be useful when displaying the description
in plots with limited space.
add_hpo_id()
: add_
Add HPO ID column to dataframe
Adds the HPO term ID column "hpo_id".
add_hpo_name()
: add_
Add HPO name column to dataframe
Adds the HPO term name column "hpo_name".
add_info_content()
: add_
Add information content
Add a column containing the information content score for each HPO ID.
add_mondo()
: add_
Add Mondo metadata
Add Mondo metadata (MONDO ID mappings, names, and definitions) for diseases using files from their respective databases: e.g. OMIM, DECIPHER, Orphanet.
add_ndisease()
: add_
Add N diseases
Annotate each HPO term with the total number of disease they are associated with.
add_omop()
: add_
Add OMOP
Add metadata from MONDO, including:
mondo_id: MONDO term ID.
mondo_name: MONDO term name.
mondo_def: MONDO term definition.
add_onset()
: add_
Add age of onset
Add age of onset for each HPO ID. onset IDs and assigned "onset_score" values:
HP:0011461 "Fetal onset" (onset_score=1)
HP:0030674 "Antenatal onset" (onset_score=2)
HP:0003577 "Congenital onset" (onset_score=3)
HP:0003623 "Neonatal onset" (onset_score=4)
HP:0003593 "Infantile onset" (onset_score=5)
HP:0011463 "Childhood onset" (onset_score=6)
HP:0003621 "Juvenile onset" (onset_score=7)
HP:0011462 "Young adult onset" (onset_score=8)
HP:0003581 "Adult onset" (onset_score=9)
HP:0003596 "Middle age onset" (onset_score=10)
HP:0003584 "Late onset" (onset_score=11)
add_ont_lvl()
: add_
Add ontology level
Add the relative ontology level for each HPO ID.
add_pheno_frequency()
: add_
Add phenotype frequency
Add phenotype-level frequency, i.e. how often a phenotype occurs in a given disease.
add_prevalence()
: add_
Add prevalence
Add a column containing the prevalence score for each disease ("disease_id") or phenotype ("hpo_id").
add_severity()
: add_
Add HPO modifiers
Annotate each HPO with modifier terms, including (but not limited to) progression and severity ratings. In order of increasing severity:
HP:0012825 "Mild" (Severity_score=4)
HP:0012827 "Borderline" (Severity_score=3)
HP:0012828 "Severe" (Severity_score=2)
HP:0012829"Profound" (Severity_score=1)
add_tier()
: add_
Add severity Tiers
Add severity Tier for each HPO ID, in accordance with the rating system provided by Lazarin et al (2014). In order of increasing severity:
Tier 4 Reduced fertility
Tier 3 Sensory impairment: vision, Immunodeficiency/cancer, Sensory impairment: hearing, Sensory impairment: touch, other (including pain), Mental illness, Dysmorphic features
Tier 2 Shortened life span: premature adulthood, Impaired mobility, Internal physical malformation
Tier 1 Shortened life span: infancy, Shortened life span: childhood/adolescence, Intellectual disability
phenos <- example_phenos()
phenos2 <- add_ancestor(phenos = phenos, lvl=5)
#> Adding level-5 ancestor to each HPO ID.
#> Adding ancestor metadata.
#> Ancestor metadata already present. Use force_new=TRUE to overwrite.
#> 10 associations remain after filtering.
phenos <- example_phenos()
phenos2 <- add_death(phenos = phenos)
#> Annotating phenos with AgeOfDeath.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
if (FALSE) { # \dontrun{
phenos <- load_phenotype_to_genes()
phenos2 <- add_severity(phenos = phenos)
} # }
phenos <- load_phenotype_to_genes()
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
phenos2 <- add_evidence(phenos = phenos)
#> Annotating gene-disease associations with Evidence Score
#> Gathering data from GenCC.
#> Importing cached file.
#> Evidence scores for:
#> - 10514 diseases
#> - 5171 genes
#> + Version: 2024-12-19
phenotype_to_genes <- load_phenotype_to_genes()[seq(1000),]
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
phenos2 <- add_gene_frequency(phenotype_to_genes = phenotype_to_genes)
#> Annotating gene frequencies.
#> Reading cached RDS file: genes_to_phenotype.txt
#> + Version: v2024-12-12
phenos <- example_phenos()
phenos2 <- add_genes(phenos = phenos)
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
phenos <- example_phenos()
phenos2 <- add_gpt_annotations(phenos)
#> Loading required namespace: piggyback
#> Translating ontology terms to ids.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> 383 phenotypes do not have matching HPO IDs.
#> Reading in GPT annotations for 16,753 phenotypes.
phenos <- example_phenos()
phenos2 <- add_hpo_definition(phenos = phenos)
#> Adding term definitions.
phenotype_to_genes <- load_phenotype_to_genes()
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
phenos <- unique(phenotype_to_genes[,c("hpo_id","hpo_name")])
phenos2 <- add_hpo_id(phenos=phenos)
phenos <- example_phenos()
phenos2 <- add_hpo_name(phenos=phenos)
#> Adding HPO names.
#> Translating ontology terms to names.
phenos <- example_phenos()
phenos2 <- add_info_content(phenos = phenos)
#> Adding information_content scores.
phenos <- load_phenotype_to_genes(3)[seq(1000)]
#> Reading cached RDS file: phenotype.hpoa
#> + Version: v2024-12-12
phenos2 <- add_mondo(phenos = phenos)
#> Loading required namespace: downloadR
#> Loading required namespace: echogithub
#> Mapping disease_id --> mondo_id
#> Loading cached ontology: /github/home/.cache/R/KGExplorer/mondo.rds
#> 0 / 55 (0%) mondo_id missing.
#> 0 / 55 (0%) mondo_name missing.
#> 18 / 55 (32.73%) mondo_def missing.
phenos <- example_phenos()
phenos2 <- add_ndisease(phenos = phenos)
#> Annotating phenos with n_diseases
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Reading cached RDS file: genes_to_phenotype.txt
#> + Version: v2024-12-12
#> Reading cached RDS file: phenotype.hpoa
#> + Version: v2024-12-12
phenos <- example_phenos()
phenos2 <- add_omop(phenos = phenos)
#> Annotating phenos with OMOP metadata.
#> 0 / 10 (0%) OMOP_ID missing.
#> 0 / 10 (0%) OMOP_NAME missing.
phenos <- example_phenos()
phenos2 <- add_onset(phenos = phenos)
#> Annotating phenos with onset.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
phenos <- make_phenos_dataframe(ancestor = "Neurodevelopmental delay")
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Extracting data for 24 descendents.
#> Computing gene counts.
#> Adding term definitions.
#> Adding level-2 ancestor to each HPO ID.
#> Adding ancestor metadata.
#> Ancestor metadata already present. Use force_new=TRUE to overwrite.
#> 24 associations remain after filtering.
#> Getting absolute ontology level for 19,025 IDs.
#> Computing ontology level / gene count ratio.
phenos2 <- add_ont_lvl(phenos = phenos)
phenos <- example_phenos()
phenos2 <- add_pheno_frequency(phenos = phenos)
#> Annotating phenotype frequencies.
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
phenos <- example_phenos()
phenos2 <- add_prevalence(phenos = phenos)
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
#> Mapping disease_id --> mondo_id
#> Loading cached ontology: /github/home/.cache/R/KGExplorer/mondo.rds
#> 5 / 7,303 (0.07%) mondo_id missing.
#> 5 / 7,303 (0.07%) mondo_name missing.
#> 2,646 / 7,303 (36.23%) mondo_def missing.
#> Mapping disease_id --> mondo_id
#> Loading cached ontology: /github/home/.cache/R/KGExplorer/mondo.rds
#> 25 / 6,089 (0.41%) mondo_id missing.
#> 25 / 6,089 (0.41%) mondo_name missing.
#> 1,079 / 6,089 (17.72%) mondo_def missing.
#> Prevalence added for 0 / 7,303 disease_id IDs (0%)
#> Prevalence added for 0 / 10 hpo_id IDs (0%)
#> Prevalence added for 0 / 6,777 mondo_id IDs (0%)
phenos <- example_phenos()
phenos2 <- add_severity(phenos = phenos)
#> Annotating phenos with modifiers
#> Reading cached RDS file: phenotype_to_genes.txt
#> + Version: v2024-12-12
#> Adding genes and disease IDs.
phenos <- example_phenos()
phenos2 <- add_tier(phenos = phenos)
#> Annotating phenos with Tiers.