Package 'IDConverter'

Convert Identifiers with Custom Database

Description

Convert Identifiers with Custom Database

Usage

convert_custom(x, from = NULL, to = NULL, dt = NULL, multiple = FALSE)

Arguments

x	A character vector to convert.
from	Which identifier type to be converted.
to	Identifier type convert to.
dt	A data.frame as database for conversion.
multiple	if TRUE, return a data.table instead of a string vector, so multiple identifier mappings can be kept.

Value

A character vector.

Examples

dt <- data.table::data.table(UpperCase = LETTERS[1:5], LowerCase = letters[1:5])
dt
x <- convert_custom(c("B", "C", "E", "E", "FF"), from = "UpperCase", to = "LowerCase", dt = dt)
x

---

Convert Human/Mouse Gene IDs between Ensembl and Hugo Symbol System

Description

Convert Human/Mouse Gene IDs between Ensembl and Hugo Symbol System

Usage

convert_hm_genes(
  IDs,
  type = c("ensembl", "symbol"),
  genome_build = c("hg38", "hg19", "mm10", "mm9"),
  multiple = FALSE
)

Arguments

IDs	a character vector to convert.
type	type of input IDs, could be 'ensembl' or 'symbol'.
genome_build	reference genome build.
multiple	if TRUE, return a data.table instead of a string vector, so multiple identifier mappings can be kept.

Value

a vector or a data.table.

Examples

convert_hm_genes("ENSG00000243485")
convert_hm_genes("ENSG00000243485", multiple = TRUE)
convert_hm_genes(c("TP53", "KRAS", "EGFR", "MYC"), type = "symbol")

---

Convert ICGC Identifiers

Description

Run data("icgc") to see detail database for conversion.

Usage

convert_icgc(
  x,
  from = "icgc_specimen_id",
  to = "icgc_donor_id",
  multiple = FALSE
)

Arguments

x	A character vector to convert.
from	Which identifier type to be converted. One of .
to	Identifier type convert to. Same as parameter from.
multiple	if TRUE, return a data.table instead of a string vector, so multiple identifier mappings can be kept.

Value

A character vector.

Examples

x <- convert_icgc("SP29019")
x

## Not run: 
convert_icgc("SA170678")

## End(Not run)

---

Convert PCAWG Identifiers

Description

Run data("pcawg_full") or data("pcawg_simple") to see detail database for conversion. The pcawg_simple database only contains PCAWG white-list donors.

Usage

convert_pcawg(
  x,
  from = "icgc_specimen_id",
  to = "icgc_donor_id",
  db = c("full", "simple"),
  multiple = FALSE
)

Arguments

x	A character vector to convert.
from	Which identifier type to be converted. For db "full", one of . For db "simple", one of .
to	Identifier type convert to. Same as parameter from.
db	Database, one of "full" (for data("pcawg_full")) or "simple" (for data("pcawg_simple")).
multiple	if TRUE, return a data.table instead of a string vector, so multiple identifier mappings can be kept.

Value

A character vector.

Examples

x <- convert_pcawg("SP1677")
x

y <- convert_pcawg("DO804",
  from = "icgc_donor_id",
  to = "icgc_specimen_id", multiple = TRUE
)
y

## Not run: 
convert_pcawg("SA5213")

## End(Not run)

---

Convert TCGA Identifiers

Description

Run data("tcga") to see detail database for conversion.

Usage

convert_tcga(x, from = "sample_id", to = "submitter_id", multiple = FALSE)

Arguments

x	A character vector to convert.
from	Which identifier type to be converted. One of .
to	Identifier type convert to. Same as parameter from.
multiple	if TRUE, return a data.table instead of a string vector, so multiple identifier mappings can be kept.

Value

A character vector.

Examples

x <- convert_tcga("TCGA-02-0001-10")
x

## Not run: 
convert_tcga("TCGA-02-0001-10A-01W-0188-10")

## End(Not run)

---

Filter TCGA Replicate Sample Barcodes

Description

Check details for filter rules.

Usage

filter_tcga_barcodes(
  tsb,
  analyte_target = c("DNA", "RNA"),
  decreasing = TRUE,
  analyte_position = 20,
  plate = c(22, 25),
  portion = c(18, 19),
  filter_FFPE = FALSE
)

Arguments

tsb	a vector of TCGA sample barcodes.
analyte_target	type of barcodes, "DNA" or "RNA".
decreasing	if TRUE (default), use decreasing order to select barcode to keep.
analyte_position	bit position for analyte. DON'T CHANGE IT if you don't understand.
plate	bit position for plate. DON'T CHANGE IT if you don't understand.
portion	bit position for portion. DON'T CHANGE IT if you don't understand.
filter_FFPE	if TRUE (FALSE is default), filter out FFPE samples.

Details

In many instances there is more than one aliquot for a given combination of individual, platform, and data type. However, only one aliquot may be ingested into Firehose. Therefore, a set of precedence rules are applied to select the most scientifically advantageous one among them. Two filters are applied to achieve this aim: an Analyte Replicate Filter and a Sort Replicate Filter.

Analyte Replicate Filter

The following precedence rules are applied when the aliquots have differing analytes. For RNA aliquots, T analytes are dropped in preference to H and R analytes, since T is the inferior extraction protocol. If H and R are encountered, H is the chosen analyte. This is somewhat arbitrary and subject to change, since it is not clear at present whether H or R is the better protocol. If there are multiple aliquots associated with the chosen RNA analyte, the aliquot with the later plate number is chosen. For DNA aliquots, D analytes (native DNA) are preferred over G, W, or X (whole-genome amplified) analytes, unless the G, W, or X analyte sample has a higher plate number.

Sort Replicate Filter

The following precedence rules are applied when the analyte filter still produces more than one sample. The sort filter chooses the aliquot with the highest lexicographical sort value, to ensure that the barcode with the highest portion and/or plate number is selected when all other barcode fields are identical.

NOTE: Basically, user provides tsb and analyte_target is fine.

Value

a barcode list.

References

Rules:

• ⁠https://confluence.broadinstitute.org/display/GDAC/FAQ#FAQ-sampleTypesQWhatTCGAsampletypesareFirehosepipelinesexecutedupon⁠

FFPE cases:

• ⁠http://gdac.broadinstitute.org/runs/sampleReports/latest/FPPP_FFPE_Cases.html⁠

Examples

filter_tcga_barcodes(c("TCGA-44-2656-01B-06D-A271-08", "TCGA-44-2656-01B-06D-A273-01"))
filter_tcga_barcodes(c("TCGA-44-2656-01B-06D-A271-08", "TCGA-44-2656-01B-06D-A273-01"),
  filter_FFPE = TRUE
)

---

ICGC Sample Identifiers

Description

ICGC Sample Identifiers

Format

A data frame with 155874 rows and 6 variables.

Source

https://dcc.icgc.org/repositories

Examples

load_data("icgc")

---

Load Data from Local or Remote Zenodo Repository

Description

Data are stored in remote Zenodo repo. This function will help download required data and load it into R.

Usage

load_data(x)

Arguments

x	a dataset name.

Value

typically a data.frame, depends on x.

Examples

load_data("pcawg_full")
load_data("pcawg_simple")
load_data("tcga")
load_data("icgc")

---

List Annotation Tables from annotables package

Description

The tables are obtained from annotables package and stored in Zenodo for better management. They can be downloaded and loaded with load_data(). See details for more info.

Usage

ls_annotables()

Details

Many bioinformatics tasks require converting gene identifiers from one convention to another, or annotating gene identifiers with gene symbol, description, position, etc. Sure, biomaRt does this for you, but users may get tired of remembering biomaRt syntax and hammering Ensembl's servers every time. These tables have basic annotation information from Ensembl Genes for:

• Human build 38 (grch38)

• Human build 37 (grch37)

• Mouse (grcm38)

• Rat (rnor6)

• Chicken (galgal5)

• Worm (wbcel235)

• Fly (bdgp6)

• Macaque (mmul801) Where each table contains:

• ensgene: Ensembl gene ID

• entrez: Entrez gene ID

• symbol: Gene symbol

• chr: Chromosome

• start: Start

• end: End

• strand: Strand

• biotype: Protein coding, pseudogene, mitochondrial tRNA, etc.

• description: Full gene name/description Additionally, there are tx2gene tables that link Ensembl gene IDs to Ensembl transcript IDs.

NOTE, the description above is copied from README of annotables package. If you are unclear to the data tables, please refer to annotables.

Value

a data.frame

References

https://github.com/stephenturner/annotables

Examples

ls_annotables()
load_data(ls_annotables()[1])

---

Parse Sample ID from GDC Portal File UUID

Description

Parse Sample ID from GDC Portal File UUID

Usage

parse_gdc_file_uuid(
  x,
  legacy = FALSE,
  fields = "cases.samples.submitter_id,cases.samples.sample_type,file_id",
  token = NULL,
  max_try = 5L
)

Arguments

x	a GDC manifest file or a vector of file UUIDs.
legacy	if use GDC legacy data.
fields	a list of fields to query. If it is a string, then fields should be separated by comma. It could also be a vector. See https://docs.gdc.cancer.gov/API/Users_Guide/Appendix_A_Available_Fields/#file-fields for list.
token	the token used for querying.
max_try	maximum try time.

Value

a data.frame

Examples

parse_gdc_file_uuid("fe522fc8-e690-49b9-b3b6-fa3658705057")
parse_gdc_file_uuid(
  c(
    "fe522fc8-e690-49b9-b3b6-fa3658705057",
    "2c16506f-1110-4d60-81e3-a85233c79909"
  )
)

---

PCAWG Full Sample Identifiers

Description

PCAWG Full Sample Identifiers

Format

A data frame with 7255 rows and 8 variables.

Source

https://dcc.icgc.org/releases/PCAWG

Examples

load_data("pcawg_full")

---

PCAWG Mutation Related Simplified Sample Identifiers

Description

This dataset contains less records than data("pcawg_full") but with more ID columns. Of note, only white-list donors included.

Format

A data frame with 2583 rows and 12 variables.

Source

https://www.nature.com/articles/s41586-020-1969-6

Examples

load_data("pcawg_simple")

---

TCGA Case Identifiers

Description

How to get the dataset can be viewed in code under data-raw. Cases in case_id column can be directly mapped to a GDC portal page, e.g. https://portal.gdc.cancer.gov/cases/30a1fe5e-5b12-472c-aa86-c2db8167ab23.

Format

A data frame with 150849 rows and 5 variables.

Source

https://portal.gdc.cancer.gov/

Examples

load_data("tcga")

---