Package 'rnaEditr' reference manual

Title:	Statistical analysis of RNA editing sites and hyper-editing regions
Description:	RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models.
Authors:	Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut]
Maintainer:	Lanyu Zhang <[email protected]>
License:	GPL-3
Version:	1.15.0
Built:	2024-07-01 04:48:39 UTC
Source:	https://github.com/bioc/rnaEditr

Extract clusters of RNA editing sites located closely in genomic regions.

Description

A wrapper function to extract clusters of RNA editing sites that are located closely in genomic regions.

Usage

AllCloseByRegions(
  regions_gr,
  rnaEditMatrix,
  maxGap = 50,
  minSites = 3,
  progressBar = "time"
)
AllCloseByRegions(
  regions_gr,
  rnaEditMatrix,
  maxGap = 50,
  minSites = 3,
  progressBar = "time"
)

Arguments

`regions_gr`	A GRanges object of input genomic regions.
`rnaEditMatrix`	A matrix (or data frame) of RNA editing level values on individual sites, with row names as site IDs in the form of "chrAA:XXXXXXXX", and column names as sample IDs. Please make sure to follow the format of example dataset (`data(rnaedit_df)`).
`maxGap`	An integer, genomic locations within `maxGap` from each other are placed into the same cluster. Defaults to 50.
`minSites`	An integer, minimum number of RNA editing sites within each resulting cluster. Defaults to 3.
`progressBar`	Name of the progress bar to use. There are currently five types of progress bars: `"time"`, `"none"`, `"text"`, `"tk"`, and `"win"`. Defaults to `"time"`. See `create_progress_bar` for more details.

Details

The algorithm of this function is based on the clusterMaker function in the bumphunter R package. Each cluster is essentially a group of site locations such that two consecutive locations in the cluster are separated by less than maxGap.

Value

A GRanges object containing genomic regions of RNA editing sites located closely within each input pre-defined genomic region.

Examples

  data(rnaedit_df)
  
  exm_regions <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  AllCloseByRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df,
    maxGap = 50,
    minSites = 3,
    progressBar = "time"
  )  
   
data(rnaedit_df)
  
  exm_regions <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  AllCloseByRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df,
    maxGap = 50,
    minSites = 3,
    progressBar = "time"
  )

Extracts contiguous co-edited genomic regions from input genomic regions .

Description

A wrapper function to extract contiguous co-edited genomic regions from input genomic regions.

Usage

AllCoeditedRegions(
  regions_gr,
  rnaEditMatrix,
  output = c("GRanges", "dataframe"),
  rDropThresh_num = 0.4,
  minPairCorr = 0.1,
  minSites = 3,
  method = c("spearman", "pearson"),
  returnAllSites = FALSE,
  progressBar = "time",
  verbose = TRUE
)
AllCoeditedRegions(
  regions_gr,
  rnaEditMatrix,
  output = c("GRanges", "dataframe"),
  rDropThresh_num = 0.4,
  minPairCorr = 0.1,
  minSites = 3,
  method = c("spearman", "pearson"),
  returnAllSites = FALSE,
  progressBar = "time",
  verbose = TRUE
)

Arguments

`regions_gr`	A GRanges object of input genomic regions.
`rnaEditMatrix`	A matrix (or data frame) of RNA editing level values on individual sites, with row names as site IDs in the form of "chrAA:XXXXXXXX", and column names as sample IDs. Please make sure to follow the format of example dataset (`data(rnaedit_df)`).
`output`	Type of output data. Defaults to `"GRanges"`.
`rDropThresh_num`	Threshold for minimum correlation between RNA editing levels of one site and the mean RNA editing levels of the rest of the sites. Please set a number between 0 and 1. Defaults to 0.4.
`minPairCorr`	Threshold for minimum pairwise correlation of sites within a selected cluster. To use this filter, set a number between -1 and 1 (defaults to 0.1). To select all clusters (i.e. no filter), please set this argument to -1.
`minSites`	Minimum number of sites to be considered as a region. Only regions with more than `minSites` number of sites will be returned.
`method`	Method for computing correlation. Defaults to `"spearman"`.
`returnAllSites`	When no contiguous co-edited regions are found in an input genomic region, `returnAllSites = TRUE` indicates returning all the sites in the input region, while `returnAllSites = FALSE` indicates not returning any site from input region. Defaults to FALSE.
`progressBar`	Name of the progress bar to use. There are currently five types of progress bars: `"time"`, `"none"`, `"text"`, `"tk"`, and `"win"`. Defaults to `"time"`. See `create_progress_bar` for more details.
`verbose`	Should messages and warnings be displayed? Defaults to FALSE, but is set to TRUE when called from within `SingleCoeditedRegion()`.

Value

When output is set as "GRanges", a GRanges object with seqnames, ranges and strand of the contiguous co-edited regions will be returned. When output is set as "dataframe", a data frame with following columns will be returned:

site : site ID.
chr : chromosome number.
pos : genomic position number.
r_drop : the correlation between RNA editing levels of one site and the mean RNA editing levels of the rest of the sites.
keep : indicator for co-edited sites, the sites with keep = 1 belong to the contiguous and co-edited region.
keep_contiguous : contiguous co-edited region number.
regionMinPairwiseCor : the pairwise correlation of a subregion.
keep_regionMinPairwiseCor : indicator for contiguous co-edited subregions, the regions with keepminPairwiseCor = 1 passed the minimum correlation and will be returned as a contiguous co-edited subregion.

Examples

  data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
   
data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )

Add Annotations to site-specific or region-based analysis results.

Description

Add annotations to site-specific or region-based analysis results from function TestAssociations.

Usage

AnnotateResults(
  results_df,
  closeByRegions_gr = NULL,
  inputRegions_gr = NULL,
  genome = c("hg38", "hg19"),
  analysis = c("region-based", "site-specific")
)
AnnotateResults(
  results_df,
  closeByRegions_gr = NULL,
  inputRegions_gr = NULL,
  genome = c("hg38", "hg19"),
  analysis = c("region-based", "site-specific")
)

Arguments

`results_df`	An output data frame from function `TestAssociations`, which includes variables for locations and result of statistical tests for the genomic sites or regions.
`closeByRegions_gr`	An output GRanges object from function `AllCloseByRegions`, defaults to `NULL`.
`inputRegions_gr`	A GRanges object for input genomic regions, defaults to `NULL`.
`genome`	Use `"hg19"` or `"hg38"` gene reference. Defaults to `"hg38"`.
`analysis`	Results type. Defaults to `"region-based"`. When it's set to `"site-specific"`, arguments `closeByRegions_gr` and `inputRegions_gr` will not be used and set to NULL automatically.

Value

A data frame with locations of the genomic sites or regions (seqnames, start, end, width), annotations for locations (inputRegion, closeByRegion, symbol), test statistics (estimate, stdErr or coef, exp_coef, se_coef), pValue and false discovery rate (fdr).

Examples

  data(rnaedit_df)
  
  # get GRanges for genes
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  # find close-by regions within the genes
  closebyRegions_gr <- AllCloseByRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df
  )
  
  # identify co-edited regions within the genes 
  coedited_gr <- AllCoeditedRegions(
    regions_gr = closebyRegions_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  # summarize editing levels within each gene by maximum
  summarizedRegions_df <- SummarizeAllRegions(
    regions_gr = coedited_gr,
    rnaEditMatrix = rnaedit_df,
    selectMethod = MaxSites
  )
  
  exm_pheno <- readRDS(
    system.file(
    "extdata",
    "pheno_df.RDS",
    package = 'rnaEditr',
    mustWork = TRUE
    )
  )
  
  # test summarized editing levels against survival outcome
  results_df <- TestAssociations(
    rnaEdit_df = summarizedRegions_df,
    pheno_df = exm_pheno,
    responses_char = "sample_type",
    covariates_char = NULL,
    respType = "binary"
  )
  
  AnnotateResults(
    results_df = results_df,
    closeByRegions_gr = closebyRegions_gr,
    inputRegions_gr = genes_gr,
    genome = "hg19"
  )
  
data(rnaedit_df)
  
  # get GRanges for genes
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  # find close-by regions within the genes
  closebyRegions_gr <- AllCloseByRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df
  )
  
  # identify co-edited regions within the genes 
  coedited_gr <- AllCoeditedRegions(
    regions_gr = closebyRegions_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  # summarize editing levels within each gene by maximum
  summarizedRegions_df <- SummarizeAllRegions(
    regions_gr = coedited_gr,
    rnaEditMatrix = rnaedit_df,
    selectMethod = MaxSites
  )
  
  exm_pheno <- readRDS(
    system.file(
    "extdata",
    "pheno_df.RDS",
    package = 'rnaEditr',
    mustWork = TRUE
    )
  )
  
  # test summarized editing levels against survival outcome
  results_df <- TestAssociations(
    rnaEdit_df = summarizedRegions_df,
    pheno_df = exm_pheno,
    responses_char = "sample_type",
    covariates_char = NULL,
    respType = "binary"
  )
  
  AnnotateResults(
    results_df = results_df,
    closeByRegions_gr = closebyRegions_gr,
    inputRegions_gr = genes_gr,
    genome = "hg19"
  )

Convert RNA editing matrix into a special data frame with class `rnaEdit_df`.

Description

Convert RNA editing matrix to a special data frame with class rnaEdit_df, which is then used to identify differentially co-edited regions with function TestAssociations.

Usage

CreateEditingTable(rnaEditMatrix)
CreateEditingTable(rnaEditMatrix)

Arguments

rnaEditMatrix

A matrix of RNA editing level values on individual sites, with row names as site IDs in the form of "chrAA:XXXXXXXX", and column names as sample IDs. Please make sure to follow the format of example dataset (data(rnaedit_df)).

Value

A dataset of class rnaEdit_df, includes variables seqnames, start, end, width and summarized RNA editing levels in each sample.

Examples

  data(rnaedit_df)
  CreateEditingTable(rnaEditMatrix = rnaedit_df)[1:3, 1:5]
  
data(rnaedit_df)
  CreateEditingTable(rnaEditMatrix = rnaedit_df)[1:3, 1:5]

Example breast cancer RNA editing dataset.

Description

A subset of the TCGA breast cancer RNA editing dataset for 272 edited sites on genes PHACTR4, CCR5, METTL7A and a few randomly sampled sites for 221 subjects.

Usage

rnaedit_df
rnaedit_df

Format

A data frame containing RNA editing levels for 272 sites (in the rows) for 221 subjects (in the columns). Row names are site IDs and column names are sample IDs.

Source

Synapse database ID: syn2374375.

Summarize RNA editing levels from multiple sites in regions.

Description

A wrapper function to summarize RNA editing levels from multiple sites in regions.

Usage

SummarizeAllRegions(
  regions_gr,
  rnaEditMatrix,
  selectMethod = MedianSites,
  progressBar = "time",
  ...
)
SummarizeAllRegions(
  regions_gr,
  rnaEditMatrix,
  selectMethod = MedianSites,
  progressBar = "time",
  ...
)

Arguments

`regions_gr`	A GRanges object of input genomic regions.
`rnaEditMatrix`	A matrix (or data frame) of RNA editing level values for individual sites, with row names as site IDs in the form of "chrAA:XXXXXXXX", and column names as sample IDs. Please make sure to follow the format of example dataset (`data(rnaedit_df)`).
`selectMethod`	Method for summarizing regions. Available options are `"MaxSites", "MeanSites", "MedianSites", "PC1Sites"`. Please see `RegionSummaryMethod` for more details.
`progressBar`	Name of the progress bar to use. There are currently five types of progress bars: `"time"`, `"none"`, `"text"`, `"tk"`, and `"win"`. Defaults to `"time"`. See `create_progress_bar` for more details.
`...`	Dots for additional internal arguments (currently unused).

Value

A data frame of the class rnaEdit_df, includes variables seqnames, start, end, width and summarized RNA editing levels in each sample.

Examples

  data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  exm_regions <- AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  SummarizeAllRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df
  )[1:3, 1:6]

data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  exm_regions <- AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  SummarizeAllRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df
  )[1:3, 1:6]

Transposed breast cancer example dataset.

Description

A subset of the TCGA breast cancer RNA editing dataset for 20 randomly selected RNA editing sites and 50 randomly selected subjects from example dataset rnaedit_df. Please note that this is only a computational testing dataset for inner functions of this package. To test main functions, please use dataset rnaedit_df instead.

Usage

t_rnaedit_df
t_rnaedit_df

Format

A data frame containing RNA editing levels for 50 subjects (in the rows) at 20 edited sites (in the columns). Row names are sample IDs and column names are site IDs.

Source

Synapse database ID: syn2374375.

Test associations between phenotype and RNA editing levels.

Description

A wrapper function to test associations between phenotype and RNA editing levels in single-site analysis or summarized RNA editing levels in region-based analysis.

Usage

TestAssociations(
  rnaEdit_df,
  pheno_df,
  responses_char,
  covariates_char = NULL,
  respType = c("binary", "continuous", "survival"),
  progressBar = "time",
  orderByPval = TRUE
)
TestAssociations(
  rnaEdit_df,
  pheno_df,
  responses_char,
  covariates_char = NULL,
  respType = c("binary", "continuous", "survival"),
  progressBar = "time",
  orderByPval = TRUE
)

Arguments

`rnaEdit_df`	A data frame with class `rnaEdit_df`, which is a output from function `CreateEditingTable()` or function `SummarizeAllRegions()`. This data frame should include RNA editing level values, with row names as site IDs or region IDs, and column names as sample IDs.
`pheno_df`	A data frame with phenotype and covariates, which should include all the samples in `rnaEdit_df`. Please make sure the input `pheno_df` has the variable named `"sample"` to indicate sample IDs.
`responses_char`	A character vector of names of response variables in `pheno_df`. When respType is set as `"survival"`, `responses_char` should have length 2. The first element must be the name of the variable with following up time, and the second element must be status indicator. Status indicator should be coded as 0/1(1=death), TRUE/FALSE(TRUE=death), or 1/2(death). Please make sure variable names are in this order. We have not tested this code on interval-censored data; use at your own risk. See `Surv` for more details.
`covariates_char`	A character vector of names of covariate variables in `pheno_df`.
`respType`	Type of outcome. Defaults to `"binary"`.
`progressBar`	Name of the progress bar to use. There are currently five types of progress bars: `"time"`, `"none"`, `"text"`, `"tk"`, and `"win"`. Defaults to `"time"`. See `create_progress_bar` for more details.
`orderByPval`	Sort co-edited regions by model p-value or not? Defaults to TRUE.

Value

A data frame with locations of the genomic regions or sites (seqnames, start, end, width), test statistics (estimate, stdErr or coef, exp_coef, se_coef), pValue and false discovery rate (fdr).

Examples

  data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  exm_regions <- AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  sum_regions <- SummarizeAllRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df,
    selectMethod = MaxSites
  )
  
  exm_pheno <- readRDS(
    system.file(
    "extdata",
    "pheno_df.RDS",
    package = 'rnaEditr',
    mustWork = TRUE
    )
  )
  
  TestAssociations(
    rnaEdit_df = sum_regions,
    pheno_df = exm_pheno,
    responses_char = "sample_type",
    covariates_char = NULL,
    respType = "binary"
  )

data(rnaedit_df)
  
  genes_gr <- TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  exm_regions <- AllCoeditedRegions(
    regions_gr = genes_gr,
    rnaEditMatrix = rnaedit_df,
    output = "GRanges",
    method = "spearman"
  )
  
  sum_regions <- SummarizeAllRegions(
    regions_gr = exm_regions,
    rnaEditMatrix = rnaedit_df,
    selectMethod = MaxSites
  )
  
  exm_pheno <- readRDS(
    system.file(
    "extdata",
    "pheno_df.RDS",
    package = 'rnaEditr',
    mustWork = TRUE
    )
  )
  
  TestAssociations(
    rnaEdit_df = sum_regions,
    pheno_df = exm_pheno,
    responses_char = "sample_type",
    covariates_char = NULL,
    respType = "binary"
  )

Transform gene symbols or region ranges into GRanges object.

Description

Transform a character vector of gene symbols or region ranges into a GRanges object.

Usage

TransformToGR(
  genes_char,
  type = c("symbol", "region"),
  genome = c("hg38", "hg19")
)
TransformToGR(
  genes_char,
  type = c("symbol", "region"),
  genome = c("hg38", "hg19")
)

Arguments

`genes_char`	A character vector of gene symbols or region ranges. If you select `type` to be `"symbol"`, then please make sure your input of `genes_char` is in the format of c("ABCB10", "PEX26"). If you select `type` to be `"region"`, then please make sure your input of `genes_char` is in the format of c("chr1:33772367-33791699", "chr22:18555686-18573797").
`type`	What is the type of `genes_char`. Can be `"symbol"` (default) or `"region"`.
`genome`	Use `"hg19"` or `"hg38"` gene reference. Defaults to `"hg38"`. It's only used when `type` is set to `"symbol"`

Details

TransformToGR() uses the hg19/hg38 genes to associate gene symbols with their genomic region ranges. The pre-processed dataset is saved in inst/extdata in this package.

Users who wish to add gene symbols to the GRanges created using function TransformToGR() can use function AddMetaData(). Please see AddMetaData for details.

Value

A GRanges object with seqnames, ranges and strand.

Examples

  TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  TransformToGR(
    genes_char = c("chr22:18555686-18573797", "chr22:36883233-36908148"),
    type = "region",
    genome = "hg19"
  )
 
TransformToGR(
    genes_char = c("PHACTR4", "CCR5", "METTL7A"),
    type = "symbol",
    genome = "hg19"
  )
  
  TransformToGR(
    genes_char = c("chr22:18555686-18573797", "chr22:36883233-36908148"),
    type = "region",
    genome = "hg19"
  )

Package 'rnaEditr'

Help Index

Extract clusters of RNA editing sites located closely in genomic regions.

Description

Usage

Arguments

Details

Value

See Also

Examples

Extracts contiguous co-edited genomic regions from input genomic regions .

Description

Usage

Arguments

Value

See Also

Examples

Add Annotations to site-specific or region-based analysis results.

Description

Usage

Arguments

Value

See Also

Examples

Convert RNA editing matrix into a special data frame with class rnaEdit_df.

Description

Usage

Arguments

Value

See Also

Examples

Example breast cancer RNA editing dataset.

Description

Usage

Format

Source

Summarize RNA editing levels from multiple sites in regions.

Description

Usage

Arguments

Value

See Also

Examples

Transposed breast cancer example dataset.

Description

Usage

Format

Source

Test associations between phenotype and RNA editing levels.

Description

Usage

Arguments

Value

See Also

Examples

Transform gene symbols or region ranges into GRanges object.

Description

Usage

Arguments

Details

Value

See Also

Examples

Convert RNA editing matrix into a special data frame with class `rnaEdit_df`.