2 Datasets
The input of rnaEditr are: (1) an RNA editing dataset,
with rows corresponding to the edited sites and columns corresponding to
the samples; (2) a phenotype dataset, with rows corresponding to
different samples, ordered in the same way as columns of dataset (1);
(3) a character string of genomic regions that users are interested in.
This is only required for region-based analysis and will be explained in
section 4.1.
The first dataset includes RNA editing levels for each sample at each
edited site. RNA editing levels are typically defined as the total
number of edited reads (i.e. reads with G nucleotides) divided by the
total number of reads covering the site (i.e. reads with A and G
nucleotides) (Breen et al. 2019 PMID: 31455887). They range from 0
(un-edited site) to 1 (completely edited site).
We assume quality control and normalization of the RNA editing
dataset have been performed, and that each site is edited in at least 5%
of the samples. For illustration, we use a subset of the TCGA breast
cancer RNA editing dataset (syn 2374375). This example dataset includes
RNA editing levels for 272 edited sites mapped to genes
PHACTR4, CCR5, METTL7A (using hg19
reference), along with a few randomly sampled sites, for 221
subjects.
## BRCA-Tumor-TCGA-AC-A2FF BRCA-Normal-TCGA-AC-A2FF
## chr1:28661572 0.00000000 0.0000000
## chr1:28661576 0.64285714 0.6307692
## chr1:28661578 0.01923077 0.0000000
## BRCA-Normal-TCGA-BH-A0AU
## chr1:28661572 0.0000000
## chr1:28661576 0.5333333
## chr1:28661578 0.0000000
In order to link the RNA editing dataset with phenotype data, the
phenotype dataset needs to have a column called sample,
whose values must be a exact match to the column names in the above RNA
editing dataset.
pheno_df <- readRDS(
system.file(
"extdata",
"pheno_df.RDS",
package = 'rnaEditr',
mustWork = TRUE
)
)
## submitter_id sample sample_type
## 1 TCGA-AC-A2FF BRCA-Tumor-TCGA-AC-A2FF Primary Tumor
## 2 TCGA-AC-A2FF BRCA-Normal-TCGA-AC-A2FF Solid Tissue Normal
## 3 TCGA-BH-A0AU BRCA-Normal-TCGA-BH-A0AU Solid Tissue Normal
Here we are using the command below to make sure sample
variable in phenotype dataset has a exact match to the column names in
the RNA editing dataset.
identical(pheno_df$sample, colnames(rnaedit_df))
## [1] TRUE
rnaEditr can perform both site-specific and region-based
analysis. In Section 3, we illustrate testing associations between
cancer status and RNA editing levels at individual sites. In Section 4,
we illustrate identifying cancer associated co-edited regions.
3 Site-specific analysis
A quick workflow for site-specific
analysis
3.1 Testing all edited sites
Before testing the associations, we use function
CreateEditingTable() to turn RNA editing matrix into a
dataframe with special class rnaEdit_df, which is a
required format of input dataset for function
TestAssociations().
rnaedit2_df <- CreateEditingTable(
rnaEditMatrix = rnaedit_df
)
In this example, we will test the association between cancer status (
sample_type variable from pheno_df dataset)
and all edited sites. Because the outcome variable “cancer status” is a
binary variable, rnaEditr will apply a logistic regression
model for each site: \[logit(Pr(cancer\;status = “yes”)) \sim
RNA\;editing\;level\]
First, we use the command below to check the distribution of variable
sample_type. Please note that rnaEditr will
fit firth corrected logistic regression instead of regular logistic
regression models for binary outcomes, when the minimum sample size per
group is less than 5.
table(pheno_df$sample_type)
##
## Primary Tumor Solid Tissue Normal
## 182 39
tumor_single_df <- TestAssociations(
# an RNA editing dataframe with special class "rnaEdit_df" from function
# CreateEditingTable() if site-specific analysis, from function
# SummarizeAllRegions() if region-based analysis.
rnaEdit_df = rnaedit2_df,
# a phenotype dataset that must have variable "sample" whose values are a
# exact match to the colnames of "rnaEdit_df".
pheno_df = pheno_df,
# name of outcome variable in phenotype dataset "pheno_df" that you want to
# test.
responses_char = "sample_type",
# names of covariate variables in phenotype dataset "pheno_df" that you want
# to add into the model.
covariates_char = NULL,
# type of outcome variable that you input in argument "responses_char".
respType = "binary",
# order the final results by p-values or not.
orderByPval = TRUE
)
## seqnames start end width estimate stdErr pValue
## 225 chr12 51324639 51324639 1 -19.66360 3.158016 4.767627e-10
## 42 chr1 28662199 28662199 1 -14.83609 2.399372 6.276575e-10
## 178 chr12 51324163 51324163 1 -42.69833 7.221196 3.361021e-09
## fdr
## 225 8.536142e-08
## 42 8.536142e-08
## 178 2.978449e-07
Here tumor_single_df is a data frame of all edited
sites, with corresponding p-values and false discovery rate (FDRs) from
the logistic regression model.
3.2 Annotate results
We next annotate these results by adding gene names mapped to the RNA
editing sites.
tumor_annot_df <- AnnotateResults(
# the output dataset from function TestAssociations().
results_df = tumor_single_df,
# close-by regions, since this is site-specific analysis, set to NULL.
closeByRegions_gr = NULL,
# input regions, since this is site-specific analysis, set to NULL.
inputRegions_gr = NULL,
genome = "hg19",
# the type of analysis result from function TestAssociations(), since we are
# running site-specific analysis, set to "site-specific".
analysis = "site-specific"
)
## seqnames start end width symbol estimate stdErr pValue
## 1 chr12 51324639 51324639 1 METTL7A -19.66360 3.158016 4.767627e-10
## 2 chr1 28662199 28662199 1 MED18 -14.83609 2.399372 6.276575e-10
## 3 chr12 51324163 51324163 1 METTL7A -42.69833 7.221196 3.361021e-09
## fdr
## 1 8.536142e-08
## 2 8.536142e-08
## 3 2.978449e-07
4 Region-based analysis
A quick workflow for region-based analysis
The region-based analysis consists of several steps: (1) make GRanges
object for the genomic regions, (2) determine RNA editing sites that are
located closely within the genomic regions, (3) identify co-edited
regions, and (4) test the association between cancer status and
co-edited regions.
4.2 Find close-by regions
Next, within each input candidate region, we identify the sub-regions
that contain closely located RNA editing sites. At default, we identify
sub-regions with at least 3 edited sites, for which maximum distance
between two neighboring sites is 50 bp.
closeByRegions_gr <- AllCloseByRegions(
# a GRanges object of genomic regions retrieved or created in section 4.1.
regions_gr = inputGenes_gr,
# an RNA editing matrix.
rnaEditMatrix = rnaedit_df,
maxGap = 50,
minSites = 3
)
## GRanges object with 8 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 28823903-28823966 *
## [2] chr1 28824256-28824490 *
## [3] chr1 28825317-28825530 *
## [4] chr1 28825670-28825927 *
## [5] chr1 28826015-28826284 *
## [6] chr3 46417171-46417221 *
## [7] chr12 51324081-51324639 *
## [8] chr12 51325229-51325622 *
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengths
closeByRegions_gr is a GRanges object that includes
sub-regions that contain closely located RNA editing sites (close-by
regions). In this example, within the three input candidate regions, 8
close-by regions were found.
4.3 Find co-edited regions
Next, within each close-by region, we identify co-edited regions
based on RNA editing levels. At default, a co-edited region needs to
satisfy the following requirements: (1) with at least 3 edited sites;
(2) the minimum correlation between RNA editing levels of one site and
the mean RNA editing levels of the rest of the sites is at least 0.4;
(3) the minimum pairwise correlation of sites within the selected
cluster is at least 0.1.
closeByCoeditedRegions_gr <- AllCoeditedRegions(
# a GRanges object of close-by regions created by AllCloseByRegions().
regions_gr = closeByRegions_gr,
# an RNA editing matrix.
rnaEditMatrix = rnaedit_df,
# type of output data.
output = "GRanges",
rDropThresh_num = 0.4,
minPairCorr = 0.1,
minSites = 3,
# the method for computing correlations.
method = "spearman",
# When no co-edited regions are found in an input genomic region, you want to
# output the whole region (when set to TRUE) or NULL (when set to FALSE).
returnAllSites = FALSE
)
closeByCoeditedRegions_gr
## GRanges object with 14 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 28825317-28825370 *
## [2] chr1 28825394-28825403 *
## [3] chr1 28825670-28825717 *
## [4] chr1 28825899-28825927 *
## [5] chr1 28826150-28826173 *
## ... ... ... ...
## [10] chr12 51324308-51324436 *
## [11] chr12 51324447-51324467 *
## [12] chr12 51324545-51324585 *
## [13] chr12 51324604-51324627 *
## [14] chr12 51325324-51325383 *
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengths
closeByCoeditedRegions_gr is a GRanges that contains
co-edited regions within the close-by regions we found from last step.
We identified 14 co-edited regions from the 8 close-by regions.
Let’s take a look at correlations of RNA editing levels within the
first co-edited region:
PlotEditingCorrelations(
region_gr = closeByCoeditedRegions_gr[1],
rnaEditMatrix = rnaedit_df
)
4.4 Summarize all regions
Next, we summarize RNA editing levels from multiple edited sites
within each co-edited region using medians.
summarizedRegions_df <- SummarizeAllRegions(
# a GRanges object of close-by regions created by AllCoeditedRegions().
regions_gr = closeByCoeditedRegions_gr,
# an RNA editing matrix.
rnaEditMatrix = rnaedit_df,
# available methods: "MaxSites", "MeanSites", "MedianSites", and "PC1Sites".
selectMethod = MedianSites
)
summarizedRegions_df[1:3, 1:5]
## seqnames start end width BRCA-Tumor-TCGA-AC-A2FF
## 1 chr1 28825317 28825370 54 0.10618417
## 2 chr1 28825394 28825403 10 0.00000000
## 3 chr1 28825670 28825717 48 0.08602151
summarizedRegions_df is a data frame with each row
corresponding to one co-edited region identified from function
AllCoeditedRegions(). Since there are 14 co-edited regions,
this data frame has 14 rows. In column 5 to the end, each column
includes median editing levels (over all sites within each co-edited
region) for one sample.
4.5 Test all regions
Next, we test the association between cancer status and co-edited
regions using logistic regression model: \[logit (Pr(sample\;type = “cancer”)) \sim
median\;RNA\;editing\;levels\]
tumor_region_df <- TestAssociations(
# an RNA editing dataframe with special class "rnaEdit_df" from function
# CreateEditingTable() if site-specific analysis, from function
# SummarizeAllRegions() if region-based analysis.
rnaEdit_df = summarizedRegions_df,
# a phenotype dataset that must have variable "sample" whose values are a
# exact match to the colnames of "rnaEdit_df".
pheno_df = pheno_df,
# name of outcome variable in phenotype dataset "pheno_df" that you want to
# test.
responses_char = "sample_type",
# names of covariate variables in phenotype dataset "pheno_df" that you want
# to add into the model.
covariates_char = NULL,
# type of outcome variable that you input in argument "responses_char".
respType = "binary",
# order the final results by p-values or not.
orderByPval = TRUE
)
## seqnames start end width estimate stdErr pValue
## 8 chr12 51324085 51324127 43 -51.23815 8.736146 4.489288e-09
## 14 chr12 51325324 51325383 60 -94.78676 16.738315 1.488692e-08
## 11 chr12 51324447 51324467 21 -102.71801 18.271132 1.888967e-08
## fdr
## 8 6.285003e-08
## 14 8.815177e-08
## 11 8.815177e-08
Here tumor_region_df contains results for testing each
co-edited region using logistic regression model.
4.6 Annotate results
We next annotate results by adding the corresponding input regions,
close-by regions and genes mapped to these genomic regions.
tumor_annot_df <- AnnotateResults(
# the output dataset from function TestAssociations().
results_df = tumor_region_df,
# close-by regions which is a output of AllCloseByRegions().
closeByRegions_gr = closeByRegions_gr,
# input regions, which are created in section 4.1.
inputRegions_gr = inputGenes_gr,
genome = "hg19",
# the type of analysis result from function TestAssociations(), since we are
# doing region-based analysis, use default here.
analysis = "region-based"
)
## seqnames start end width inputRegion
## 1 chr12 51324085 51324127 43 chr12:51317255-51326300
## 2 chr12 51325324 51325383 60 chr12:51317255-51326300
## 3 chr12 51324447 51324467 21 chr12:51317255-51326300
## closeByRegion symbol estimate stdErr pValue
## 1 chr12:51324081-51324639 METTL7A -51.23815 8.736146 4.489288e-09
## 2 chr12:51325229-51325622 METTL7A -94.78676 16.738315 1.488692e-08
## 3 chr12:51324081-51324639 METTL7A -102.71801 18.271132 1.888967e-08
## fdr
## 1 6.285003e-08
## 2 8.815177e-08
## 3 8.815177e-08
To summarize this final dataset, values of column
inputRegion are the user-specified candidate genomic
regions, values of column closeByRegion are the regions
that contain closely located RNA editing sites, and columns
seqnames, start, end are the
co-edited regions.
5 Further examples of function TestAssociations in
rnaEditr
rnaEditr can analyze different types of phenotypes,
including binary, continuous and survival outcomes. In the last section,
we analyzed a binary phenotype sample_type. We next
illustrate analysis for continuous and survival phenotypes.
For continuous outcome, as an example, to identify co-edited regions
associated age, we use the following commands:
tumor_region_df <- TestAssociations(
# an RNA editing dataframe with special class "rnaEdit_df" from function
# CreateEditingTable() if site-specific analysis, from function
# SummarizeAllRegions() if region-based analysis.
rnaEdit_df = summarizedRegions_df,
# a phenotype dataset that must have variable "sample" whose values are a
# exact match to the colnames of "rnaEdit_df".
pheno_df = pheno_df,
# name of outcome variable in phenotype dataset "pheno_df" that you want to
# test.
responses_char = "age_at_diagnosis",
# names of covariate variables in phenotype dataset "pheno_df" that you want
# to add into the model.
covariates_char = NULL,
# type of outcome variable that you input in argument "responses_char".
respType = "continuous",
# order the final results by p-values or not.
orderByPval = TRUE
)
## seqnames start end width estimate stdErr pValue fdr
## 7 chr3 46417171 46417221 51 -29.56179 12.64776 0.02032533 0.2845546
## 2 chr1 28825394 28825403 10 -72.25518 35.67815 0.04406090 0.3084263
## 11 chr12 51324447 51324467 21 54.28664 29.48137 0.06691720 0.3122803
For survival outcome, for example, we use the following commands:
tumor_region_df <- TestAssociations(
# an RNA editing dataframe with special class "rnaEdit_df" from function
# CreateEditingTable() if site-specific analysis, from function
# SummarizeAllRegions() if region-based analysis.
rnaEdit_df = summarizedRegions_df,
# a phenotype dataset that must have variable "sample" whose values are a
# exact match to the colnames of "rnaEdit_df".
pheno_df = pheno_df,
# name of outcome variable in phenotype dataset "pheno_df" that you want to
# test.
responses_char = c("OS.time", "OS"),
# names of covariate variables in phenotype dataset "pheno_df" that you want
# to add into the model.
covariates_char = NULL,
# type of outcome variable that you input in argument "responses_char".
respType = "survival",
# order the final results by p-values or not.
orderByPval = TRUE
)
## seqnames start end width coef exp_coef se_coef
## 3 chr1 28825670 28825717 48 -13.64262 1.188740e-06 3.485844
## 14 chr12 51325324 51325383 60 -21.61839 4.085568e-10 5.753652
## 4 chr1 28825899 28825927 29 -24.57176 2.131191e-11 7.063456
## pValue fdr
## 3 9.088515e-05 0.001202134
## 14 1.717334e-04 0.001202134
## 4 5.038231e-04 0.002111975