Package 'sesame'

Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips
Description: Tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines.
Authors: Wanding Zhou [aut, cre] , Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut]
Maintainer: Wanding Zhou <[email protected]>
License: MIT + file LICENSE
Version: 1.25.0
Built: 2024-11-01 06:29:25 UTC
Source: https://github.com/bioc/sesame

Help Index

Analyze DNA methylation data

Description

SEnsible and step-wise analysis of DNA methylation data

Details

This package complements array functionalities that allow processing >10,000 samples in parallel on clusters.

Value

package

Author(s)

Wanding Zhou [email protected], Hui Shen [email protected] Timothy J Triche Jr [email protected]

References

Zhou W, Triche TJ, Laird PW, Shen H (2018)

See Also

Useful links:

Examples

sdf <- readIDATpair(sub('_Grn.idat','',system.file(
    'extdata','4207113116_A_Grn.idat',package='sesameData')))

## The OpenSesame pipeline
betas <- openSesame(sdf)

Add probes to mask

Description

This function essentially merge existing probe masking with new probes to mask

Usage

addMask(sdf, probes)

Arguments

sdf

a SigDF
probes

a vector of probe IDs or a logical vector with TRUE representing masked probes

Value

a SigDF with added mask

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
sum(sdf$mask)
sum(addMask(sdf, c("cg14057072", "cg22344912"))$mask)

Aggregate test enrichment results

Description

Aggregate test enrichment results

Usage

aggregateTestEnrichments(result_list, column = "estimate", return_df = FALSE)

Arguments

result_list

a list of results from testEnrichment
column

the column name to aggregate (Default: estimate)
return_df

whether to return a merged data frame

Value

a matrix for all results

Examples

## pick some big TFBS-overlapping CpG groups
cg_lists <- KYCG_getDBs("MM285.TFBS")
queries <- cg_lists[(sapply(cg_lists, length) > 40000)]
result_list <- lapply(queries, testEnrichment, "MM285.chromHMM")
mtx <- aggregateTestEnrichments(result_list)

assemble plots

Description

assemble plots

Usage

assemble_plots(
  betas,
  txns,
  probes,
  plt.txns,
  plt.mapLines,
  plt.cytoband,
  heat.height = NULL,
  mapLine.height = 0.2,
  show.probeNames = TRUE,
  show.samples.n = NULL,
  show.sampleNames = TRUE,
  sample.name.fontsize = 10,
  dmin = 0,
  dmax = 1
)

Arguments

betas

beta value
txns

transcripts GRanges
probes

probe GRanges
plt.txns

transcripts plot objects
plt.mapLines

map line plot objects
plt.cytoband

cytoband plot objects
heat.height

heatmap height (auto inferred based on rows)
mapLine.height

height of the map lines
show.probeNames

whether to show probe names
show.samples.n

number of samples to show (default: all)
show.sampleNames

whether to show sample names
sample.name.fontsize

sample name font size
dmin

data min
dmax

data max

Value

a grid object

Collapse betas by averagng probes with common probe ID prefix

Description

Collapse betas by averagng probes with common probe ID prefix

Usage

betasCollapseToPfx(betas, BPPARAM = SerialParam())

Arguments

betas

either a named numeric vector or a numeric matrix (row: probes, column: samples)
BPPARAM

use MulticoreParam(n) for parallel processing

Value

either named numeric vector or a numeric matrix of collapsed beta value matrix

Examples

## input is a matrix
m <- matrix(seq(0,1,length.out=9), nrow=3)
rownames(m) <- c("cg00004963_TC21", "cg00004963_TC22", "cg00004747_TC21")
colnames(m) <- c("A","B","C")
betasCollapseToPfx(m)

## input is a vector
m <- setNames(seq(0,1,length.out=3),
    c("cg00004963_TC21", "cg00004963_TC22", "cg00004747_TC21"))
betasCollapseToPfx(m)

Convert beta-value to M-value

Description

Logit transform a beta value vector to M-value vector.

Usage

BetaValueToMValue(b)

Arguments

b

vector of beta values

Details

Convert beta-value to M-value (aka logit transform)

Value

a vector of M values

Examples

BetaValueToMValue(c(0.1, 0.5, 0.9))

Bin signals from probe signals

Description

require GenomicRanges

Usage

binSignals(probe.signals, bin.coords, probeCoords)

Arguments

probe.signals

probe signals
bin.coords

bin coordinates
probeCoords

probe coordinates

Value

bin signals

Compute internal bisulfite conversion control

Description

Compute GCT score for internal bisulfite conversion control. The function takes a SigSet as input. The higher the GCT score, the more likely the incomplete conversion.

Usage

bisConversionControl(sdf, extR = NULL, extA = NULL, verbose = FALSE)

Arguments

sdf

a SigDF
extR

a vector of probe IDs for Infinium-I probes that extend to converted A
extA

a vector of probe IDs for Infinium-I probes that extend to original A
verbose

print more messages

Value

GCT score (the higher, the more incomplete conversion)

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
bisConversionControl(sdf)

## For more recent platforms like EPICv2, MSA:
## One need extR and extA of other arrays using the sesameAnno
## Not run: 
mft = sesameAnno_buildManifestGRanges(sprintf(
  "%s/EPICv2/EPICv2.hg38.manifest.tsv.gz",
  "https://github.com/zhou-lab/InfiniumAnnotationV1/raw/main/Anno/"),
  columns="nextBase")
extR = names(mft)[!is.na(mft$nextBase) & mft$nextBase=="R"]
extA = names(mft)[!is.na(mft$nextBase) & mft$nextBase=="A"]

## End(Not run)

Compute effect size for different variables from prediction matrix

Description

The effect size is defined by the maximum variation of a variable with all the other variables controled constant.

Usage

calcEffectSize(pred)

Arguments

pred

predictions

Value

a data.frame of effect sizes. Columns are different variables. Rows are different probes.

Examples

data <- sesameDataGet('HM450.76.TCGA.matched')
res <- DMLpredict(data$betas[1:10,], ~type, meta=data$sampleInfo)
head(calcEffectSize(res))

filter data matrix by factor completeness only works for discrete factors

Description

filter data matrix by factor completeness only works for discrete factors

Usage

checkLevels(betas, fc)

Arguments

betas

matrix data
fc

factors, or characters

Value

a boolean vector whether there is non-NA value for each tested group for each probe

Examples

se0 <- sesameDataGet("MM285.10.SE.tissue")[1:100,]
se_ok <- checkLevels(SummarizedExperiment::assay(se0),
    SummarizedExperiment::colData(se0)$tissue)
sum(se_ok) # number of good probes
se1 <- se0[se_ok,]

sesameDataGet_resetEnv()

Lookup address in one sample

Description

Lookup address and transform address to probe

Usage

chipAddressToSignal(dm, mft, min_beads = NULL)

Arguments

dm

data frame in chip address, 2 columns: cy3/Grn and cy5/Red
mft

a data frame with columns Probe_ID, M, U and col
min_beads

minimum bead counts, otherwise masked

Details

Translate data in chip address to probe address. Type I probes can be separated into Red and Grn channels. The methylated allele and unmethylated allele are at different addresses. For type II probes methylation allele and unmethylated allele are at the same address. Grn channel is for methylated allele and Red channel is for unmethylated allele. The out-of-band signals are type I probes measured using the other channel.

Value

a SigDF, indexed by probe ID address

Perform copy number segmentation

Description

Perform copy number segmentation using the signals in the signal set. The function takes a SigDF for the target sample and a set of normal SigDF for the normal samples. An optional arguments specifies the version of genome build that the inference will operate on. The function outputs an object of class CNSegment with signals for the segments ( seg.signals), the bin coordinates ( bin.coords) and bin signals (bin.signals).

Usage

cnSegmentation(
  sdf,
  sdfs.normal = NULL,
  genomeInfo = NULL,
  probeCoords = NULL,
  tilewidth = 50000,
  verbose = FALSE,
  return.probe.signals = FALSE
)

Arguments

sdf

SigDF
sdfs.normal

a list of SigDFs for normalization, if not given, use the stored normal data from sesameData. However, we do recommend using a matched copy number normal dataset for normalization. assembly
genomeInfo

the genomeInfo files. The default is retrieved from sesameData. Alternative genomeInfo files can be found at https://github.com/zhou-lab/GenomeInfo
probeCoords

the probe coordinates in the corresponding genome if NULL (default), then the default genome assembly is used. Default genome is given by, e.g., sesameData_check_genome(NULL, "EPIC") For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., probeCoords = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.
tilewidth

tile width for smoothing
verbose

print more messages
return.probe.signals

return probe-level instead of bin-level signal

Value

an object of CNSegment

Examples

sesameDataCache()

## Not run: 
sdfs <- sesameDataGet('EPICv2.8.SigDF')
sdf <- sdfs[["K562_206909630040_R01C01"]]
seg <- cnSegmentation(sdf)
seg <- cnSegmentation(sdf, return.probe.signals=TRUE)
visualizeSegments(seg)

## End(Not run)

calculates the pariwise overlap between given list of database sets using a distance metric.

Description

calculates the pariwise overlap between given list of database sets using a distance metric.

Usage

compareDatbaseSetOverlap(databases = NA, metric = "Jaccard")

Arguments

databases

List of vectors corresponding to the database sets of interest with associated meta data as an attribute to each element. Optional. (Default: NA)
metric

String representing the similarity metric to use. Optional. (Default: "Jaccard").

Value

An upper triangular matrix containing a metric (Jaccard) comparing the pairwise distances between database sets.

Compare Strain SNPs with a reference panel

Description

Compare Strain SNPs with a reference panel

Usage

compareMouseStrainReference(
  betas = NULL,
  show_sample_names = FALSE,
  query_width = NULL
)

Arguments

betas

beta value vector or matrix (for multiple samples)
show_sample_names

whether to show sample name
query_width

optional argument for adjusting query width

Value

grid object that contrast the target sample with pre-built mouse strain reference

Examples

sesameDataCache() # if not done yet
compareMouseStrainReference()

Compare mouse array data with mouse tissue references

Description

Compare mouse array data with mouse tissue references

Usage

compareMouseTissueReference(
  betas = NULL,
  ref = NULL,
  color = "blueYellow",
  query_width = 0.3
)

Arguments

betas

matrix of betas for the target sample This argument is optional. If not given, only the reference will be shown.
ref

the reference beta values in SummarizedExperiment. This argument is optional. If not given, the reference will be downloaded from the sesameData package.
color

either blueYellow or fullJet
query_width

the width of the query beta value matrix

Value

grid object that contrast the target sample with pre-built mouse tissue reference

Examples

cat("Deprecated, see compareReference")

Compare array data with references (e.g., tissue, cell types)

Description

Compare array data with references (e.g., tissue, cell types)

Usage

compareReference(
  ref,
  betas = NULL,
  stop.points = NULL,
  query_width = 0.3,
  show_sample_names = FALSE
)

Arguments

ref

the reference beta values in SummarizedExperiment. One can download them from the sesameData package. See examples.
betas

matrix of betas for the target sample This argument is optional. If not given, only the reference will be shown.
stop.points

stop points for the color palette. Default to blue, yellow.
query_width

the width of the query beta value matrix
show_sample_names

whether to show sample names (default: FALSE)

Value

grid object that contrast the target sample with references.

Examples

sesameDataCache() # if not done yet
compareReference(sesameDataGet("MM285.tissueSignature"))
sesameDataGet_resetEnv()

get the controls attributes

Description

get the controls attributes

Usage

controls(sdf, verbose = FALSE)

Arguments

sdf

a SigDF
verbose

print more messages

Value

the controls data frame

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
head(controls(sdf))

Convert Probe ID

Description

Convert Probe ID

Usage

convertProbeID(
  x,
  target_platform,
  source_platform = NULL,
  mapping = NULL,
  target_uniq = TRUE,
  include_new = FALSE,
  include_old = FALSE,
  return_mapping = FALSE
)

Arguments

x

source probe IDs
target_platform

the platform to take the data to
source_platform

optional source platform
mapping

a liftOver mapping file. Typically this file contains empirical evidence whether a probe mapping is reliable. If given, probe ID-based mapping will be skipped. This is to perform more stringent probe ID mapping.
target_uniq

whether the target Probe ID should be kept unique.
include_new

if true, include mapping of added probes
include_old

if true, include mapping of deleted probes
return_mapping

return mapping table, instead of the target IDs.

Value

mapped probe IDs, or mapping table if return_mapping = T

createGeneNetwork creates database network using the Jaccard index.

Description

createGeneNetwork creates database network using the Jaccard index.

Usage

createDBNetwork(databases)

Arguments

databases

Vector of probes corresponding to a single database set of interest.

Value

ggplot lollipop plot

Turn beta values into a UCSC browser track

Description

Turn beta values into a UCSC browser track

Usage

createUCSCtrack(betas, output = NULL, platform = "HM450", genome = "hg38")

Arguments

betas

a named numeric vector
output

output file name
platform

HM450, EPIC etc.
genome

hg38, mm10, ..., will infer if not given. For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., genome = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.

Value

when output is null, return a data.frame, otherwise NULL

Examples

betas.tissue <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
## add output to create an actual file
df <- createUCSCtrack(betas.tissue)

## to convert to bigBed
## sort -k1,1 -k2,2n output.bed >output_sorted.bed
## bedToBigBed output_sorted.bed hg38.chrom output.bb

Convert data frame to sesameQC object

Description

The function convert a data frame back to a list of sesameQC objects

Usage

dataFrame2sesameQC(df)

Arguments

df

a publicQC data frame

Value

a list sesameQC objects

dbStats builds dataset for a given betas matrix composed of engineered features from the given database sets

Description

dbStats builds dataset for a given betas matrix composed of engineered features from the given database sets

Usage

dbStats(
  betas,
  databases,
  fun = mean,
  na.rm = TRUE,
  n_min = NULL,
  f_min = 0.1,
  long = FALSE
)

Arguments

betas

matrix of beta values where probes are on the rows and samples are on the columns
databases

List of vectors corresponding to probe locations for which the features will be extracted
fun

aggregation function, default to mean
na.rm

whether to remove NA
n_min

min number of non-NA for aggregation function to apply, overrides f_min
f_min

min fraction of non-NA for aggregation function to apply
long

produce long-form result

Value

matrix with samples on the rows and database set on the columns

Examples

library(SummarizedExperiment)
se <- sesameDataGet('MM285.467.SE.tissue20Kprobes')
head(dbStats(assay(se), "MM285.chromHMM")[,1:3])
sesameDataGet_resetEnv()

De-identify IDATs by removing SNP probes

Description

Mask SNP probe intensity mean by zero.

Usage

deIdentify(path, out_path = NULL, snps = NULL, mft = NULL, randomize = FALSE)

Arguments

path

input IDAT file
out_path

output IDAT file
snps

SNP definition, if not given, default to SNP probes
mft

sesame-compatible manifest if non-standard
randomize

whether to randomize the SNPs. if TRUE, randomize the signal intensities. one can use set.seed to reidentify the IDAT with the secret seed (see examples). If FALSE, this sets all SNP intensities to zero.

Value

NULL, changes made to the IDAT files

Examples

my_secret <- 13412084
set.seed(my_secret)
temp_out <- tempfile("test")
deIdentify(system.file(
    "extdata", "4207113116_A_Grn.idat", package = "sesameData"),
     temp_out, randomize = TRUE)
unlink(temp_out)

Detection P-value based on ECDF of negative control

Description

The function takes a SigDF as input, computes detection p-value using negative control probes' empirical distribution and returns a new SigDF with an updated mask slot.

Usage

detectionPnegEcdf(sdf, return.pval = FALSE, pval.threshold = 0.05)

Arguments

sdf

a SigDF
return.pval

whether to return p-values, instead of a masked SigDF
pval.threshold

minimum p-value to mask

Value

a SigDF, or a p-value vector if return.pval is TRUE

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
sum(sdf$mask)
sum(detectionPnegEcdf(sdf)$mask)

Restrict refset to differentially methylated probes use with care, might introduce bias

Description

The function takes a matrix with probes on the rows and cell types on the columns and output a subset matrix and only probes that show discordant methylation levels among the cell types.

Usage

diffRefSet(g)

Arguments

g

a matrix with probes on the rows and cell types on the columns

Value

g a matrix with a subset of input probes (rows)

Examples

g = diffRefSet(getRefSet(platform='HM450'))
sesameDataGet_resetEnv()

List all contrasts of a DMLSummary

Description

List all contrasts of a DMLSummary

Usage

dmContrasts(smry)

Arguments

smry

a DMLSummary object

Value

a character vector of contrasts

Examples

data <- sesameDataGet('HM450.76.TCGA.matched')
smry <- DML(data$betas[1:10,], ~type, meta=data$sampleInfo)
dmContrasts(smry)

sesameDataGet_resetEnv()

Test differential methylation on each locus

Description

The function takes a beta value matrix with probes on the rows and samples on the columns. It also takes a sample information data frame (meta) and formula for testing. The function outputs a list of coefficient tables for each factor tested.

Usage

DML(betas, fm, meta = NULL, BPPARAM = SerialParam())

Arguments

betas

beta values, matrix or SummarizedExperiment rows are probes and columns are samples.
fm

formula
meta

data frame for sample information, column names are predictor variables (e.g., sex, age, treatment, tumor/normal etc) and are referenced in formula. Rows are samples. When the betas argument is a SummarizedExperiment object, this is ignored. colData(betas) will be used instead. The row order of the data frame must match the column order of the beta value matrix.
BPPARAM

number of cores for parallel processing, default to SerialParam() Use MulticoreParam(mc.cores) for parallel processing. For Windows, try DoparParam or SnowParam.

Value

a list of test summaries, summary.lm objects

Examples

sesameDataCache() # in case not done yet
data <- sesameDataGet('HM450.76.TCGA.matched')
smry <- DML(data$betas[1:1000,], ~type, meta=data$sampleInfo)

sesameDataGet_resetEnv()

Predict new data from DML

Description

This function is also important for investigating factor interactions.

Usage

DMLpredict(betas, fm, pred = NULL, meta = NULL, BPPARAM = SerialParam())

Arguments

betas

beta values, matrix or SummarizedExperiment rows are probes and columns are samples.
fm

formula
pred

new data for prediction, useful for studying effect size. This argument is a data.frame to specify new data. If the argument is NULL, all combinations of all contrasts will be used as input. It might not work if there is a continuous variable input. One may need to explicitly provide the input in a data frame.
meta

data frame for sample information, column names are predictor variables (e.g., sex, age, treatment, tumor/normal etc) and are referenced in formula. Rows are samples. When the betas argument is a SummarizedExperiment object, this is ignored. colData(betas) will be used instead.
BPPARAM

number of cores for parallel processing, default to SerialParam() Use MulticoreParam(mc.cores) for parallel processing. For Windows, try DoparParam or SnowParam.

Value

a SummarizedExperiment of predictions. The colData describes the input of the prediction.

Examples

data <- sesameDataGet('HM450.76.TCGA.matched')

## use all contrasts as new input
res <- DMLpredict(data$betas[1:10,], ~type, meta=data$sampleInfo)

## specify new input
res <- DMLpredict(data$betas[1:10,], ~type, meta=data$sampleInfo,
  pred = data.frame(type=c("Normal","Tumour")))

## note that the prediction needs to be a factor of the same
## level structure as the original training data.
pred = data.frame(type=factor(c("Normal"), levels=c("Normal","Tumour")))
res <- DMLpredict(data$betas[1:10,], ~type,
  meta=data$sampleInfo, pred = pred)

Find Differentially Methylated Region (DMR)

Description

This subroutine uses Euclidean distance to group CpGs and then combine p-values for each segment. The function performs DML test first if cf is NULL. It groups the probe testing results into differential methylated regions in a coefficient table with additional columns designating the segment ID and statistical significance (P-value) testing the segment.

Usage

DMR(
  betas,
  smry,
  contrast,
  platform = NULL,
  probe.coords = NULL,
  dist.cutoff = NULL,
  seg.per.locus = 0.5
)

Arguments

betas

beta values for distance calculation
smry

DML
contrast

the pair-wise comparison or contrast check colnames(attr(smry, "model.matrix")) if uncertain
platform

EPIC, HM450, MM285, ...
probe.coords

GRanges object that defines CG coordinates if NULL (default), then the default genome assembly is used. Default genome is given by, e.g., sesameData_check_genome(NULL, "EPIC") For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., probe.coords = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.
dist.cutoff

cutoff of beta value differences for two neighboring CGs to be considered the same DMR (by default it's determined using the quantile function on seg.per.locus)
seg.per.locus

number of segments per locus higher value leads to more segments

Value

coefficient table with segment ID and segment P-value each row is a locus, multiple loci may share a segment ID if they are merged to the same segment. Records are ordered by Seg_Est.

Examples

sesameDataCache() # in case not done yet

data <- sesameDataGet('HM450.76.TCGA.matched')
smry <- DML(data$betas[1:1000,], ~type, meta=data$sampleInfo)
colnames(attr(smry, "model.matrix")) # pick a contrast from here
## showing on a small set of 100 CGs
merged_segs <- DMR(data$betas[1:1000,], smry, "typeTumour", platform="HM450")

sesameDataGet_resetEnv()

Correct dye bias in by linear scaling.

Description

The function takes a SigDF as input and scale both the Grn and Red signal to a reference (ref) level. If the reference level is not given, it is set to the mean intensity of all the in-band signals. The function returns a SigDF with dye bias corrected.

Usage

dyeBiasCorr(sdf, ref = NULL)

Arguments

sdf

a SigDF
ref

reference signal level

Value

a normalized SigDF

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.db <- dyeBiasCorr(sdf)

Correct dye bias using most balanced sample as the reference

Description

The function chose the reference signal level from a list of SigDF. The chosen sample has the smallest difference in Grn and Red signal intensity as measured using the normalization control probes. In practice, it doesn't matter which sample is chosen as long as the reference level does not deviate much. The function returns a list of SigDFs with dye bias corrected.

Usage

dyeBiasCorrMostBalanced(sdfs)

Arguments

sdfs

a list of normalized SigDFs

Value

a list of normalized SigDFs

Examples

sesameDataCache() # if not done yet
sdfs <- sesameDataGet('HM450.10.SigDF')[1:2]
sdfs.db <- dyeBiasCorrMostBalanced(sdfs)

Correct dye bias in by linear scaling.

Description

The function takes a SigDF as input and scale both the Grn and Red signal to a reference (ref) level. If the reference level is not given, it is set to the mean intensity of all the in-band signals. The function returns a SigDF with dye bias corrected.

Usage

dyeBiasL(sdf, ref = NULL)

Arguments

sdf

a SigDF
ref

reference signal level

Value

a normalized SigDF

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.db <- dyeBiasL(sdf)

Dye bias correction by matching green and red to mid point

Description

This function compares the Type-I Red probes and Type-I Grn probes and generates and mapping to correct signal of the two channels to the middle. The function takes one single SigDF and returns a SigDF with dye bias corrected.

Usage

dyeBiasNL(sdf, mask = TRUE, verbose = FALSE)

dyeBiasCorrTypeINorm(sdf, mask = TRUE, verbose = FALSE)

Arguments

sdf

a SigDF
mask

include masked probes in Infinium-I probes. No big difference is noted in practice. More probes are generally better.
verbose

print more messages

Value

a SigDF after dye bias correction.

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.db <- dyeBiasNL(sdf)
sdf <- sesameDataGet('EPIC.1.SigDF')
sdf <- dyeBiasCorrTypeINorm(sdf)

ELiminate BAckground-dominated Reading (ELBAR)

Description

ELiminate BAckground-dominated Reading (ELBAR)

Usage

ELBAR(
  sdf,
  return.pval = FALSE,
  pval.threshold = 0.05,
  margin = 0.05,
  capMU = 3000,
  delta.beta = 0.2,
  n.windows = 500
)

Arguments

sdf

a SigDF
return.pval

whether to return p-values, instead of a SigDF
pval.threshold

minimum p-value to mask
margin

the percentile margin to define envelope, the smaller the value the more aggressive the masking.
capMU

the maximum M+U to search for intermediate betas
delta.beta

maximum beta value change from sheer background-dominated readings
n.windows

number of windows for smoothing

Value

a SigDF with mask added

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
sum(sdf$mask)
sum(ELBAR(sdf)$mask)

Estimate leukocyte fraction using a two-component model

Description

The method assumes only two components in the mixture: the leukocyte component and the target tissue component. The function takes the beta values matrix of the target tissue and the beta value matrix of the leukocyte. Both matrices have probes on the row and samples on the column. Row names should have probe IDs from the platform. The function outputs a single numeric describing the fraction of leukocyte.

Usage

estimateLeukocyte(
  betas.tissue,
  betas.leuko = NULL,
  betas.tumor = NULL,
  platform = c("EPIC", "HM450", "HM27")
)

Arguments

betas.tissue

tissue beta value matrix (#probes X #samples)
betas.leuko

leukocyte beta value matrix, if missing, use the SeSAMe default by infinium platform
betas.tumor

optional, tumor beta value matrix
platform

"HM450", "HM27" or "EPIC"

Value

leukocyte estimate, a numeric vector

Examples

betas.tissue <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
estimateLeukocyte(betas.tissue)
sesameDataGet_resetEnv()

Convert SNP from Infinium array to VCF file

Description

Convert SNP from Infinium array to VCF file

Usage

formatVCF(sdf, anno, vcf = NULL, genome = "hg38", verbose = FALSE)

Arguments

sdf

SigDF
anno

SNP variant annotation, available at https://github.com/zhou-lab/InfiniumAnnotationV1/tree/main/Anno/EPIC EPIC.hg38.snp.tsv.gz
vcf

output VCF file path, if NULL output to console
genome

genome
verbose

print more messages

Value

VCF file. If vcf is NULL, a data.frame is output to console. The data.frame does not contain VCF headers. Note the output vcf is not sorted.

Examples

sesameDataCacheAll() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')

## Not run: 
## download anno from
## http://zwdzwd.github.io/InfiniumAnnotation
## output to console
anno = read_tsv(sesameAnno_download("EPICv2.hg38.snp.tsv.gz"))
head(formatVCF(sdf, anno))

## End(Not run)

Get allele frequency

Description

Get allele frequency

Usage

getAFs(sdf, ...)

Arguments

sdf

SigDF
...

additional options to getBetas

Value

allele frequency

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
af <- getAFs(sdf)

Get allele frequency treating type I by summing alleles

Description

Takes a SigDF as input and returns a numeric vector containing extra allele frequencies based on Color-Channel-Switching (CCS) probes. If no CCS probes exist in the SigDF, then an numeric(0) is returned.

Usage

getAFTypeIbySumAlleles(sdf, known.ccs.only = TRUE)

Arguments

sdf

SigDF
known.ccs.only

consider only known CCS probes

Value

beta values

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
af <- getAFTypeIbySumAlleles(sdf)

Get beta Values

Description

sum.typeI is used for rescuing beta values on Color-Channel-Switching CCS probes. The function takes a SigDF and returns beta value except that Type-I in-band signal and out-of-band signal are combined. This prevents color-channel switching due to SNPs.

Usage

getBetas(
  sdf,
  mask = TRUE,
  sum.TypeI = FALSE,
  collapseToPfx = FALSE,
  collapseMethod = c("mean", "minPval")
)

Arguments

sdf

SigDF
mask

whether to use mask
sum.TypeI

whether to sum type I channels
collapseToPfx

remove replicate to prefix (e.g., cg number) and remove the suffix
collapseMethod

mean or minPval

Value

a numeric vector, beta values

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
betas <- getBetas(sdf)

Get bin coordinates

Description

requires GenomicRanges, IRanges

Usage

getBinCoordinates(seqLength, gapInfo, tilewidth = 50000, probeCoords)

Arguments

seqLength

chromosome information object
gapInfo

chromosome gap information
tilewidth

tile width for smoothing
probeCoords

probe coordinates

Value

bin.coords

get probe masking by mask names

Description

get probe masking by mask names

Usage

getMask(platform = "EPICv2", mask_names = "recommended")

Arguments

platform

EPICv2, EPIC, HM450, HM27, ...
mask_names

mask names (see listAvailableMasks) by default: "recommended" see recommendedMaskNames() for detail.

Value

a vector of probe ID

Examples

length(getMask("MSA", "recommended"))
length(getMask("EPICv2", "recommended"))
length(getMask("EPICv2", c("recommended", "M_SNPcommon_1pt")))
length(getMask("EPICv2", "M_mapping"))
length(getMask("EPIC"))
length(getMask("HM450"))
length(getMask("MM285"))

Retrieve reference set

Description

The function retrieves the curated reference DNA methylation status for a set of cell type names under the Infinium platform. Supported cell types include "CD4T", "CD19B", "CD56NK", "CD14Monocytes", "granulocytes", "scFat", "skin" etc. See package sesameData for more details. The function output a matrix with probes on the rows and specified cell types on the columns. 0 suggests unmethylation and 1 suggests methylation. Intermediate methylation and nonclusive calls are left with NA.

Usage

getRefSet(cells = NULL, platform = c("EPIC", "HM450"))

Arguments

cells

reference cell types
platform

EPIC or HM450

Value

g, a 0/1 matrix with probes on the rows and specified cell types on the columns.

Examples

betas = getRefSet('CD4T', platform='HM450')
sesameDataGet_resetEnv()

Impute of missing data of specific platform

Description

Impute of missing data of specific platform

Usage

imputeBetas(
  betas,
  platform = NULL,
  BPPARAM = SerialParam(),
  celltype = NULL,
  sd_max = 999
)

Arguments

betas

named vector of beta values
platform

platform
BPPARAM

use MulticoreParam(n) for parallel processing
celltype

celltype/tissue context of imputation, if not given, will use nearest neighbor to determine.
sd_max

maximum standard deviation in imputation confidence

Value

imputed data, vector or matrix

Examples

betas = openSesame(sesameDataGet("EPIC.1.SigDF"))
sum(is.na(betas))
betas2 = imputeBetas(betas, "EPIC")
sum(is.na(betas2))

Impute missing data based on genomic neighbors.

Description

Impute missing data based on genomic neighbors.

Usage

imputeBetasByGenomicNeighbors(
  betas,
  platform = NULL,
  BPPARAM = SerialParam(),
  max_neighbors = 3,
  max_dist = 10000
)

Arguments

betas

named vector of beta values
platform

platform
BPPARAM

use MulticoreParam(n) for parallel processing
max_neighbors

maximum neighbors to use for dense regions
max_dist

maximum distance to count as neighbor

Value

imputed data, vector or matrix

Examples

betas = openSesame(sesameDataGet("EPICv2.8.SigDF")[[1]])
sum(is.na(betas))
betas2 = imputeBetasByGenomicNeighbors(betas, "EPICv2")
sum(is.na(betas2))

Impute Missing Values with Mean This function replaces missing values (NA) in a matrix, default is row means.

Description

Impute Missing Values with Mean This function replaces missing values (NA) in a matrix, default is row means.

Usage

imputeBetasMatrixByMean(mx, axis = 1)

Arguments

mx

A matrix
axis

A single integer. Use 1 to impute column means (default), and 2 to impute row means.

Value

A matrix with missing values imputed.

Examples

mx <- cbind(c(1, 2, NA, 4), c(NA, 2, 3, 4))
imputeBetasMatrixByMean(mx, axis = 1)
imputeBetasMatrixByMean(mx, axis = 2)

Infer Ethnicity

Description

This function uses both the built-in rsprobes as well as the type I Color-Channel-Switching probes to infer ethnicity.

Usage

inferEthnicity(sdf, verbose = FALSE)

Arguments

sdf

a SigDF
verbose

print more messages

Details

s better be background subtracted and dyebias corrected for best accuracy

Please note: the betas should come from SigDF *without* channel inference.

Value

string of ethnicity

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
## inferEthnicity(sdf)

Infer and reset color channel for Type-I probes instead of using what is specified in manifest. The results are stored to sdf@extra$IGG and sdf@extra$IRR slot.

Description

IGG => Type-I green that is inferred to be green IRR => Type-I red that is inferred to be red

Usage

inferInfiniumIChannel(
  sdf,
  switch_failed = FALSE,
  mask_failed = FALSE,
  verbose = FALSE,
  summary = FALSE
)

Arguments

sdf

a SigDF
switch_failed

whether to switch failed probes (default to FALSE)
mask_failed

whether to mask failed probes (default to FALSE)
verbose

whether to print correction summary
summary

return summarized numbers only.

Value

a SigDF, or numerics if summary == TRUE

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
inferInfiniumIChannel(sdf)

Infer sex.

Description

We established our sex calling based on the CpGs hypermethylated in inactive X (XiH), CpGs hypomethylated in inactive X (XiL).

Usage

inferSex(betas, platform = NULL)

Arguments

betas

DNA methylation beta
platform

EPICv2, EPIC, HM450, MM285, etc.

Details

Note genotype abnormalities such as Dnmt genotype, XXY male (Klinefelter's), 45,X female (Turner's) can confuse the model sometimes. This function works on a single sample.

Value

Inferred sex of sample

Examples

## EPICv2 input
betas = openSesame(sesameDataGet("EPICv2.8.SigDF")[[1]])
inferSex(betas)

## Not run: 
## MM285 input
betas = openSesame(sesameDataGet("MM285.1.SigDF"))
inferSex(betas)

## EPIC input
betas = openSesame(sesameDataGet('EPIC.1.SigDF'))
inferSex(betas)

## HM450 input
betas = openSesame(sesameDataGet("HM450.10.SigDF")[[1]])
inferSex(betas)

## End(Not run)

Infer Species

Description

We infer species based on probes pvalues and alignment score. AUC was calculated for each specie, y_true is 1 or 0 for pval < threshold.pos or pval > threshold.neg, respeceively,

Usage

inferSpecies(
  sdf,
  topN = 1000,
  threshold.pos = 0.01,
  threshold.neg = 0.1,
  return.auc = FALSE,
  return.species = FALSE,
  verbose = FALSE
)

Arguments

sdf

a SigDF
topN

Top n positive and negative probes used to infer species. increase this number can sometimes improve accuracy (DEFAULT: 1000)
threshold.pos

pvalue < threshold.pos are considered positive (default: 0.01).
threshold.neg

pvalue > threshold.neg are considered negative (default: 0.2).
return.auc

return AUC calculated, override return.species
return.species

return a string to represent species
verbose

print more messaeges

Value

a SigDF

Examples

sdf <- sesameDataGet("MM285.1.SigDF")
sdf <- inferSpecies(sdf)

## all available species
all_species <- names(sesameDataGet(sprintf(
  "%s.addressSpecies", sdfPlatform(sdf)))$species)

Infer strain information for mouse array

Description

Infer strain information for mouse array

Usage

inferStrain(
  sdf,
  return.strain = FALSE,
  return.probability = FALSE,
  return.pval = FALSE,
  min_frac_dt = 0.2,
  verbose = FALSE
)

Arguments

sdf

SigDF
return.strain

return strain name
return.probability

return probability vector for all strains
return.pval

return p-value
min_frac_dt

minimum fraction of detected signal (DEFAULT: 0.2) otherwise, we give up strain inference and return NA.
verbose

print more messages

Value

a list of best guess, p-value of the best guess and the probabilities of all strains

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('MM285.1.SigDF')
inferStrain(sdf, return.strain = TRUE)
sdf.strain <- inferStrain(sdf)

inferTissue infers the tissue of a single sample (as identified through the branchIDs in the row data of the reference) by reporting independent composition through cell type deconvolution.

Description

inferTissue infers the tissue of a single sample (as identified through the branchIDs in the row data of the reference) by reporting independent composition through cell type deconvolution.

Usage

inferTissue(
  betas,
  reference = NULL,
  platform = NULL,
  abs_delta_beta_min = 0.3,
  auc_min = 0.99,
  coverage_min = 0.8,
  topN = 15
)

Arguments

betas

Named vector with probes and their corresponding beta value measurement
reference

Summarized Experiment with either hypomethylated or hypermethylated probe selection (row data), sample selection (column data), meta data, and the betas (assay)
platform

String representing the array type of the betas and reference
abs_delta_beta_min

Numerical value indicating the absolute minimum required delta beta for the probe selection criteria
auc_min

Numeric value corresponding to the minimum AUC value required for a probe to be considered
coverage_min

Numeric value corresponding to the minimum coverage requirement for a probe to be considered. Coverage is defined here as the proportion of samples without an NA value at a given probe.
topN

number of probes to at most use for each branch

Value

inferred tissue as a string

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet("MM285.1.SigDF")
inferTissue(getBetas(dyeBiasNL(noob(sdf))))

sesameDataGet_resetEnv()

initialize a fileSet class by allocating appropriate storage

Description

initialize a fileSet class by allocating appropriate storage

Usage

initFileSet(map_path, platform, samples, probes = NULL, inc = 4)

Arguments

map_path

path of file to map
platform

EPIC, HM450 or HM27, consistent with sdfPlatform(sdf)
samples

sample names
probes

probe names
inc

bytes per unit data storage

Value

a sesame::fileSet object

Examples

fset <- initFileSet('mybetas2', 'HM27', c('s1','s2'))

Annotate Probe IDs using KYCG databases

Description

see sesameData_annoProbes if you'd like to annotate by genomic coordinates (in GRanges)

Usage

KYCG_annoProbes(
  query,
  databases,
  db_names = NULL,
  platform = NULL,
  sep = ",",
  indicator = FALSE,
  silent = FALSE
)

Arguments

query

probe IDs in a character vector
databases

character or actual database (i.e. list of probe IDs)
db_names

specific database (default to all databases)
platform

EPIC, MM285 etc. will infer from probe IDs if not given
sep

delimiter used in paste
indicator

return the indicator matrix instead of a concatenated annotation (in the case of have multiple annotations)
silent

suppress message

Value

named annotation vector, or indicator matrix

Examples

query <- names(sesameData_getManifestGRanges("MM285"))
anno <- KYCG_annoProbes(query, "designGroup", silent = TRUE)

build gene-probe association database

Description

build gene-probe association database

Usage

KYCG_buildGeneDBs(
  query = NULL,
  platform = NULL,
  genome = NULL,
  max_distance = 10000,
  silent = FALSE
)

Arguments

query

the query probe list. If NULL, use all the probes on the platform
platform

HM450, EPIC, MM285, Mammal40, will infer from query if not given
genome

hg38, mm10, ..., will infer if not given. For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., genome = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.
max_distance

probe-gene distance for association
silent

suppress messages

Value

gene databases

Examples

query <- c("cg04707299", "cg13380562", "cg00480749")
dbs <- KYCG_buildGeneDBs(query, platform = "EPIC")
testEnrichment(query, dbs, platform = "EPIC")

Get databases by full or partial names of the database group(s)

Description

Get databases by full or partial names of the database group(s)

Usage

KYCG_getDBs(
  group_nms,
  db_names = NULL,
  platform = NULL,
  summary = FALSE,
  allow_multi = FALSE,
  ignore.case = FALSE,
  type = NULL,
  silent = FALSE
)

Arguments

group_nms

database group names
db_names

name of the database, fetech only the given databases
platform

EPIC, HM450, MM285, ... If given, will restrict to that platform.
summary

return a summary of database instead of db itself
allow_multi

allow multiple groups to be returned for
ignore.case

ignore case or not
type

numerical, categorical, default: all
silent

no messages each query.

Value

a list of databases, return NULL if no database is found

Examples

dbs <- KYCG_getDBs("MM285.chromHMM")
dbs <- KYCG_getDBs(c("MM285.chromHMM", "MM285.probeType"))

List database group names

Description

List database group names

Usage

KYCG_listDBGroups(filter = NULL, path = NULL, type = NULL)

Arguments

filter

keywords for filtering
path

file path to downloaded knowledgebase sets
type

categorical, numerical (default: all)

Value

a list of db group names

Examples

head(KYCG_listDBGroups("chromHMM"))
## or KYCG_listDBGroups(path = "~/Downloads")

Load database groups

Description

Load database groups

Usage

KYCG_loadDBs(in_paths, group_use_filename = FALSE)

Arguments

in_paths

folder that contains all databases
group_use_filename

whether to use file name for groups

Value

a list of db group names

Examples

## download regulatory annotations from
## http://zwdzwd.github.io/InfiniumAnnotation
## unzip the file
if (FALSE) {
dbs <- KYCG_loadDBs(path_to_unzipped_folder)
}

Bar plot to show most enriched CG groups from testEnrichment

Description

The input data frame should have an "estimate" and a "FDR" columns.

Usage

KYCG_plotBar(df, y = "-log10(FDR)", n = 20, order_by = "FDR", label = FALSE)

Arguments

df

KYCG result data frame
y

the column to be plotted on y-axis
n

number of CG groups to plot
order_by

the column by which CG groups are ordered
label

whether to label significant bars

Details

Top CG groups are determined by estimate (descending order).

Value

grid plot object

Examples

KYCG_plotBar(data.frame(
  estimate=runif(10,0,10), FDR=runif(10,0,1), nD=10,
  overlap=as.integer(runif(10,0,30)), group="g", dbname=seq_len(10)))

Dot plot to show most enriched CG groups from testEnrichment

Description

The input data frame should have an "estimate" and a "FDR" columns.

Usage

KYCG_plotDot(
  df,
  y = "-log10(FDR)",
  n = 20,
  order_by = "FDR",
  title = "Enriched Databases",
  label_by = "dbname",
  size_by = "overlap",
  color_by = "estimate",
  short_label = FALSE
)

Arguments

df

KYCG result data frame
y

the column to be plotted on y-axis
n

number of CG groups to plot
order_by

the column by which CG groups are ordered
title

plot title
label_by

the column for label
size_by

the column by which CG group size plot
color_by

the column by which CG groups are colored
short_label

omit group in label

Details

Top CG groups are determined by estimate (descending order).

Value

grid plot object (by ggplot)

Examples

KYCG_plotDot(data.frame(
  estimate=runif(10,0,10), FDR=runif(10,0,1), nD=runif(10,10,20),
  overlap=as.integer(runif(10,0,30)), group="g", dbname=seq_len(10)))

plot enrichment test result

Description

plot enrichment test result

Usage

KYCG_plotEnrichAll(
  df,
  fdr_max = 25,
  n_label = 15,
  min_estimate = 0,
  short_label = TRUE
)

Arguments

df

test enrichment result data frame
fdr_max

maximum fdr for capping
n_label

number of database to label
min_estimate

minimum estimate
short_label

use short label

Value

grid object

Examples

query <- KYCG_getDBs("MM285.designGroup")[["PGCMeth"]]
res <- testEnrichment(query, platform="MM285")
KYCG_plotEnrichAll(res)

creates a lollipop plot of log(estimate) given data with fields estimate.

Description

creates a lollipop plot of log(estimate) given data with fields estimate.

Usage

KYCG_plotLollipop(df, label_column = "dbname", n = 20)

Arguments

df

DataFrame where each row is a database name with its estimate.
label_column

column in df to be used as the label (default: dbname)
n

Integer representing the number of top enrichments to report. Optional. (Default: 10)

Value

ggplot lollipop plot

Examples

KYCG_plotLollipop(data.frame(
  estimate=runif(10,0,10), FDR=runif(10,0,1), nD=runif(10,10,20),
  overlap=as.integer(runif(10,0,30)), group="g",
  dbname=as.character(seq_len(10))))

KYCG_plotManhattan makes a manhattan plot to summarize EWAS results

Description

KYCG_plotManhattan makes a manhattan plot to summarize EWAS results

Usage

KYCG_plotManhattan(
  vals,
  platform = NULL,
  genome = NULL,
  title = NULL,
  label_min = 100,
  col = c("wheat1", "sienna3"),
  ylabel = "Value"
)

Arguments

vals

named vector of values (P,Q etc), vector name is Probe ID.
platform

String corresponding to the type of platform to use for retrieving GRanges coordinates of probes. Either MM285, EPIC, HM450, or HM27. If it is not provided, it will be inferred from the query set probeIDs (Default: NA).
genome

hg38, mm10, ..., will infer if not given. For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., genome = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.
title

title for plot
label_min

Threshold above which data points will be labelled with Probe ID
col

color
ylabel

y-axis label

Value

a ggplot object

Examples

## see vignette for examples
sesameDataGet_resetEnv()

Plot meta gene or other meta genomic features

Description

Plot meta gene or other meta genomic features

Usage

KYCG_plotMeta(betas, platform = NULL)

Arguments

betas

a named numeric vector or a matrix (row: probes; column: samples)
platform

if not given and x is a SigDF, will be inferred the meta features

Value

a grid plot object

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
KYCG_plotMeta(getBetas(sdf))

Plot meta gene or other meta genomic features

Description

Plot meta gene or other meta genomic features

Usage

KYCG_plotMetaEnrichment(result_list)

Arguments

result_list

one or a list of testEnrichment

Value

a grid plot object

Examples

cg_lists <- KYCG_getDBs("MM285.TFBS")
queries <- cg_lists[(sapply(cg_lists, length) > 40000)]
result_list <- lapply(queries, testEnrichment,
    "MM285.metagene", silent=TRUE, platform="MM285")

KYCG_plotMetaEnrichment(result_list)

Plot point range for a list of enrichment testing results against the same set of databases

Description

Plot point range for a list of enrichment testing results against the same set of databases

Usage

KYCG_plotPointRange(result_list)

Arguments

result_list

a list of testEnrichment resultsx

Value

grid plot object

Examples

## pick some big TFBS-overlapping CpG groups
cg_lists <- KYCG_getDBs("MM285.TFBS")
queries <- cg_lists[(sapply(cg_lists, length) > 40000)]
result_list <- lapply(queries, testEnrichment,
    "MM285.chromHMM", platform="MM285")
KYCG_plotPointRange(result_list)

Plot Set Enrichment

Description

Plot Set Enrichment

Usage

KYCG_plotSetEnrichment(result, n_sample = 1000, n_presence = 200)

Arguments

result

result object as returned from an element of the list of testEnrichmentSEA(..., prepPlot=TRUE)
n_sample

number of CpGs to sample
n_presence

number of overlap to sample for the plot

Value

grid object for plot

Examples

query <- KYCG_getDBs("KYCG.MM285.designGroup")[["VMR"]]
db <- KYCG_getDBs("MM285.seqContextN", "distToTSS")
res <- testEnrichmentSEA(query, db, prepPlot = TRUE)
KYCG_plotSetEnrichment(res[[1]])

creates a volcano plot of -log2(p.value) and log(estimate) given data with fields estimate and p.value.

Description

creates a volcano plot of -log2(p.value) and log(estimate) given data with fields estimate and p.value.

Usage

KYCG_plotVolcano(df, label_by = "dbname", alpha = 0.05)

Arguments

df

DataFrame where each field is a database name with two fields for the estimate and p.value.
label_by

column in df to be used as the label (default: dbname)
alpha

Float representing the cut-off alpha value for the plot. Optional. (Default: 0.05)

Value

ggplot volcano plot

Examples

KYCG_plotVolcano(data.frame(
  estimate=runif(10,0,10), FDR=runif(10,0,1), nD=runif(10,10,20),
  overlap=as.integer(runif(10,0,30)), group="g", dbname=seq_len(10)))

create a waterfall plot of log(estimate) given test enrichment

Description

create a waterfall plot of log(estimate) given test enrichment

Usage

KYCG_plotWaterfall(
  df,
  order_by = "Log2(OR)",
  size_by = "-log10(FDR)",
  label_by = "dbname",
  n_label = 10
)

Arguments

df

data frame where each row is a database with test enrichment result
order_by

the column by which CG groups are ordered
size_by

the column by which CG group size plot
label_by

column in df to be used as the label (default: dbname)
n_label

number of datapoints to label

Value

grid

Examples

library(SummarizedExperiment)
df <- rowData(sesameDataGet('MM285.tissueSignature'))
query <- df$Probe_ID[df$branch == "fetal_brain" & df$type == "Hypo"]
results <- testEnrichment(query, "TFBS", platform="MM285")
KYCG_plotWaterfall(results)

liftOver, see mLiftOver (renamed)

Description

liftOver, see mLiftOver (renamed)

Usage

liftOver(...)

Arguments

...

see mLiftOver

Value

imputed data, vector, matrix, SigDF(s)

list existing quality masks for a SigDF

Description

list existing quality masks for a SigDF

Usage

listAvailableMasks(platform, verbose = FALSE)

Arguments

platform

EPIC, MM285, HM450 etc
verbose

print more messages

Value

a tibble of masks

Examples

listAvailableMasks("EPICv2")

Deposit data of one sample to a fileSet (and hence to file)

Description

Deposit data of one sample to a fileSet (and hence to file)

Usage

mapFileSet(fset, sample, named_values)

Arguments

fset

a sesame::fileSet, as obtained via readFileSet
sample

sample name as a string
named_values

value vector named by probes

Value

a sesame::fileSet

Examples

## create two samples
fset <- initFileSet('mybetas2', 'HM27', c('s1','s2'))

## a hypothetical numeric array (can be beta values, intensities etc)
hypothetical <- setNames(runif(fset$n), fset$probes)

## map the numeric to file
mapFileSet(fset, 's1', hypothetical)

## get data
sliceFileSet(fset, 's1', 'cg00000292')

Map the SDF (from overlap array platforms) Replicates are merged by picking the best detection

Description

Map the SDF (from overlap array platforms) Replicates are merged by picking the best detection

Usage

mapToMammal40(sdf)

Arguments

sdf

a SigDF object

Value

a named numeric vector for beta values

Examples

sdf <- sesameDataGet("Mammal40.1.SigDF")
betas <- mapToMammal40(sdf[1:10,])

normalize Infinium I probe betas to Infinium II

Description

This is designed to counter tail inflation in Infinium I probes.

Usage

matchDesign(sdf, min_dbeta = 0.3)

Arguments

sdf

SigDF
min_dbeta

the default algorithm perform 2-state quantile-normalization of the unmethylated and methylated modes separately. However, when the two modes are too close, we fall back to a one-mode normalization. The threshold defines the maximum inter-mode distance.

Value

SigDF

Examples

library(RPMM)
sdf <- sesameDataGet("MM285.1.SigDF")
sesameQC_plotBetaByDesign(sdf)
sesameQC_plotBetaByDesign(matchDesign(sdf))

Whole-dataset-wide Mean Intensity

Description

The function takes one single SigDF and computes mean intensity of all the in-band measurements. This includes all Type-I in-band measurements and all Type-II probe measurements. Both methylated and unmethylated alleles are considered. This function outputs a single numeric for the mean.

Usage

meanIntensity(sdf, mask = TRUE)

Arguments

sdf

a SigDF
mask

whether to mask probes using mask column

Details

Note: mean in this case is more informative than median because methylation level is mostly bimodal.

Value

mean of all intensities

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
meanIntensity(sdf)

Whole-dataset-wide Median Total Intensity (M+U)

Description

The function takes one single SigDF and computes median intensity of M+U for each probe. This function outputs a single numeric for the median.

Usage

medianTotalIntensity(sdf, mask = TRUE)

Arguments

sdf

a SigDF
mask

whether to mask probes using mask column

Value

median of all intensities

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
medianTotalIntensity(sdf)

Lift over beta values or SigDFs to another Infinium platform This function wraps ID conversion and provide optional imputation functionality.

Description

Lift over beta values or SigDFs to another Infinium platform This function wraps ID conversion and provide optional imputation functionality.

Usage

mLiftOver(
  x,
  target_platform,
  source_platform = NULL,
  BPPARAM = SerialParam(),
  mapping = NULL,
  impute = FALSE,
  sd_max = 999,
  celltype = "Blood",
  ...
)

Arguments

x

either named beta value (vector or matrix), probe IDs or SigDF(s) if input is a matrix, probe IDs should be in the row names if input is a numeric vector, probe IDs should be in the vector names. If input is a character vector, the input will be considered probe IDs.
target_platform

the platform to take the data to
source_platform

optional information of the source data platform (when there might be ambiguity).
BPPARAM

use MulticoreParam(n) for parallel processing
mapping

a liftOver mapping file. Typically this file contains empirical evidence whether a probe mapping is reliable. If given, probe ID-based mapping will be skipped. This is to perform more stringent probe ID mapping.
impute

whether to impute or not, default is FALSE
sd_max

the maximum standard deviation for filtering low confidence imputation.
celltype

the cell type / tissue context of imputation, if not given, will use nearest neighbor to find out.
...

extra arguments, see ?convertProbeID

Value

imputed data, vector, matrix, SigDF(s)

Examples

## Not run: 
sesameDataCache()

## lift SigDF

sdf = sesameDataGet("EPICv2.8.SigDF")[["GM12878_206909630042_R08C01"]]
dim(mLiftOver(sdf, "EPICv2"))
dim(mLiftOver(sdf, "EPIC"))
dim(mLiftOver(sdf, "HM450"))

sdfs = sesameDataGet("EPICv2.8.SigDF")[1:2]
sdfs_hm450 = mLiftOver(sdfs, "HM450")
## parallel processing
sdfs_hm450 = mLiftOver(sdfs, "HM450", BPPARAM=BiocParallel::MulticoreParam(2))

sdf = sesameDataGet("EPIC.5.SigDF.normal")[[1]]
dim(mLiftOver(sdf, "EPICv2"))
dim(mLiftOver(sdf, "EPIC"))
dim(mLiftOver(sdf, "HM450"))

sdf = sesameDataGet("HM450.10.SigDF")[[1]]
dim(mLiftOver(sdf, "EPICv2"))
dim(mLiftOver(sdf, "EPIC"))
dim(mLiftOver(sdf, "HM450"))

## lift beta values

betas = openSesame(sesameDataGet("EPICv2.8.SigDF")[[1]])
betas_hm450 = mLiftOver(betas, "HM450", impute=TRUE)
length(betas_hm450)
sum(is.na(betas_hm450))
betas_hm450 <- mLiftOver(betas, "HM450", impute=FALSE)
length(betas_hm450)
sum(is.na(betas_hm450))
betas_epic1 <- mLiftOver(betas, "EPIC", impute=TRUE)
length(betas_epic1)
sum(is.na(betas_epic1))
betas_epic1 <- mLiftOver(betas, "EPIC", impute=FALSE)
length(betas_epic1)
sum(is.na(betas_epic1))

betas_matrix = openSesame(sesameDataGet("EPICv2.8.SigDF")[1:4])
dim(betas_matrix)
betas_matrix_hm450 = mLiftOver(betas_matrix, "HM450", impute=T)
dim(betas_matrix_hm450)
## parallel processing
betas_matrix_hm450 = mLiftOver(betas_matrix, "HM450", impute=T,
BPPARAM=BiocParallel::MulticoreParam(4))

## use empirical evidence in mLiftOver
mapping = sesameDataGet("liftOver.EPICv2ToEPIC")
betas_matrix = openSesame(sesameDataGet("EPICv2.8.SigDF")[1:4])
dim(mLiftOver(betas_matrix, "EPIC", mapping = mapping))
## compare to without using empirical evidence
dim(mLiftOver(betas_matrix, "EPIC"))

betas <- c("cg04707299"=0.2, "cg13380562"=0.9, "cg00000103"=0.1)
head(mLiftOver(betas, "HM450", impute=TRUE))

betas <- c("cg00004963_TC21"=0, "cg00004963_TC22"=0.5, "cg00004747_TC21"=1.0)
betas_hm450 <- mLiftOver(betas, "HM450", impute=TRUE)
head(na.omit(mLiftOver(betas, "HM450", impute=FALSE)))

## lift probe IDs

cg_epic2 = names(sesameData_getManifestGRanges("EPICv2"))
head(mLiftOver(cg_epic2, "HM450"))

cg_epic2 = grep("cg", names(sesameData_getManifestGRanges("EPICv2")), value=T)
head(mLiftOver(cg_epic2, "HM450"))

cg_hm450 = grep("cg", names(sesameData_getManifestGRanges("HM450")), value=T)
head(mLiftOver(cg_hm450, "EPICv2"))

rs_epic2 = grep("rs", names(sesameData_getManifestGRanges("EPICv2")), value=T)
head(mLiftOver(rs_epic2, "HM450", source_platform="EPICv2"))

probes_epic2 = names(sesameData_getManifestGRanges("EPICv2"))
head(mLiftOver(probes_epic2, "EPIC"))
head(mLiftOver(probes_epic2, "EPIC", target_uniq = TRUE))
head(mLiftOver(probes_epic2, "EPIC", include_new = FALSE))
head(mLiftOver(probes_epic2, "EPIC", include_old = FALSE))
head(mLiftOver(probes_epic2, "EPIC", return_mapping=TRUE))


## End(Not run)

Convert M-value to beta-value

Description

Convert M-value to beta-value (aka inverse logit transform)

Usage

MValueToBetaValue(m)

Arguments

m

a vector of M values

Value

a vector of beta values

Examples

MValueToBetaValue(c(-3, 0, 3))

get negative control signal

Description

get negative control signal

Usage

negControls(sdf)

Arguments

sdf

a SigDF

Value

a data frame of negative control signals

remove masked probes from SigDF

Description

remove masked probes from SigDF

Usage

noMasked(sdf)

Arguments

sdf

input SigDF object

Value

a SigDF object without masked probes

Examples

sesameDataCache()
sdf <- sesameDataGet("EPIC.1.SigDF")
sdf <- pOOBAH(sdf)

sdf_noMasked <- noMasked(sdf)

Noob background subtraction

Description

The function takes a SigDF and returns a modified SigDF with background subtracted. Background was modelled in a normal distribution and true signal in an exponential distribution. The Norm-Exp deconvolution is parameterized using Out-Of-Band (oob) probes. For species-specific processing, one should call inferSpecies on SigDF first. Multi-mapping probes are excluded.

Usage

noob(sdf, combine.neg = TRUE, offset = 15)

Arguments

sdf

a SigDF
combine.neg

whether to combine negative control probe.
offset

offset

Details

When combine.neg = TRUE, background will be parameterized by both negative control and out-of-band probes.

Value

a new SigDF with noob background correction

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.nb <- noob(sdf)

get normalization control signal

Description

get normalization control signal from SigDF. The function optionally takes mean for each channel.

Usage

normControls(sdf, average = FALSE, verbose = FALSE)

Arguments

sdf

a SigDF
average

whether to average
verbose

print more messages

Value

a data frame of normalization control signals

The openSesame pipeline

Description

This function is a simple wrapper of noob + nonlinear dye bias correction + pOOBAH masking.

Usage

openSesame(
  x,
  prep = "QCDPB",
  prep_args = NULL,
  manifest = NULL,
  func = getBetas,
  BPPARAM = SerialParam(),
  platform = "",
  min_beads = 1,
  ...
)

Arguments

x

SigDF(s), IDAT prefix(es)
prep

preprocessing code, see ?prepSesame
prep_args

optional preprocessing argument list, see ?prepSesame
manifest

optional dynamic manifest
func

either getBetas or getAFs, if NULL, then return SigDF list
BPPARAM

get parallel with MulticoreParam(n)
platform

optional platform string
min_beads

minimum bead number, probes with R or G smaller than this threshold will be masked. If NULL, no filtering based on bead count will be applied. Default to 1.
...

parameters to getBetas

Details

Please use mask=FALSE to turn off masking.

If the input is an IDAT prefix or a SigDF, the output is the beta value numerics.

Value

a numeric vector for processed beta values

Examples

in_dir <- system.file("extdata", "", package = "sesameData")
betas <- openSesame(in_dir)
## or
IDATprefixes <- searchIDATprefixes(in_dir)
betas <- openSesame(IDATprefixes)

openSesame pipeline with file-backed storage

Description

openSesame pipeline with file-backed storage

Usage

openSesameToFile(map_path, idat_dir, BPPARAM = SerialParam(), inc = 4)

Arguments

map_path

path of file to be mapped (beta values file)
idat_dir

source IDAT directory
BPPARAM

get parallel with MulticoreParam(2)
inc

bytes per item data storage. increase to 8 if precision is important. Most cases 32-bit representation is enough.

Value

a sesame::fileSet

Examples

openSesameToFile('mybetas',
    system.file('extdata',package='sesameData'))

Generate some additional color palettes

Description

Generate some additional color palettes

Usage

palgen(pal, n = 150, space = "Lab")

Arguments

pal

a string for adhoc pals
n

the number of colors for interpolation
space

rgb or Lab

Value

a palette-generating function

Examples

library(pals)
pal.bands(palgen("whiteturbo"))

Convert signal M and U to SigDF

Description

This overcomes the issue of missing IDAT files. However, out-of-band signals will be missing or faked (sampled from a normal distribution).

Usage

parseGEOsignalMU(
  sigM,
  sigU,
  Probe_IDs,
  oob.mean = 500,
  oob.sd = 300,
  platform = NULL
)

Arguments

sigM

methylated signal, a numeric vector
sigU

unmethylated signal, a numirc vector
Probe_IDs

probe ID vector
oob.mean

assumed mean for out-of-band signals
oob.sd

assumed standard deviation for out-of-band signals
platform

platform code, will infer if not given

Value

SigDF

Examples

sigM <- c(11436, 6068, 2864)
sigU <- c(1476, 804, 393)
probes <- c("cg07881041", "cg23229610", "cg03513874")
sdf <- parseGEOsignalMU(sigM, sigU, probes, platform = "EPIC")

Detection P-value based on ECDF of out-of-band signal

Description

aka pOOBAH (p-vals by Out-Of-Band Array Hybridization)

Usage

pOOBAH(
  sdf,
  return.pval = FALSE,
  combine.neg = TRUE,
  pval.threshold = 0.05,
  verbose = FALSE
)

Arguments

sdf

a SigDF
return.pval

whether to return p-values, instead of a masked SigDF
combine.neg

whether to combine negative control probes with the out-of-band probes in simulating the signal background
pval.threshold

minimum p-value to mask
verbose

print more messages

Details

The function takes a SigDF as input, computes detection p-value using out-of-band probes empirical distribution and returns a new SigDF with an updated mask slot.

Value

a SigDF, or a p-value vector if return.pval is TRUE

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
sum(sdf$mask)
sum(pOOBAH(sdf)$mask)

Predict age using linear models

Description

The function takes a named numeric vector of beta values. The name attribute contains the probe ID (cg, ch or rs IDs). The function looks for overlapping probes and estimate age using different models.

Usage

predictAge(betas, model, na_fallback = FALSE, min_nonna = 10)

Arguments

betas

a probeID-named vector of beta values
model

a model object from sesameDataGet. should contain param, intercept, response2age. default to the Horvath353 model.
na_fallback

use fall back values if na
min_nonna

the minimum number of non-NA values.

Details

You can get the models such as the Horvath aging model (Horvath 2013 Genome Biology) from sesameDataGet. The function outputs a single numeric of age in years.

Here are some built-in age models: Anno/HM450/Clock_Horvath353.rds Anno/HM450/Clock_Hannum.rds Anno/HM450/Clock_SkinBlood.rds Anno/EPIC/Clock_PhenoAge.rds Anno/MM285/Clock_Zhou347.rds see vignette inferences.html#Age__Epigenetic_Clock for details

Value

age in the unit specified in the model (usually in year, but sometimes can be month, like in the mouse clocks).

Examples

betas <- sesameDataGet('HM450.1.TCGA.PAAD')$betas
## Not run: 
## download age models from
## https://github.com/zhou-lab/InfiniumAnnotationV1/tree/main/Anno
## e.g., Anno/HM450/Clock_Horvath353.rds
predictAge(betas, model)

## End(Not run)

Horvath 353 age predictor

Description

The function takes a named numeric vector of beta values. The name attribute contains the probe ID (cg, ch or rs IDs). The function looks for overlapping probes and estimate age using Horvath aging model (Horvath 2013 Genome Biology). The function outputs a single numeric of age in years.

Usage

predictAgeHorvath353(betas)

Arguments

betas

a probeID-named vector of beta values

Value

age in years

Examples

cat("Deprecated. See predictAge")

Horvath Skin and Blood age predictor

Description

The function takes a named numeric vector of beta values. The name attribute contains the probe ID (cg, ch or rs IDs). The function looks for overlapping probes and estimate age using Horvath aging model (Horvath et al. 2018 Aging, 391 probes). The function outputs a single numeric of age in years.

Usage

predictAgeSkinBlood(betas)

Arguments

betas

a probeID-named vector of beta values

Value

age in years

Examples

cat("Deprecated. See predictAge")

Mouse age predictor

Description

The function takes a named numeric vector of beta values. The name attribute contains the probe ID. The function looks for overlapping probes and estimate age using an aging model built from 321 MM285 probes. The function outputs a single numeric of age in months. The clock is most accurate with the sesame preprocessing.

Usage

predictMouseAgeInMonth(betas, na_fallback = TRUE)

Arguments

betas

a probeID-named vector of beta values
na_fallback

use the fallback default for NAs.

Value

age in month

Examples

cat("Deprecated. See predictAge")

Mask SigDF by probe ID prefix

Description

Mask SigDF by probe ID prefix

Usage

prefixMask(sdf, prefixes = NULL, invert = FALSE)

Arguments

sdf

SigDF
prefixes

prefix characters
invert

use the complement set

Value

SigDF

Examples

sdf <- resetMask(sesameDataGet("MM285.1.SigDF"))
sum(prefixMask(sdf, c("ctl","rs"))$mask)
sum(prefixMask(sdf, c("ctl"))$mask)
sum(prefixMask(sdf, c("ctl","rs","ch"))$mask)

Mask all but C probes in SigDF

Description

Mask all but C probes in SigDF

Usage

prefixMaskButC(sdf)

Arguments

sdf

SigDF

Value

SigDF

Examples

sdf <- resetMask(sesameDataGet("MM285.1.SigDF"))
sum(prefixMaskButC(sdf)$mask)

Mask all but CG probes in SigDF

Description

Mask all but CG probes in SigDF

Usage

prefixMaskButCG(sdf)

Arguments

sdf

SigDF

Value

SigDF

Examples

sdf <- resetMask(sesameDataGet("MM285.1.SigDF"))
sum(prefixMaskButCG(sdf)$mask)

Apply a chain of sesame preprocessing functions in an arbitrary order

Description

Notes on the order of operation: 1. qualityMask and inferSpecies should go before noob and pOOBAH, otherwise the background is too high because of Multi, uk and other probes 2. dyeBias correction needs to happen early 3. channel inference before dyebias 4. noob should happen last, pOOBAH before noob because noob modifies oob

Usage

prepSesame(sdf, prep = "QCDPB", prep_args = NULL)

Arguments

sdf

SigDF
prep

code that indicates preprocessing functions and their execution order (functions on the left is executed first).
prep_args

optional argument list to individual functions, e.g., prepSesame(sdf, prep_args=list(Q=list(mask_names = "design_issue"))) sets qualityMask(sdf, mask_names = "design_issue")

Value

SigDF

Examples

sdf <- sesameDataGet("MM285.1.SigDF")
sdf1 <- prepSesame(sdf, "QCDPB")

List supported prepSesame functions

Description

List supported prepSesame functions

Usage

prepSesameList()

Value

a data frame with code, func, description

Examples

prepSesameList()

Print DMLSummary object

Description

Print DMLSummary object

Usage

## S3 method for class 'DMLSummary'
print(x, ...)

Arguments

x

a DMLSummary object
...

extra parameter for print

Value

print DMLSummary result on screen

Examples

sesameDataCache() # in case not done yet
data <- sesameDataGet('HM450.76.TCGA.matched')
## test the first 10
smry <- DML(data$betas[1:10,], ~type, meta=data$sampleInfo)
smry

sesameDataGet_resetEnv()

Print a fileSet

Description

Print a fileSet

Usage

## S3 method for class 'fileSet'
print(x, ...)

Arguments

x

a sesame::fileSet
...

stuff for print

Value

string representation

Examples

fset <- initFileSet('mybetas2', 'HM27', c('s1','s2'))
fset

Extract the probe type field from probe ID This only works with the new probe ID system. See https://github.com/zhou-lab/InfiniumAnnotation for illustration

Description

Extract the probe type field from probe ID This only works with the new probe ID system. See https://github.com/zhou-lab/InfiniumAnnotation for illustration

Usage

probeID_designType(Probe_ID)

Arguments

Probe_ID

Probe ID

Value

a vector of '1' and '2' suggesting Infinium-I and Infinium-II

Examples

probeID_designType("cg36609548_TC21")

Whole-dataset-wide Probe Success Rate

Description

This function calculates the probe success rate using pOOBAH detection p-values. Probes that has a detection p-value higher than a specific threshold are considered failed probes.

Usage

probeSuccessRate(sdf, mask = TRUE, max_pval = 0.05)

Arguments

sdf

a SigDF
mask

whether or not we count the masked probes in SigDF
max_pval

the maximum p-value to consider detection success

Value

a fraction number as probe success rate

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
probeSuccessRate(sdf)

Mask beta values by design quality

Description

Currently quality masking only supports three platforms see also listAvailableMasks(sdfPlatform(sdf))

Usage

qualityMask(sdf, mask_names = "recommended", verbose = TRUE)

Arguments

sdf

a SigDF object
mask_names

a vector of masking groups, see listAvailableMasks use "recommended" for recommended masking. One can also combine "recommended" with other masking groups by specifying a vector, e.g., c("recommended", "M_mapping")
verbose

be verbose

Value

a filtered SigDF

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sum(sdf$mask)
sum(qualityMask(sdf)$mask)
sum(qualityMask(sdf, mask_names = NULL)$mask)

## list available masks, the dbname column
listAvailableMasks(sdfPlatform(sdf))
listAvailableMasks("EPICv2")

Read an existing fileSet from storage

Description

This function only reads the meta-data.

Usage

readFileSet(map_path)

Arguments

map_path

path of file to map (should contain valid _idx.rds index)

Value

a sesame::fileSet object

Examples

## create two samples
fset <- initFileSet('mybetas2', 'HM27', c('s1','s2'))

## a hypothetical numeric array (can be beta values, intensities etc)
hypothetical <- setNames(runif(fset$n), fset$probes)

## map the numeric to file
mapFileSet(fset, 's1', hypothetical)

## read it from file
fset <- readFileSet('mybetas2')

## get data
sliceFileSet(fset, 's1', 'cg00000292')

Import a pair of IDATs from one sample

Description

The function takes a prefix string that are shared with _Grn.idat and _Red.idat. The function returns a SigDF.

Usage

readIDATpair(
  prefix.path,
  manifest = NULL,
  platform = "",
  min_beads = NULL,
  controls = NULL,
  verbose = FALSE
)

Arguments

prefix.path

sample prefix without _Grn.idat and _Red.idat
manifest

optional design manifest file
platform

EPIC, HM450 and HM27 etc.
min_beads

minimum bead number, probes with R or G smaller than this threshold will be masked. If NULL, no filtering based on bead count will be applied.
controls

optional control probe manifest file
verbose

be verbose? (FALSE)

Value

a SigDF

Examples

sdf <- readIDATpair(sub('_Grn.idat','',system.file(
    "extdata", "4207113116_A_Grn.idat", package = "sesameData")))

Recommended mask names for each Infinium platform

Description

The returned name is the db name used in KYCG.mask

Usage

recommendedMaskNames()

Value

a named list of mask names

Examples

recommendedMaskNames()[["EPICv2"]]
recommendedMaskNames()[["EPIC"]]

Re-identify IDATs by restoring scrambled SNP intensities

Description

This requries setting a seed with a secret number that was used to de-identify the IDAT (see example). This requires a secret number that was used to de-idenitfy the IDAT

Usage

reIdentify(path, out_path = NULL, snps = NULL, mft = NULL)

Arguments

path

input IDAT file
out_path

output IDAT file
snps

SNP definition, if not given, default to SNP probes
mft

sesame-compatible manifest if non-standard

Value

NULL, changes made to the IDAT files

Examples

temp_out <- tempfile("test")

set.seed(123)
reIdentify(system.file(
    "extdata", "4207113116_A_Grn.idat", package = "sesameData"), temp_out)
unlink(temp_out)

Reset Masking

Description

Reset Masking

Usage

resetMask(sdf, verbose = FALSE)

Arguments

sdf

a SigDF
verbose

print more messages

Value

a new SigDF with mask reset to all FALSE

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sum(sdf$mask)
sdf <- addMask(sdf, c("cg14057072", "cg22344912"))
sum(sdf$mask)
sum(resetMask(sdf)$mask)

SCRUB background correction

Description

This function takes a SigDF and returns a modified SigDF with background subtracted. scrub subtracts residual background using background median

Usage

scrub(sdf)

Arguments

sdf

a SigDF

Details

This function is meant to be used after noob.

Value

a new SigDF with noob background correction

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.nb <- noob(sdf)
sdf.nb.scrub <- scrub(sdf.nb)

SCRUB background correction

Description

This function takes a SigDF and returns a modified SigDF with background subtracted. scrubSoft subtracts residual background using a noob-like procedure.

Usage

scrubSoft(sdf)

Arguments

sdf

a SigDF

Details

This function is meant to be used after noob.

Value

a new SigDF with noob background correction

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
sdf.nb <- noob(sdf)
sdf.nb.scrubSoft <- scrubSoft(sdf.nb)

read a table file to SigDF

Description

read a table file to SigDF

Usage

sdf_read_table(fname, platform = NULL, verbose = FALSE, ...)

Arguments

fname

file name
platform

array platform (will infer if not given)
verbose

print more information
...

additional argument to read.table

Value

read table file to SigDF

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
fname <- sprintf("%s/sigdf.txt", tempdir())
sdf_write_table(sdf, file=fname)
sdf2 <- sdf_read_table(fname)

write SigDF to table file

Description

write SigDF to table file

Usage

sdf_write_table(sdf, ...)

Arguments

sdf

the SigDF to output
...

additional argument to write.table

Value

write SigDF to table file

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sdf_write_table(sdf, file=sprintf("%s/sigdf.txt", tempdir()))

collapse to probe prefix

Description

collapse to probe prefix

Usage

SDFcollapseToPfx(sdf)

Arguments

sdf

a SigDF object

Value

a data frame with updated Probe_ID

Convenience function to output platform attribute of SigDF

Description

Convenience function to output platform attribute of SigDF

Usage

sdfPlatform(sdf, verbose = FALSE)

Arguments

sdf

a SigDF object
verbose

print more messages

Value

the platform string for the SigDF object

Examples

sesameDataCache()
sdf <- sesameDataGet('EPIC.1.SigDF')
sdfPlatform(sdf)

Identify IDATs from a directory

Description

The input is the directory name as a string. The function identifies all the IDAT files under the directory. The function returns a vector of such IDAT prefixes under the directory.

Usage

searchIDATprefixes(dir.name, recursive = TRUE, use.basename = TRUE)

Arguments

dir.name

the directory containing the IDAT files.
recursive

search IDAT files recursively
use.basename

basename of each IDAT path is used as sample name This won't work in rare situation where there are duplicate IDAT files.

Value

the IDAT prefixes (a vector of character strings).

Examples

## only search what are directly under
IDATprefixes <- searchIDATprefixes(
    system.file("extdata", "", package = "sesameData"))

## search files recursively is by default
IDATprefixes <- searchIDATprefixes(
    system.file(package = "sesameData"), recursive=TRUE)

Segment bins using DNAcopy

Description

Segment bins using DNAcopy

Usage

segmentBins(bin.signals, bin.coords)

Arguments

bin.signals

bin signals (input)
bin.coords

bin coordinates

Value

segment signal data frame

Check SeSAMe versions

Description

print package verison of sesame and depended packages to help troubleshoot installation issues.

Usage

sesame_checkVersion()

Value

print the version of sesame, sesameData, biocondcutor and R

Examples

sesame_checkVersion()

Annotate a data.frame using manifest

Description

Annotate a data.frame using manifest

Usage

sesameAnno_attachManifest(
  df,
  probe_id = "Probe_ID",
  platform = NULL,
  genome = NULL
)

Arguments

df

input data frame with Probe_ID as a column
probe_id

the Probe_ID column name, default to "Probe_ID" or rownames
platform

which array platform, guess from probe ID if not given
genome

the genome build, use default if not given

Value

a new data.frame with manifest attached

Examples

## Not run: 
df <- data.frame(Probe_ID = c("cg00101675_BC21", "cg00116289_BC21"))
sesameAnno_attachManifest(df)

## End(Not run)

Build sesame ordering address file from tsv

Description

Build sesame ordering address file from tsv

Usage

sesameAnno_buildAddressFile(tsv)

Arguments

tsv

a platform name, a file path or a tibble/data.frame manifest file

Value

a list of ordering and controls

Examples

## Not run: 
tsv = sesameAnno_download("HM450.hg38.manifest.tsv.gz")
addr <- sesameAnno_buildAddressFile(tsv)

## End(Not run)

Build manifest GRanges from tsv

Description

manifest tsv files can be downloaded from http://zwdzwd.github.io/InfiniumAnnotation

Usage

sesameAnno_buildManifestGRanges(
  tsv,
  genome = NULL,
  decoy = FALSE,
  columns = NULL
)

Arguments

tsv

a file path, a platform (e.g., EPIC), or a tibble/data.frame object
genome

a genome string, e.g., hg38, mm10
decoy

consider decoy sequence in chromosome order
columns

the columns to include in the GRanges

Value

GRanges

Examples

## Not run: 
tsv = sesameAnno_download("HM450.hg38.manifest.tsv.gz")
gr <- sesameAnno_buildManifestGRanges(tsv)
## direct access
gr <- sesameAnno_buildManifestGRanges("HM450.hg38.manifest")

## End(Not run)

Download SeSAMe annotation files

Description

see also http://zwdzwd.github.io/InfiniumAnnotation

Usage

sesameAnno_download(url, destfile = tempfile(basename(url)))

Arguments

url

url or title of the annotation file
destfile

download to this file, a temp file if unspecified

Details

This function acts similarly as sesameAnno_get except that it directly download files without invoking BiocFileCache. This is needed in some situation because BiocFileCache may change the file name and downstream program may depend on the correct file names. It also lets you download files in a cleaner way without routing through BiocFileCache

Value

the path to downloaded file

Examples

## Not run: 
## avoid testing as this function uses external host
sesameAnno_download("Test/3999492009_R01C01_Grn.idat")
sesameAnno_download("EPIC.hg38.manifest.tsv.gz")
sesameAnno_download("EPIC.hg38.snp.tsv.gz")

## End(Not run)

Read manifest file to a tsv format

Description

Read manifest file to a tsv format

Usage

sesameAnno_readManifestTSV(tsv_fn)

Arguments

tsv_fn

tsv file path

Value

a manifest as a tibble

Examples

## Not run: 
tsv = sesameAnno_download("HM450.hg38.manifest.tsv.gz")
mft <- sesameAnno_readManifestTSV(tsv)
## direct access
mft <- sesameAnno_readManifestTSV("HM450.hg38.manifest")

## End(Not run)

Calculate QC statistics

Description

It is a function to call one or multiple sesameQC_calcStats functions

Usage

sesameQC_calcStats(sdf, funs = NULL)

Arguments

sdf

a SigDF object
funs

a sesameQC_calcStats_* function or a list of them default to all functions. One can also use a string such as "detection" or c("detection", "intensity") to reduce typing

Details

currently supporting: detection, intensity, numProbes, channel, dyeBias, betas

Value

a sesameQC object

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sesameQC_calcStats(sdf)
sesameQC_calcStats(sdf, "detection")
sesameQC_calcStats(sdf, c("detection", "channel"))
## retrieve stats as a list
sesameQC_getStats(sesameQC_calcStats(sdf, "detection"))
## or as data frames
as.data.frame(sesameQC_calcStats(sdf, "detection"))

Get stat numbers from an sesameQC object

Description

Get stat numbers from an sesameQC object

Usage

sesameQC_getStats(qc, stat_names = NULL, drop = TRUE)

Arguments

qc

a sesameQC object
stat_names

which stat(s) to retrieve, default to all.
drop

whether to drop to a string when stats_names has only one element.

Value

a list of named stats to be retrieved

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
qc <- sesameQC_calcStats(sdf, "detection")
sesameQC_getStats(qc, "frac_dt")

Bar plots for sesameQC

Description

By default, it plots median_beta_cg, median_beta_ch, RGratio, RGdistort, frac_dt

Usage

sesameQC_plotBar(qcs, keys = NULL)

Arguments

qcs

a list of SigDFs
keys

optional, other key to plot, instead of the default keys can be found in the parenthesis of the print output of each sesameQC output.

Value

a bar plot comparing different QC metrics

Examples

sesameDataCache() # if not done yet
sdfs <- sesameDataGet("EPIC.5.SigDF.normal")[1:2]
sesameQC_plotBar(lapply(sdfs, sesameQC_calcStats, "detection"))

Plot betas distinguishing different Infinium chemistries

Description

Plot betas distinguishing different Infinium chemistries

Usage

sesameQC_plotBetaByDesign(
  sdf,
  prep = NULL,
  legend_pos = "top",
  mar = c(3, 3, 1, 1),
  main = "",
  ...
)

Arguments

sdf

SigDF
prep

prep codes to step through
legend_pos

legend position (default: top)
mar

margin of layout when showing steps of prep
main

main title in plots
...

additional options to plot

Value

create a density plot

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
sesameQC_plotBetaByDesign(sdf, prep="DB")

Plot SNP heatmap

Description

Plot SNP heatmap

Usage

sesameQC_plotHeatSNPs(sdfs, cluster = TRUE, filter.nonvariant = TRUE)

Arguments

sdfs

beta value matrix, row: probes; column: samples
cluster

show clustered heatmap
filter.nonvariant

whether to filter nonvariant (range < 0.3)

Value

a grid graphics object

Examples

sdfs <- sesameDataGet("EPIC.5.SigDF.normal")[1:2]
plt <- sesameQC_plotHeatSNPs(sdfs, filter.nonvariant = FALSE)

Plot Total Signal Intensities vs Beta Values This plot is helpful in revealing the extent of signal background and dye bias.

Description

Plot Total Signal Intensities vs Beta Values This plot is helpful in revealing the extent of signal background and dye bias.

Usage

sesameQC_plotIntensVsBetas(
  sdf,
  mask = TRUE,
  use_max = FALSE,
  intens.range = c(5, 15),
  pal = "whiteturbo",
  ...
)

Arguments

sdf

a SigDF
mask

whether to remove probes that are masked
use_max

to use max(M,U) or M+U
intens.range

plot range of signal intensity
pal

color palette, whiteturbo, whiteblack, whitejet
...

additional arguments to smoothScatter

Value

create a total signal intensity vs beta value plot

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sesameQC_plotIntensVsBetas(sdf)

Plot red-green QQ-Plot using Infinium-I Probes

Description

Plot red-green QQ-Plot using Infinium-I Probes

Usage

sesameQC_plotRedGrnQQ(sdf, main = "R-G QQ Plot", ...)

Arguments

sdf

a SigDF
main

plot title
...

additional options to qqplot

Value

create a qqplot

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sesameQC_plotRedGrnQQ(sdf)

This function compares the input sample with public data. Only overlapping metrics will be compared.

Description

This function compares the input sample with public data. Only overlapping metrics will be compared.

Usage

sesameQC_rankStats(qc, publicQC = NULL, platform = "EPIC")

Arguments

qc

a sesameQC object
publicQC

public QC statistics, filtered from e.g.: EPIC.publicQC, MM285.publicQC and Mammal40.publicQC
platform

EPIC, MM285 or Mammal40, used when publicQC is not given

Value

a sesameQC

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
sesameQC_rankStats(sesameQC_calcStats(sdf, "intensity"))

An S4 class to hold QC statistics

Description

An S4 class to hold QC statistics

Value

sesameQC object

Slots

stat

a list to store qc stats

Convert a list of sesameQC to data frame

Description

Convert a list of sesameQC to data frame

Usage

sesameQCtoDF(qcs, cols = c("frac_dt_cg", "RGdistort", "RGratio"))

Arguments

qcs

sesameQCs
cols

QC columns, use NULL to report all

Value

a data frame

Examples

sdf <- sesameDataGet("EPIC.1.SigDF")
qcs <- sesameQC_calcStats(sdf, "detection")
sesameQCtoDF(qcs)

sesamize function is deprecated. Please check https://github.com/zwdzwd/sesamize for previous scripts

Description

sesamize function is deprecated. Please check https://github.com/zwdzwd/sesamize for previous scripts

Usage

sesamize(...)

Arguments

...

arguments for sesamize

Value

a message text for deprecated function

Examples

cat("Deprecated. see https://github.com/zwdzwd/sesamize")

Set mask to only the probes specified

Description

Set mask to only the probes specified

Usage

setMask(sdf, probes)

Arguments

sdf

a SigDF
probes

a vector of probe IDs or a logical vector with TRUE representing masked probes

Value

a SigDF with added mask

Examples

sdf <- sesameDataGet('EPIC.1.SigDF')
sum(sdf$mask)
sum(setMask(sdf, "cg14959801")$mask)
sum(setMask(sdf, c("cg14057072", "cg22344912"))$mask)

SigDF validation from a plain data frame

Description

SigDF validation from a plain data frame

Usage

SigDF(df, platform = "EPIC", ctl = NULL)

Arguments

df

a data.frame with Probe_ID, MG, MR, UG, UR, col and mask
platform

a string to specify the array platform
ctl

optional control probe data frame

Value

a SigDF object

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')

report M and U for regular probes

Description

report M and U for regular probes

Usage

signalMU(sdf, mask = TRUE, MU = FALSE)

Arguments

sdf

a SigDF
mask

whether to apply mask
MU

add a column for M+U

Value

a data frame of M and U columns

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
head(signalMU(sdf))

Slice a fileSet with samples and probes

Description

Slice a fileSet with samples and probes

Usage

sliceFileSet(fset, samples = fset$samples, probes = fset$probes, memmax = 10^5)

Arguments

fset

a sesame::fileSet, as obtained via readFileSet
samples

samples to query (default to all samples)
probes

probes to query (default to all probes)
memmax

maximum items to read from file to memory, to protect from accidental memory congestion.

Value

a numeric matrix of length(samples) columns and length(probes) rows

Examples

## create two samples
fset <- initFileSet('mybetas2', 'HM27', c('s1','s2'))

## a hypothetical numeric array (can be beta values, intensities etc)
hypothetical <- setNames(runif(fset$n), fset$probes)

## map the numeric to file
mapFileSet(fset, 's1', hypothetical)

## get data
sliceFileSet(fset, 's1', 'cg00000292')

Extract slope information from DMLSummary

Description

Extract slope information from DMLSummary

Usage

summaryExtractTest(smry)

Arguments

smry

DMLSummary from DML command

Value

a table of slope and p-value

Examples

sesameDataCache() # in case not done yet
data <- sesameDataGet('HM450.76.TCGA.matched')
smry <- DML(data$betas[1:10,], ~type, meta=data$sampleInfo)
slopes <- summaryExtractTest(smry)

sesameDataGet_resetEnv()

testEnrichment tests for the enrichment of set of probes (query set) in a number of features (database sets).

Description

testEnrichment tests for the enrichment of set of probes (query set) in a number of features (database sets).

Usage

testEnrichment(
  query,
  databases = NULL,
  universe = NULL,
  alternative = "greater",
  include_genes = FALSE,
  platform = NULL,
  silent = FALSE
)

Arguments

query

Vector of probes of interest (e.g., significant probes)
databases

List of vectors corresponding to the database sets of interest with associated meta data as an attribute to each element. Optional. (Default: NA)
universe

Vector of probes in the universe set containing all of the probes to be considered in the test. If it is not provided, it will be inferred from the provided platform. (Default: NA).
alternative

"two.sided", "greater", or "less"
include_genes

include gene link enrichment testing
platform

String corresponding to the type of platform to use. Either MM285, EPIC, HM450, or HM27. If it is not provided, it will be inferred from the query set probeIDs (Default: NA).
silent

output message? (Default: FALSE)

Value

A data frame containing features corresponding to the test estimate, p-value, and type of test.

Examples

library(SummarizedExperiment)
df <- rowData(sesameDataGet('MM285.tissueSignature'))
query <- df$Probe_ID[df$branch == "B_cell"]
res <- testEnrichment(query, "chromHMM", platform="MM285")
sesameDataGet_resetEnv()

testEnrichmentFisher uses Fisher's exact test to estimate the association between two categorical variables.

Description

Estimates log2 Odds ratio

Usage

testEnrichmentFisher(query, database, universe, alternative = "greater")

Arguments

query

Vector of probes of interest (e.g., significant probes)
database

Vectors corresponding to the database set of interest with associated meta data as an attribute to each element.
universe

Vector of probes in the universe set containing all of
alternative

greater or two.sided (default: greater) the probes to be considered in the test. (Default: NULL)

Value

A DataFrame with the estimate/statistic, p-value, and name of test for the given results.

Convenient function for testing enrichment of gene linkage

Description

Convenient function for testing enrichment of gene linkage

Usage

testEnrichmentGene(query, platform = NULL, silent = FALSE, ...)

Arguments

query

probe set of interest
platform

string corresponding to the type of platform to use. Either MM285, EPIC, HM450, or HM27. If it is not provided, it will be inferred from the query set probe IDs.
silent

whether to output message
...

addition argument provided to testEnrichment

Value

A data frame containing features corresponding to the test estimate, p-value, and type of test etc.

Examples

query <- c("cg04707299", "cg13380562", "cg00480749")
testEnrichment(query, platform = "EPIC")

uses the GSEA-like test to estimate the association of a categorical variable against a continuous variable.

Description

estimate represent enrichment score and negative estimate indicate a test for depletion

Usage

testEnrichmentSEA(
  query,
  databases,
  platform = NULL,
  silent = FALSE,
  precise = FALSE,
  prepPlot = FALSE
)

Arguments

query

query, if numerical, expect categorical database, if categorical expect numerical database
databases

database, numerical or categorical, but needs to be different from query
platform

EPIC, MM285, ..., infer if not given
silent

suppress message (default: FALSE)
precise

whether to compute precise p-value (up to numerical limit) of interest.
prepPlot

return the raw enrichment scores and presence vectors for plotting

Value

A DataFrame with the estimate/statistic, p-value, and name of test for the given results.

Examples

query <- KYCG_getDBs("KYCG.MM285.designGroup")[["TSS"]]
res <- testEnrichmentSEA(query, "MM285.seqContextN")

testEnrichmentSpearman uses the Spearman statistical test to estimate the association between two continuous variables.

Description

testEnrichmentSpearman uses the Spearman statistical test to estimate the association between two continuous variables.

Usage

testEnrichmentSpearman(query, database)

Arguments

query

Vector of probes of interest (e.g., significant probes)
database

List of vectors corresponding to the database set of interest with associated meta data as an attribute to each element.

Value

A DataFrame with the estimate/statistic, p-value, and name of test for the given results.

M+U Intensities Array

Description

The function takes one single SigDF and computes total intensity of all the in-band measurements by summing methylated and unmethylated alleles. This function outputs a single numeric for the mean.

Usage

totalIntensities(sdf, mask = FALSE)

Arguments

sdf

a SigDF
mask

whether to mask probes using mask column

Value

a vector of M+U signal for each probe

Examples

sesameDataCache() # if not done yet
sdf <- sesameDataGet('EPIC.1.SigDF')
intensities <- totalIntensities(sdf)

Estimate the fraction of the 2nd component in a 2-component mixture

Description

Estimate the fraction of the 2nd component in a 2-component mixture

Usage

twoCompsEst2(
  pop1,
  pop2,
  target,
  use.ave = TRUE,
  diff_1m2u = NULL,
  diff_1u2m = NULL
)

Arguments

pop1

Reference methylation level matrix for population 1
pop2

Reference methylation level matrix for population 2
target

Target methylation level matrix to be analyzed
use.ave

use population average in selecting differentially methylated probes
diff_1m2u

A vector of differentially methylated probes (methylated in population 1 but unmethylated in population 2)
diff_1u2m

A vector of differentially methylated probes (unmethylated in population 1 but methylated in population 2)

Value

Estimate of the 2nd component in the 2-component mixture

Set color and mask using strain/species-specific manifest

Description

also sets attr(,"species")

Usage

updateSigDF(sdf, species = NULL, strain = NULL, addr = NULL, verbose = FALSE)

Arguments

sdf

a SigDF
species

the species the sample is considered to be
strain

the strain the sample is considered to be
addr

species-specific address species, optional
verbose

print more messages

Value

a SigDF with updated color channel and mask

Examples

sdf <- sesameDataGet('Mammal40.1.SigDF')
sdf_mouse <- updateSigDF(sdf, species="mus_musculus")

Visualize Gene

Description

Visualize the beta value in heatmaps for a given gene. The function takes a gene name which is taken from the UCSC refGene. It searches all the transcripts for the given gene and optionally extend the span by certain number of base pairs. The function also takes a beta value matrix with sample names on the columns and probe names on the rows. The function can also work on different genome builds (default to hg38, can be hg19).

Usage

visualizeGene(
  gene_name,
  betas,
  platform = NULL,
  genome = NULL,
  upstream = 2000,
  dwstream = 2000,
  ...
)

Arguments

gene_name

gene name
betas

beta value matrix (row: probes, column: samples)
platform

HM450, EPIC, or MM285 (default)
genome

hg19, hg38, or mm10 (default)
upstream

distance to extend upstream
dwstream

distance to extend downstream
...

additional options, see visualizeRegion, assemble_plots

Value

None

Examples

betas <- sesameDataGet('HM450.76.TCGA.matched')$betas
visualizeGene('ADA', betas, 'HM450')

Visualize Region that Contains the Specified Probes

Description

Visualize the beta value in heatmaps for the genomic region containing specified probes. The function works only if specified probes can be spanned by a single genomic region. The region can cover more probes than specified. Hence the plotting heatmap may encompass more probes. The function takes as input a string vector of probe IDs (cg/ch/rs-numbers). if draw is FALSE, the function returns the subset beta value matrix otherwise it returns the grid graphics object.

Usage

visualizeProbes(
  probeNames,
  betas,
  platform = NULL,
  genome = NULL,
  upstream = 1000,
  dwstream = 1000,
  ...
)

Arguments

probeNames

probe names
betas

beta value matrix (row: probes, column: samples)
platform

HM450, EPIC or MM285 (default)
genome

hg19, hg38 or mm10 (default)
upstream

distance to extend upstream
dwstream

distance to extend downstream
...

additional options, see visualizeRegion and assemble_plots

Value

None

Examples

betas <- sesameDataGet('HM450.76.TCGA.matched')$betas
visualizeProbes(c('cg22316575', 'cg16084772', 'cg20622019'), betas, 'HM450')

Visualize Region

Description

The function takes a genomic coordinate (chromosome, start and end) and a beta value matrix (probes on the row and samples on the column). It plots the beta values as a heatmap for all probes falling into the genomic region. If 'draw=TRUE' the function returns the plotted grid graphics object. Otherwise, the selected beta value matrix is returned. 'cluster.samples=TRUE/FALSE' controls whether hierarchical clustering is applied to the subset beta value matrix.

Usage

visualizeRegion(
  chrm,
  beg,
  end,
  betas,
  platform = NULL,
  genome = NULL,
  draw = TRUE,
  cluster.samples = FALSE,
  na.rm = FALSE,
  nprobes.max = 1000,
  txn.types = "protein_coding",
  txn.font.size = 6,
  ...
)

Arguments

chrm

chromosome
beg

begin of the region
end

end of the region
betas

beta value matrix (row: probes, column: samples)
platform

EPIC, HM450, or MM285
genome

hg38, mm10, ..., will infer if not given. For additional mapping, download the GRanges object from http://zwdzwd.github.io/InfiniumAnnotation and provide the following argument ..., genome = sesameAnno_buildManifestGRanges("downloaded_file"),... to this function.
draw

draw figure or return betas
cluster.samples

whether to cluster samples
na.rm

remove probes with all NA.
nprobes.max

maximum number of probes to plot
txn.types

default to protein_coding, use NULL for all
txn.font.size

transcript name font size
...

additional options, see assemble_plots

Value

graphics or a matrix containing the captured beta values

Examples

betas <- sesameDataGet('HM450.76.TCGA.matched')$betas
visualizeRegion('chr20', 44648623, 44652152, betas, 'HM450')

Visualize segments

Description

The function takes a CNSegment object obtained from cnSegmentation and plot the bin signals and segments (as horizontal lines).

Usage

visualizeSegments(seg, to.plot = NULL, genes.to.label = NULL)

Arguments

seg

a CNSegment object
to.plot

chromosome to plot (by default plot all chromosomes)
genes.to.label

gene(s) to label

Details

require ggplot2, scales

Value

plot graphics

Examples

sesameDataCache()
## Not run: 
sdfs <- sesameDataGet('EPICv2.8.SigDF')
sdf <- sdfs[["K562_206909630040_R01C01"]]
seg <- cnSegmentation(sdf)
seg <- cnSegmentation(sdf, return.probe.signals=TRUE)
visualizeSegments(seg)
visualizeSegments(seg, to.plot=c("chr9","chr22"))
visualizeSegments(seg, genes.to.label=c("ABL1","BCR"))

## End(Not run)

sesameDataGet_resetEnv()