Package 'knowYourCG'

Title: Functional analysis of DNA methylome datasets
Description: KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks (<doi:10.1126/sciadv.adw3027>).
Authors: Wanding Zhou [aut, fnd] (ORCID: <https://orcid.org/0000-0001-9126-1932>), David Goldberg [aut, cre] (ORCID: <https://orcid.org/0000-0002-9622-4708>), Hongxiang Fu [ctb]
Maintainer: David Goldberg <[email protected]>
License: AGPL-3
Version: 1.9.0
Built: 2026-05-23 09:31:24 UTC
Source: https://github.com/bioc/knowYourCG

Help Index

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
kycgDataCache(data_titles=
c("KYCG.MM285.TFBSconsensus.20220116","KYCG.MM285.chromHMM.20210210"))

sesameData::sesameDataCache(data_titles=
c("probeIDSignature", "MM285.address"))

cg_lists <- getDBs("MM285.TFBS")
queries <- cg_lists[(sapply(cg_lists, length) > 40000)]
result_list <- lapply(queries, testEnrichment, "MM285.chromHMM")
mtx <- aggregateTestEnrichments(result_list)

Annotate Probe IDs using KYCG databases

Description

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

Usage

annoProbes(
  probeIDs,
  databases,
  db_names = NULL,
  platform = NULL,
  sep = ",",
  indicator = FALSE,
  silent = FALSE
)

Arguments

probeIDs

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

kycgDataCache(data_titles="KYCG.MM285.designGroup.20210210")
probes <- names(sesameData::sesameData_getManifestGRanges("MM285"))
anno <- annoProbes(probeIDs=probes, "designGroup", silent = TRUE)

Convert BED CpG set to YAME .cg format

Description

Converts a BED file listing CpGs into a YAME-compressed .cg file using the YAME reference coordinate and command-line tools.

Usage

bedToCg(bed_file, ref_cr, out_file, sort_bed = TRUE, verbose = FALSE)

Arguments

bed_file

Character string. Path to BED file listing CpGs.
ref_cr

Character string. Path to YAME reference coordinate file (.cr).
out_file

Character string. Path to output .cg file.
sort_bed

Logical. Whether to sort the BED file with bedtools before intersecting. (Default: TRUE)
verbose

Logical. Whether to print the shell commands. (Default: FALSE)

Value

Invisibly returns the output file path.

Examples

# Download YAME reference coordinate file for mm10
ref_cr <- tempfile(fileext = ".cr")
download.file(
  "https://zenodo.org/records/18176270/files/mm10chr16_f7_cpg.cr",
  destfile = ref_cr, quiet = TRUE, mode = "wb"
)

# Create a BED file with CpG positions
bed_file <- tempfile(fileext = ".bed")
bed_data <- c(
    "chr16\t3003648\t3003650",
    "chr16\t3003766\t3003768",
    "chr16\t3004036\t3004038",
    "chr16\t3004052\t3004054",
    "chr16\t3004275\t3004277",
    "chr16\t3004470\t3004472",
    "chr16\t3004545\t3004547",
    "chr16\t3004633\t3004635",
    "chr16\t3004652\t3004654",
    "chr16\t3004739\t3004741",
    "chr16\t3004841\t3004843",
    "chr16\t3004959\t3004961",
    "chr16\t3005065\t3005067",
    "chr16\t3005444\t3005446",
    "chr16\t3005522\t3005524")

writeLines(bed_data, bed_file)

# Convert BED to YAME .cg format (requires bedtools and yame)
out_file <- tempfile(fileext = ".cg")

bedToCg(bed_file, ref_cr, out_file, verbose = TRUE)


# Clean up
unlink(c(bed_file, ref_cr, out_file))

build gene-probe association database

Description

build gene-probe association database

Usage

buildGeneDBs(
  probeIDs = NULL,
  platform = NULL,
  genome = NULL,
  max_distance = 10000,
  silent = FALSE
)

Arguments

probeIDs

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.
max_distance

probe-gene distance for association
silent

suppress messages

Value

gene databases

Examples

sesameData::sesameDataCache(data_titles=
c("EPIC.address","genomeInfo.hg38","probeIDSignature"))
query <- c("cg04707299", "cg13380562", "cg00480749")
dbs <- buildGeneDBs(query, platform = "EPIC")
testEnrichment(query, dbs, platform = "EPIC")

dbStats aggregates methylation of a given betas matrix over specified database set features

Description

dbStats aggregates methylation of a given betas matrix over specified database set features

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)
sesameData::sesameDataCache(data_titles=
c("MM285.467.SE.tissue20Kprobes","KYCG.MM285.probeType.20210630"))
se <- sesameData::sesameDataGet("MM285.467.SE.tissue20Kprobes")
head(dbStats(assay(se), "MM285.probeType")[,1:3])
sesameData::sesameDataGet_resetEnv()

Master data frame for all object to cache

Description

This is an internal object which will be updated on every new release library(ExperimentHub) eh <- query(ExperimentHub(localHub=FALSE), "knowYourCG") eh <- query(ExperimentHub(localHub=FALSE), "sesameData") # older data data.frame(name=eh$title, eh=names(eh))

Format

A data.frame with columns:

name

Character. Resource name/title.

eh

Character. ExperimentHub record ID.

Details

Cache location is default to /Users/zhouw3/Library/Caches/org.R-project.R/R/ExperimentHub/

Value

master sheet of knowYourCG objects

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

getDBs(
  group_nms,
  db_names = NULL,
  platform = NULL,
  summary = FALSE,
  allow_multi = FALSE,
  type = "all",
  silent = FALSE
)

Arguments

group_nms

database group names
db_names

name of the database, fetch 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 each query
type

numerical, categorical, default: all
silent

no messages

Value

a list of databases, return NULL if no database is found. Each element includes 'group' and 'dbname' attributes.

Examples

kycgDataCache(data_titles=
c("KYCG.MM285.chromHMM.20210210","KYCG.MM285.probeType.20210630"))
dbs <- getDBs("MM285.chromHMM")
dbs <- getDBs(c("MM285.chromHMM", "MM285.probeType"))

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 Knowledgebases",
  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 <- 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,
  rasterize = FALSE,
  rasterize_thres = 3,
  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
rasterize

if true use ggrastr to rasterize non-significant data.
rasterize_thres

the threshold of rasterize
label_min

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

color
ylabel

y-axis label

Value

a ggplot object

Examples

library(sesameData)

## Create example with simulated -log10(p-values)
## Mix of non-significant (low values) and significant (high values)
probes <- names(sesameData_getManifestGRanges("HM450"))
set.seed(123)
vals <- setNames(
    c(runif(990, 0, 3),      # Non-significant probes
      runif(10, 5, 25)),     # Significant probes
    sample(probes, 1000)
)

KYCG_plotManhattan(vals,
    platform = "HM450",
    title = "Example Manhattan Plot",
    ylabel = "-log10(P-value)",
    label_min = 20)

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

library(sesameData)
library(sesame)
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 <- 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 <- 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 <- getDBs("KYCG.MM285.designGroup")[["VMR"]]
db <- 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)
library(sesameData)
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)

Cache KnowYourCG data

Description

Cache KnowYourCG data

Usage

kycgDataCache(data_titles = NULL)

Arguments

data_titles

data to cache, if not given will cache all

Value

TRUE

Examples

kycgDataCache("KYCG.HM27.Mask.20220123")
## to cache all data: kycgDataCache()

Get KnowYourCG data

Description

Get KnowYourCG data

Usage

kycgDataGet(title, verbose = FALSE)

Arguments

title

title of the data
verbose

whether to output ExperimentHub message

Value

data object

Examples

kycgDataCache("KYCG.MSA.CGI.20220904")
EPIC.1.SigDF <- kycgDataGet('KYCG.MSA.CGI.20220904')

find genes in genomic proximity to given Infinium probes

Description

This is a convenient function that uses sesameData_getGenomeInfo() to retrieve stored gene models.

Usage

linkProbesToProximalGenes(probeIDs, platform = NULL, genome = NULL)

Arguments

probeIDs

character vector of probe IDs
platform

HM450, EPIC, EPICv2, MM285, MSA, ..., will infer from probe ID if not given
genome

hg38, hg19, mm10, this is usually inferred from platform.

Details

For finer control, such as taking only genes by their promoters, please use sesameData_getTxnGRanges followed by sesameData_annoProbes(). See code of this convenient function for details.

Value

a data frame annotate gene list linked to each given probes

Examples

library(SummarizedExperiment)
probes = rowData(
    sesameData::sesameDataGet('MM285.tissueSignature'))$Probe_ID[1:10]
linkProbesToProximalGenes(probes, platform = "MM285")

List database group names

Description

List database group names

Usage

listDBGroups(filter = NULL, path = NULL)

Arguments

filter

keywords for filtering
path

file path to downloaded knowledgebase sets

Value

a list of db group names

Examples

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

Load knowledgebase databases from TSV files

Description

This used to be an exported function. Now it's internal. Use RDS download directly.

Usage

loadDBs(in_paths)

Arguments

in_paths

Character vector of file paths, URLs to .tsv or .tsv.gz files, or a single directory path containing such files. If a directory is provided, all files in that directory will be loaded. URLs (starting with http:// or https://) are loaded directly.

Details

This function loads knowledgebase sets from tab-delimited (.tsv or .tsv.gz) files downloaded from Zenodo or other sources. The TSV files should contain two columns: "Probe_ID" and "Knowledgebase". The function splits the data by knowledgebase name and returns a list of database vectors.

The input TSV file(s) must have a header row and contain at least two columns:

  • Probe_ID - Probe identifiers (e.g., cg12345678)

  • Knowledgebase - Knowledgebase/database name

Value

A list of database vectors. Each element contains Probe_IDs with attributes:

  • group - The database group name (derived from filename)

  • dbname - The knowledgebase name (from the Knowledgebase column)

Test for enrichment of query in knowledgebase sets

Description

Test for enrichment of query in knowledgebase sets

Usage

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

Arguments

query

For array input, a vector of probes of interest (e.g., significant differential methylated probes). For sequencing data input, the file name for YAME-compressed CG sets.
databases

List of vectors corresponding to the database sets of interest with associated meta data as an attribute to each element. If NULL, all available databases for the platform are used. (Default: NULL)
universe

Vector of probes in the universe set containing all probes to be considered in the test. If NULL, will be inferred from the provided platform. (Default: NULL)
alternative

Test alternative: "two.sided", "greater", or "less". (Default: "greater")
include_genes

Include gene link enrichment testing. (Default: FALSE)
platform

String corresponding to the type of platform to use: MM285, EPIC, HM450, or HM27. If NULL, will be inferred from query set probe IDs. (Default: NULL)
silent

Suppress output messages? (Default: FALSE)
mtc_by_group

Perform multiple testing correction within knowledgebase groups. (Default: TRUE)
mtc_method

Method for multiple test correction. (Default: "fdr")

Value

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

Examples

library(SummarizedExperiment)
library(sesameData)
library(knowYourCG)
kycgDataCache(data_titles = "KYCG.MM285.chromHMM.20210210")
sesameDataCache("MM285.tissueSignature")
df <- rowData(sesameDataGet("MM285.tissueSignature"))
probes <- df$Probe_ID[df$branch == "B_cell"]
res <- testEnrichment(probes, "chromHMM", platform = "MM285")


# Define temporary directory and file URLs
temp_dir <- tempdir()
knowledgebase <- file.path(temp_dir, "ChromHMM.20220414.cm")
query <- file.path(temp_dir, "mm10_f3_10cells.cg")

# URLs for the knowledgebase and query files
knowledgebase_url <- paste0(
  "https://zenodo.org/records/18175656/files/",
  "ChromHMM.20220414.cm"
)
query_url <- paste0(
  "https://zenodo.org/records/18176004/files/",
  "mm10_f3_10cells.cg"
)

# Download the files
download.file(knowledgebase_url, destfile = knowledgebase)
download.file(query_url, destfile = query)

# Confirm file download
list.files(temp_dir)
res <- testEnrichment(query, knowledgebase)

Test enrichment from YAME-compressed CG sets

Description

Tests for enrichment of genomic regions in YAME-compressed CG sets using Fisher's exact test. This function is optimized for sequencing data and uses compiled C code for efficient processing.

Usage

testEnrichment2(
  query_fn,
  knowledge_fn,
  universe_fn = NULL,
  alternative = "greater",
  min_overlap = 1,
  verbose = FALSE
)

Arguments

query_fn

Character string specifying the file path to the query CG set file (YAME-compressed format).
knowledge_fn

Character string or vector specifying the file path(s) to the knowledgebase file(s) (YAME-compressed format). Can be a single file or multiple files.
universe_fn

Optional character string specifying the file path to the universe CG set file. If NULL, universe will be inferred from the knowledgebase. (Default: NULL)
alternative

Character string specifying the alternative hypothesis: "greater" (enrichment), "less" (depletion), or "two.sided". (Default: "greater")
min_overlap

Minimum number of overlapping CGs required for a test to be included in results. (Default: 1)
verbose

Logical indicating whether to print progress messages. (Default: FALSE)

Value

A tibble containing enrichment test results with the following columns:

Mask

Name/identifier of the knowledgebase mask

N_mask

Number of CGs in the mask

N_query

Number of CGs in the query

N_overlap

Number of overlapping CGs

N_univ

Total number of CGs in universe

estimate

Log2 odds ratio

p.value

P-value from Fisher's exact test

log10.p.value

Log10-transformed p-value

test

Type of test performed

Additional effect size metrics

Jaccard, MCC, etc.

Examples

if (.Platform$OS.type != "windows") {
  kfn <- system.file("extdata", "chromhmm.cm", package = "knowYourCG")
  qfn <- system.file("extdata", "onecell.cg", package = "knowYourCG")
  res <- testEnrichment2(qfn, kfn)
  head(res)
}

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

sesameData::sesameDataCache(data_titles=
c("KYCG.MM285.designGroup.20210210","KYCG.MM285.seqContextN.20210630",
"probeIDSignature"))
query <- 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(num_query, num_db)

Arguments

num_query

named numeric vector of probes of interest where names are probe IDs (e.g significant probes)
num_db

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.

testProbeProximity tests if a query set of probes share closer genomic proximity than if randomly distributed

Description

testProbeProximity tests if a query set of probes share closer genomic proximity than if randomly distributed

Usage

testProbeProximity(
  probeIDs,
  gr = NULL,
  platform = NULL,
  iterations = 100,
  bin_size = 1500
)

Arguments

probeIDs

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

GRanges to draw samples and compute genomic distances
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).
iterations

Number of random samples to generate null distribution (Default: 100).
bin_size

the poisson interval size for computing neighboring hits

Value

list containing a dataframe for the poisson statistics and a data frame for the probes in close proximity

Examples

sesameData::sesameDataCache(data_titles=
c("MM285.tissueSignature","MM285.address","probeIDSignature"))
library(SummarizedExperiment)
df <- rowData(sesameData::sesameDataGet("MM285.tissueSignature"))
probes <- df$Probe_ID[df$branch == "B_cell"]
res <- testProbeProximity(probeIDs=probes,platform="MM285")
sesameData::sesameDataGet_resetEnv()