Package 'UMI4Cats' reference manual

Title:	UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data
Description:	UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay.
Authors:	Mireia Ramos-Rodriguez [aut, cre] , Marc Subirana-Granes [aut], Lorenzo Pasquali [aut]
Maintainer:	Mireia Ramos-Rodriguez <[email protected]>
License:	Artistic-2.0
Version:	1.15.0
Built:	2024-09-28 07:02:33 UTC
Source:	https://github.com/bioc/UMI4Cats

Get BiocFileCache object

Description

Get BiocFileCache object

Usage

.getCache()
.getCache()

Value

Returns BFC object with the cache for the UMI4Cats package

Summarize BAM file

Description

Get summary of interesting bam statistics

Usage

.getSummaryBam(bam_file, mapped = TRUE, secondary = FALSE)
.getSummaryBam(bam_file, mapped = TRUE, secondary = FALSE)

Arguments

`bam_file`	Path for the bam file.
`mapped`	Logical indicating whether to extract mapped reads.
`secondary`	Logical indicating whether to extract secondary aligned reads.

Value

Returns a numeric containing the number of reads in bam_file that has the specified mapped and secondary status.

Align split fastq file

Description

Align split fastq file

Usage

.singleAlignmentUMI4C(
  split_file,
  align_dir,
  threads = 1,
  bowtie_index,
  pos_viewpoint,
  filter_bp = 1e+07
)
.singleAlignmentUMI4C(
  split_file,
  align_dir,
  threads = 1,
  bowtie_index,
  pos_viewpoint,
  filter_bp = 1e+07
)

Arguments

`split_file`	Split fastq file to align.
`align_dir`	Directory where to save aligned files.
`threads`	Number of threads to use in the analysis. Default=1.
`bowtie_index`	Path and prefix of the bowtie index to use for the alignment.
`pos_viewpoint`	GRanges object containing the genomic position of the viewpoint.
`filter_bp`	Integer indicating the bp upstream and downstream of the viewpoint to select for further analysis. Default=10e6

Value

Creates a BAM file in wk_dir/align named "basename(fastq))_filtered.bam", containing the aligned filtered reads. A data.frame object with the statisitics is also returned.

Count UMIs for a given bam file.

Description

Count UMIs for a given bam file.

Usage

.singleCounterUMI4C(
  filtered_bam_R1,
  filtered_bam_R2,
  digested_genome_gr,
  pos_viewpoint,
  res_enz,
  count_dir,
  filter_bp = 1e+07
)
.singleCounterUMI4C(
  filtered_bam_R1,
  filtered_bam_R2,
  digested_genome_gr,
  pos_viewpoint,
  res_enz,
  count_dir,
  filter_bp = 1e+07
)

Arguments

`filtered_bam_R1`	R1 bam file.
`filtered_bam_R2`	R2 bam file.
`digested_genome_gr`	GRanges object containing the coordinates for the digested genome.
`pos_viewpoint`	Vector consist of chromosome, start and end position of the viewpoint.
`res_enz`	Character containing the restriction enzyme sequence.
`count_dir`	Counter directory.
`filter_bp`	Integer indicating the bp upstream and downstream of the viewpoint to select for further analysis. Default=10e6

Value

Creates a tab-delimited file in wk_dir/count named "basename(fastq) _counts.tsv", containing the coordinates for the viewpoint fragment, contact fragment and the number of UMIs detected in the ligation.

Prepar fastq files at a given barcode.

Description

Prepar fastq files at a given barcode.

Usage

.singlePrepUMI4C(
  fq_R1,
  fq_R2,
  bait_seq,
  bait_pad,
  res_enz,
  prep_dir,
  numb_reads = 1e+09
)
.singlePrepUMI4C(
  fq_R1,
  fq_R2,
  bait_seq,
  bait_pad,
  res_enz,
  prep_dir,
  numb_reads = 1e+09
)

Arguments

`fq_R1`	Fastq file R1.
`fq_R2`	Fastq file R2.
`bait_seq`	Character containing the bait primer sequence.
`bait_pad`	Character containing the pad sequence (sequence between the bait primer and the restriction enzyme sequence).
`res_enz`	Character containing the restriction enzyme sequence.
`prep_dir`	Prep directory.
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=1e9.

Value

Creates a compressed FASTQ file in wk_dir/prep named basename(Fastq)).fq.gz, containing the filtered reads with the UMI sequence in the header. A data.frame object with the statisitics is also returned.

Split fastq files at a given restriction site.

Description

Split fastq files at a given restriction site.

Usage

.singleSplitUMI4C(
  fastq_file,
  res_enz,
  cut_pos,
  split_dir,
  min_flen = 20,
  numb_reads = 1e+09
)
.singleSplitUMI4C(
  fastq_file,
  res_enz,
  cut_pos,
  split_dir,
  min_flen = 20,
  numb_reads = 1e+09
)

Arguments

`fastq_file`	Fastq file path.
`res_enz`	Character containing the restriction enzyme sequence.
`cut_pos`	Numeric indicating the nucleotide position where restriction enzyme cuts (zero-based) (for example, for DpnII is 0).
`split_dir`	Directory where to save split files.
`min_flen`	Minimal fragment length to use for selecting the fragments. Default=20
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=1e9.

Value

Creates a compressed FASTQ file in wk_dir/split named basename(fastq)).fq.gz, containing the split reads based on the restriction enzyme used.

Monotone smoothing of the VST counts

Description

Takes the variance stabilized count values and calculates a symmetric monotone fit for the distance dependency. The signal trend is fitted using the fda package.

Usage

.smoothMonotone(trafo_counts, alpha = 20, penalty = 0.1, frag_data)
.smoothMonotone(trafo_counts, alpha = 20, penalty = 0.1, frag_data)

Arguments

`trafo_counts`	Variance stabilized count values assay from DDS object.
`alpha`	Approximate number of fragments desired for every basis function of the B-spline basis. `floor((max(number of fragments)) / alpha)` is passed to `create.bspline.basis` as nbasis argument. 4 is the minimum allowed value. Default: 20.
`penalty`	Amount of smoothing to be applied to the estimated functional parameter. Default: 0.1.
`frag_data`	Data frame with all the information on restriction fragments and the interval around the viewpoint.

Details

This function computes the smoothing function for the VST values, based on fda package, and calculates a symmetric monotone fit counts for the distance dependency

Value

A dataframe with monotone smoothed fit counts.

Add grouping of UMI-4C samples

Description

This function can be used to add specific groupings to UMI4C objects.

Usage

addGrouping(
  umi4c,
  grouping = "sampleID",
  scales = 5:150,
  normalized = TRUE,
  sd = 2
)
addGrouping(
  umi4c,
  grouping = "sampleID",
  scales = 5:150,
  normalized = TRUE,
  sd = 2
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`grouping`	Name of the column in colData used to merge the samples or replicates. Set to NULL for skipping grouping. Default: "condition".
`scales`	Numeric vector containing the scales for calculating the domainogram.
`normalized`	Logical indicating whether UMI-4C profiles should be normalized to the `ref_umi4c` sample/group. Default: TRUE
`sd`	Stantard deviation for adaptative trend.

Value

Adds a new UMI4C object into the groupsUMI4C slot with samples grouped according to grouping variable.

Examples

data("ex_ciita_umi4c")

ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")
data("ex_ciita_umi4c")

ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

Add stepping for plotting genes

Description

Given a GRanges dataset representing genes, will add an arbitrary value for them to be plotted in the Y axis without overlapping each other.

Usage

addStepping(genesDat, coordinates, mcol.name)
addStepping(genesDat, coordinates, mcol.name)

Arguments

`genesDat`	GRanges object containing gene information.
`coordinates`	GRanges object with coordinates you want to plot.
`mcol.name`	Integer containing the column number that contains the gene name.

Value

Calculates the stepping position to avoid overlap between genes.

UMI4C alignment

Description

Align split UMI-4C reads to a reference genome using Bowtie2.

Usage

alignmentUMI4C(
  wk_dir,
  pos_viewpoint,
  bowtie_index,
  threads = 1,
  filter_bp = 1e+07
)
alignmentUMI4C(
  wk_dir,
  pos_viewpoint,
  bowtie_index,
  threads = 1,
  filter_bp = 1e+07
)

Arguments

`wk_dir`	Working directory where to save the outputs generated by the UMI-4c analysis.
`pos_viewpoint`	GRanges object containing the genomic position of the viewpoint. It can be generated by `getViewpointCoordinates` function.
`bowtie_index`	Path and prefix of the bowtie index to use for the alignment.
`threads`	Number of threads to use in the analysis. Default=1.
`filter_bp`	Integer indicating the bp upstream and downstream of the viewpoint to select for further analysis. Default=10e6

Value

Creates a BAM file in wk_dir/align named "basename(fastq))_filtered.bam", containing the aligned filtered reads. The alignment log is also generated in wk_dir/logs named "umi4c_alignment_stats.txt".

Examples

if (interactive()){
path <- downloadUMI4CexampleData(reduced = TRUE)
alignmentUMI4C(
    wk_dir = file.path(path, "CIITA"),
    pos_viewpoint = GenomicRanges::GRanges("chr16:10972515-10972548"),
    bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16")
)
}
if (interactive()){
path <- downloadUMI4CexampleData(reduced = TRUE)
alignmentUMI4C(
    wk_dir = file.path(path, "CIITA"),
    pos_viewpoint = GenomicRanges::GRanges("chr16:10972515-10972548"),
    bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16")
)
}

Adaptative smoothing of normalized trend

Description

Will perform adaptative smoothing will scaling one profile to the reference UMI-4C profile.

Usage

calculateAdaptativeTrend(umi4c, sd = 2, normalized = TRUE)
calculateAdaptativeTrend(umi4c, sd = 2, normalized = TRUE)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`sd`	Stantard deviation for adaptative trend.
`normalized`	Logical indicating whether UMI-4C profiles should be normalized to the `ref_umi4c` sample/group. Default: TRUE

Value

Calculates the adaptative trend considering the minimum number of molecules to use for merging different restriction fragments. It also calculates the geometric mean of the coordinates of the merged restriction fragments.

Create Domainogram

Description

Using as input the raw UMIs, this function creates a domainogram for the supplied scales.

Usage

calculateDomainogram(umi4c, scales = 5:150, normalized = TRUE)
calculateDomainogram(umi4c, scales = 5:150, normalized = TRUE)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`scales`	Integer vector indicating the number of scales to use for the domainogram creation. Default: 5:150.
`normalized`	Logical whether the the resulting domainograms should be normalized or not. Default: TRUE.

Value

A matrix where the first column represents the fragment end coordinates (start) and the rest represent the number of UMIs found when using a specific scale.

Call significant interactions

Description

Test a set of query_regions for significant interactions with the viewpoint.

Usage

callInteractions(
  umi4c,
  design = ~condition,
  query_regions,
  padj_method = "fdr",
  zscore_threshold = 2,
  padj_threshold = 0.1,
  alpha = 20,
  penalty = 0.1
)
callInteractions(
  umi4c,
  design = ~condition,
  query_regions,
  padj_method = "fdr",
  zscore_threshold = 2,
  padj_threshold = 0.1,
  alpha = 20,
  penalty = 0.1
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`design`	A `formula` or `matrix`. The formula expresses how the counts for each fragment end depend on the variables in `colData`. See `DESeqDataSet`.
`query_regions`	`GRanges` object or `data.frame` containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.
`padj_method`	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
`zscore_threshold`	Numeric indicating the z-score threshold to use to define significant differential contacts. Default: 2.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.1.
`alpha`	Approximate number of fragments desired for every basis function of the B-spline basis. `floor((max(number of fragments)) / alpha)` is passed to `create.bspline.basis` as nbasis argument. 4 is the minimum allowed value. Default: 20.
`penalty`	Amount of smoothing to be applied to the estimated functional parameter. Default: 0.1.

Value

GRangesList where each element is a UMI4C sample with the queried regions and their adjusted p-values and Z-scores.

Examples

data("ex_ciita_umi4c")
umi <- ex_ciita_umi4c
win_frags <- makeWindowFragments(umi, n_frags=8, sliding=1)

gr <- callInteractions(umi, ~condition, win_frags, padj_threshold = 0.01, zscore_threshold=2)
inter <- getSignInteractions(gr)
data("ex_ciita_umi4c")
umi <- ex_ciita_umi4c
win_frags <- makeWindowFragments(umi, n_frags=8, sliding=1)

gr <- callInteractions(umi, ~condition, win_frags, padj_threshold = 0.01, zscore_threshold=2)
inter <- getSignInteractions(gr)

Combine UMI4C fragments

Description

Combine the UMI4C fragments that overlap a given set of query_regions.

Usage

combineUMI4C(umi4c, query_regions)
combineUMI4C(umi4c, query_regions)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`query_regions`	`GRanges` object containing the coordinates of the genomic regions for combining restriction fragments.

Value

UMI4C object with rowRanges corresponding to query_regions and assay containing the sum of raw UMI counts at each specified query_region.

Examples

data("ex_ciita_umi4c")

wins <- makeWindowFragments(ex_ciita_umi4c)
umi_comb <- combineUMI4C(ex_ciita_umi4c, wins)
data("ex_ciita_umi4c")

wins <- makeWindowFragments(ex_ciita_umi4c)
umi_comb <- combineUMI4C(ex_ciita_umi4c, wins)

UMI4C Contacts Processing

Description

Using demultiplexed FastQ files as input, performs all necessary steps to end up with a tsv file summarizing the restriction enzyme fragments and the number of UMIs supporting that specific contact with the viewpoint (bait) of interest.

Usage

contactsUMI4C(
  fastq_dir,
  wk_dir,
  file_pattern = NULL,
  bait_seq,
  bait_pad,
  res_enz,
  cut_pos,
  digested_genome,
  bowtie_index,
  threads = 1,
  numb_reads = 1e+09,
  rm_tmp = TRUE,
  min_flen = 20,
  filter_bp = 1e+07,
  ref_gen,
  sel_seqname = NULL
)
contactsUMI4C(
  fastq_dir,
  wk_dir,
  file_pattern = NULL,
  bait_seq,
  bait_pad,
  res_enz,
  cut_pos,
  digested_genome,
  bowtie_index,
  threads = 1,
  numb_reads = 1e+09,
  rm_tmp = TRUE,
  min_flen = 20,
  filter_bp = 1e+07,
  ref_gen,
  sel_seqname = NULL
)

Arguments

`fastq_dir`	Path of the directory containing the FastQ files (compressed or uncompressed).
`wk_dir`	Working directory where to save the outputs generated by the UMI-4c analysis.
`file_pattern`	Character that can be used to filter the files you want to analyze in the `fastq_dir`.
`bait_seq`	Character containing the bait primer sequence.
`bait_pad`	Character containing the pad sequence (sequence between the bait primer and the restriction enzyme sequence).
`res_enz`	Character containing the restriction enzyme sequence.
`cut_pos`	Numeric indicating the nucleotide position where restriction enzyme cuts (zero-based) (for example, for DpnII is 0).
`digested_genome`	Path for the digested genome file generated using the `digestGenome` function.
`bowtie_index`	Path and prefix of the bowtie index to use for the alignment.
`threads`	Number of threads to use in the analysis. Default=1.
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=1e9.
`rm_tmp`	Logical indicating whether to remove temporary files (sam and intermediate bams). TRUE or FALSE. Default=TRUE.
`min_flen`	Minimal fragment length to use for selecting the fragments. Default=20
`filter_bp`	Integer indicating the bp upstream and downstream of the viewpoint to select for further analysis. Default=10e6
`ref_gen`	A BSgenome object of the reference genome.
`sel_seqname`	A character with the chromosome name to focus the search for the viewpoint sequence.

Value

This function is a combination of calls to other functions that perform the necessary steps for processing UMI-4C data.

Examples

if (interactive()) {
path <- downloadUMI4CexampleData()

hg19_dpnii <- digestGenome(
    cut_pos = 0,
    res_enz = "GATC",
    name_RE = "DpnII",
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    out_path = file.path(path, "digested_genome")
)


raw_dir <- file.path(path, "CIITA", "fastq")

contactsUMI4C(
    fastq_dir = raw_dir,
    wk_dir = file.path(path, "CIITA"),
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC",
    cut_pos = 0,
    digested_genome = hg19_dpnii,
    bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16"),
    threads = 1,
    numb_reads = 1e9,
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    sel_seqname = "chr16"
)

unlink(path, recursive=TRUE)
}
if (interactive()) {
path <- downloadUMI4CexampleData()

hg19_dpnii <- digestGenome(
    cut_pos = 0,
    res_enz = "GATC",
    name_RE = "DpnII",
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    out_path = file.path(path, "digested_genome")
)


raw_dir <- file.path(path, "CIITA", "fastq")

contactsUMI4C(
    fastq_dir = raw_dir,
    wk_dir = file.path(path, "CIITA"),
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC",
    cut_pos = 0,
    digested_genome = hg19_dpnii,
    bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16"),
    threads = 1,
    numb_reads = 1e9,
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    sel_seqname = "chr16"
)

unlink(path, recursive=TRUE)
}

UMI counting

Description

Algorithm for counting and collapsing the number of UMIs supporting a specific ligation.

Usage

counterUMI4C(
  wk_dir,
  pos_viewpoint,
  res_enz,
  digested_genome,
  filter_bp = 1e+07
)
counterUMI4C(
  wk_dir,
  pos_viewpoint,
  res_enz,
  digested_genome,
  filter_bp = 1e+07
)

Arguments

`wk_dir`	Working directory where to save the outputs generated by the UMI-4c analysis.
`pos_viewpoint`	GRanges object containing the genomic position of the viewpoint.
`res_enz`	Character containing the restriction enzyme sequence.
`digested_genome`	Path for the digested genome file generated using the `digestGenome` function.
`filter_bp`	Integer indicating the bp upstream and downstream of the viewpoint to select for further analysis. Default=10e6.

Details

For collapsing different molecules into the same UMI, takes into account the ligation position and the number of UMI sequence mismatches.

Value

Creates a compressed tab-delimited file in wk_dir/count named "basename(fastq) _counts.tsv.gz", containing the coordinates for the viewpoint fragment, contact fragment and the number of UMIs detected in the ligation.

Examples

if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)

hg19_dpnii <- digestGenome(
    cut_pos = 0,
    res_enz = "GATC",
    name_RE = "DpnII",
    sel_chr = "chr16", # digest only chr16 to make example faster
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    out_path = file.path(path, "digested_genome")
)

viewpoint <- GenomicRanges::GRanges("chr16:10972515-10972548")

counterUMI4C(
    wk_dir = file.path(path, "CIITA"),
    pos_viewpoint = viewpoint,
    res_enz = "GATC",
    digested_genome = hg19_dpnii
)

}
if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)

hg19_dpnii <- digestGenome(
    cut_pos = 0,
    res_enz = "GATC",
    name_RE = "DpnII",
    sel_chr = "chr16", # digest only chr16 to make example faster
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    out_path = file.path(path, "digested_genome")
)

viewpoint <- GenomicRanges::GRanges("chr16:10972515-10972548")

counterUMI4C(
    wk_dir = file.path(path, "CIITA"),
    pos_viewpoint = viewpoint,
    res_enz = "GATC",
    digested_genome = hg19_dpnii
)

}

Create gene annotation object

Description

Create gene annotation object

Usage

createGeneAnnotation(
  window,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE
)
createGeneAnnotation(
  window,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE
)

Arguments

`window`	GRanges object with coordinates to use for selecting the genes to plot.
`TxDb`	TxDb object to use for drawing the genomic annotation.
`longest`	Logical indicating whether to plot only the longest transcripts for each gene in the gene annotation plot.

Value

GRanges object with the gene annotation in the window.

Examples

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
window <- GRanges("chr16:11298262-11400036")
gene_anno <- createGeneAnnotation(
    window = window,
    TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene
)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
window <- GRanges("chr16:11298262-11400036")
gene_anno <- createGeneAnnotation(
    window = window,
    TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene
)

Create stats table

Description

Create a statistical summary of the UMI-4C experiments analyzed with contactsUMI4C.

Usage

createStatsTable(wk_dir)
createStatsTable(wk_dir)

Arguments

wk_dir

Working directory where to save the outputs generated by the UMI-4c analysis.

Value

Returns a data.frame summarizing all the different statistics for each sample analyzed in wk_dir.

Darken colors

Description

Darken colors

Usage

darken(color, factor = 1.4)
darken(color, factor = 1.4)

Arguments

`color`	Character containing the name or hex value of a color.
`factor`	Numeric representing a factor by which darken the specified color.

Value

Darkens the provided color by the provided factor.

Examples

darken("blue", factor = 1.4)
darken("blue", factor = 1.4)

DDS object to UMI4Cats object.

Description

Transforms an DDS object to a UMI4C object after applying nbinomWaldTestUMI4C.

Usage

dds2UMI4C(
  umi4c,
  dds,
  normalized = TRUE,
  padj_method = "fdr",
  padj_threshold = 0.05
)
dds2UMI4C(
  umi4c,
  dds,
  normalized = TRUE,
  padj_method = "fdr",
  padj_threshold = 0.05
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`dds`	DDS object as generated by `nbinomWaldTestUMI4C` with the DESeq2 Wald Test results
`normalized`	Logical indicating if the function should return normalized or raw UMI counts. Default: TRUE.
`padj_method`	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.05.

Value

UMI4C object with the DESeq2 Wald Test results.

Demultiplex FASTQ files using fastq-multx

Description

Demultiplex FASTQ files containng different bait information

Usage

demultiplexFastq(barcodes, fastq, out_path = "raw_fastq", numb_reads = 1e+11)
demultiplexFastq(barcodes, fastq, out_path = "raw_fastq", numb_reads = 1e+11)

Arguments

`barcodes`	Dataframe with "name of sample" and "barcode" for every sample to demultiplex.
`fastq`	Fastq to demultiplex containing mate 1s. Different pairs should be named as "_R1" or "_R2". Allowed formats: _R1.fastq.gz, _R1.fq.gz, _R1.fastq or _R1.fq.
`out_path`	Path where to save the demultiplex output. Defaults to a path named `raw_fastq` in your working directory.
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=10e10.

Value

Paired-end FastQ files demultiplexed in a compressed format. A log file with the statistics is also generated in out_path named barcode_umi4cats_demultiplexFastq_stats.txt.

Examples

## Not run: 
path <- downloadUMI4CexampleData(use_sample = TRUE)
fastq <- file.path(path, "CIITA", "fastq", "sub_ctrl_hi19_CIITA_R1.fastq.gz")
barcodes <- data.frame(
    sample = c("CIITA"),
    barcode = c("GGACAAGCTCCCTGCAACTCA")
)

demultiplexFastq(
    barcodes = barcodes,
    fastq = fastq,
    out_path = path
)

## End(Not run)

## Not run: 
path <- downloadUMI4CexampleData(use_sample = TRUE)
fastq <- file.path(path, "CIITA", "fastq", "sub_ctrl_hi19_CIITA_R1.fastq.gz")
barcodes <- data.frame(
    sample = c("CIITA"),
    barcode = c("GGACAAGCTCCCTGCAACTCA")
)

demultiplexFastq(
    barcodes = barcodes,
    fastq = fastq,
    out_path = path
)

## End(Not run)

UMI4C class methods

Description

This page contains a summary of the different methods used to access the information contained inside the UMI4C-class object. See the details section for more information on the different accessors.

Usage

dgram(object)

dgram(object) <- value

groupsUMI4C(object, value)

groupsUMI4C(object) <- value

bait(object)

trend(object)

resultsUMI4C(object, format = "GRanges", counts = TRUE, ordered = FALSE)

## S4 method for signature 'UMI4C'
dgram(object)

## S4 replacement method for signature 'UMI4C'
dgram(object) <- value

## S4 method for signature 'UMI4C'
groupsUMI4C(object)

## S4 replacement method for signature 'UMI4C'
groupsUMI4C(object) <- value

## S4 method for signature 'UMI4C'
bait(object)

## S4 method for signature 'UMI4C'
trend(object)

## S4 method for signature 'UMI4C'
resultsUMI4C(object, format = "GRanges", counts = FALSE, ordered = FALSE)
dgram(object)

dgram(object) <- value

groupsUMI4C(object, value)

groupsUMI4C(object) <- value

bait(object)

trend(object)

resultsUMI4C(object, format = "GRanges", counts = TRUE, ordered = FALSE)

## S4 method for signature 'UMI4C'
dgram(object)

## S4 replacement method for signature 'UMI4C'
dgram(object) <- value

## S4 method for signature 'UMI4C'
groupsUMI4C(object)

## S4 replacement method for signature 'UMI4C'
groupsUMI4C(object) <- value

## S4 method for signature 'UMI4C'
bait(object)

## S4 method for signature 'UMI4C'
trend(object)

## S4 method for signature 'UMI4C'
resultsUMI4C(object, format = "GRanges", counts = FALSE, ordered = FALSE)

Arguments

`object`	a `UMI4C-class` object.
`value`	Alternative list of dgrams to replace the current slot.
`format`	Either "GRanges" (default) or "data.frame", indicating the format output of the results.
`counts`	Logical indicating whether counts for the different region should be provided. Default: FALSE.
`ordered`	Logical indicating whether to sort output by significance (adjusted p-value). Default: FALSE.

Value

There are several accessors to easily retrive information from a UMI4C-class object:

dgram: Returns a named list with the output domainograms for each sample.
bait: Returns a GRanges object with the position of the bait.
trend: Returns a data.frame in long format with the values of the adapative smoothen trend.
resultsUMI4C: Returns a GRanges or data.frame with the results of the differential analysis.

Examples

# Access the different information inside the UMI4C object
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

dgram(ex_ciita_umi4c)
bait(ex_ciita_umi4c)
head(trend(ex_ciita_umi4c))

# Perform differential test
enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
resultsUMI4C(umi_dif)
# Access the different information inside the UMI4C object
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

dgram(ex_ciita_umi4c)
bait(ex_ciita_umi4c)
head(trend(ex_ciita_umi4c))

# Perform differential test
enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
resultsUMI4C(umi_dif)

Differential UMI4C contacts using DESeq2 Wald Test

Description

Using a UMI4C object, infers the differences between conditions specified in design of the smooth monotone fitted values using a Wald Test from DESeq2 package.

Usage

differentialNbinomWaldTestUMI4C(
  umi4c,
  design = ~condition,
  normalized = TRUE,
  padj_method = "fdr",
  query_regions = NULL,
  padj_threshold = 0.05,
  penalty = 0.1,
  alpha = 20
)
differentialNbinomWaldTestUMI4C(
  umi4c,
  design = ~condition,
  normalized = TRUE,
  padj_method = "fdr",
  query_regions = NULL,
  padj_threshold = 0.05,
  penalty = 0.1,
  alpha = 20
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`design`	A `formula` or `matrix`. The formula expresses how the counts for each fragment end depend on the variables in `colData`. See `DESeqDataSet`.
`normalized`	Logical indicating if the function should return normalized or raw UMI counts. Default: TRUE.
`padj_method`	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
`query_regions`	`GRanges` object or `data.frame` containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.05.
`penalty`	Amount of smoothing to be applied to the estimated functional parameter. Default: 0.1.
`alpha`	Approximate number of fragments desired for every basis function of the B-spline basis. `floor((max(number of fragments)) / alpha)` is passed to `create.bspline.basis` as nbasis argument. 4 is the minimum allowed value. Default: 20.

Details

This function fits the signal trend of a variance stabilized count values using a symmetric monotone fit for the distance dependency. Then scales the raw counts across the samples to obtain normalized factors. Finally, it detects differences between conditions applying the DESeq2 Wald Test.

Value

UMI4C object with the DESeq2 Wald Test results.

Examples

## Not run: 
 files <- list.files(system.file("extdata", "CIITA", "count", package="UMI4Cats"),
                     pattern = "*_counts.tsv.gz",
                     full.names = TRUE
 )
# Create colData including all relevant information
colData <- data.frame(
  sampleID = gsub("_counts.tsv.gz", "", basename(files)),
  file = files,
  stringsAsFactors = FALSE
)

library(tidyr)
colData <- colData %>%
  separate(sampleID,
           into = c("condition", "replicate", "viewpoint"),
           remove = FALSE
  )
  
# Make UMI-4C object including grouping by condition
umi <- makeUMI4C(
  colData = colData,
  viewpoint_name = "CIITA",
  grouping = NULL,
  bait_expansion = 2e6
)

umi_wald <- differentialNbinomWaldTestUMI4C(umi4c=umi,
                                            design=~condition,
                                            alpha = 100)

## End(Not run)
## Not run: 
 files <- list.files(system.file("extdata", "CIITA", "count", package="UMI4Cats"),
                     pattern = "*_counts.tsv.gz",
                     full.names = TRUE
 )
# Create colData including all relevant information
colData <- data.frame(
  sampleID = gsub("_counts.tsv.gz", "", basename(files)),
  file = files,
  stringsAsFactors = FALSE
)

library(tidyr)
colData <- colData %>%
  separate(sampleID,
           into = c("condition", "replicate", "viewpoint"),
           remove = FALSE
  )
  
# Make UMI-4C object including grouping by condition
umi <- makeUMI4C(
  colData = colData,
  viewpoint_name = "CIITA",
  grouping = NULL,
  bait_expansion = 2e6
)

umi_wald <- differentialNbinomWaldTestUMI4C(umi4c=umi,
                                            design=~condition,
                                            alpha = 100)

## End(Not run)

Digest reference genome

Description

Performs an in silico digestion of a given reference genome using a given restriction enzyme sequence.

Usage

digestGenome(
  res_enz,
  cut_pos,
  name_RE,
  ref_gen,
  sel_chr = paste0("chr", c(seq_len(22), "X", "Y")),
  out_path = "digested_genome/"
)
digestGenome(
  res_enz,
  cut_pos,
  name_RE,
  ref_gen,
  sel_chr = paste0("chr", c(seq_len(22), "X", "Y")),
  out_path = "digested_genome/"
)

Arguments

`res_enz`	Character containing the restriction enzyme sequence.
`cut_pos`	Numeric indicating the nucleotide position where restriction enzyme cuts (zero-based) (for example, for DpnII is 0).
`name_RE`	Restriction enzyme name.
`ref_gen`	A BSgenome object of the reference genome.
`sel_chr`	Character vector indicating which chromosomes to select for the digestion. Default: chr1-22, chrX, chrY.
`out_path`	Output path where to save the genomic track. The default is a directory named `digested_genome/` created in your working directory. The rda objects are saved in folder named by the `ref_gene`_`name_RE` in the `out_path` folder.

Value

Creates a rda file for every chromosome defined in sel_chr.

Examples

library(BSgenome.Hsapiens.UCSC.hg19)
ref_gen <- BSgenome.Hsapiens.UCSC.hg19

hg19_dpnii <- digestGenome(
    res_enz = "GATC",
    cut_pos = 0,
    name_RE = "dpnII",
    sel_chr = "chr16", # Only in chr16 to reduce example running time
    ref_gen = ref_gen,
    out_path = file.path(tempdir(), "digested_genome/")
)
library(BSgenome.Hsapiens.UCSC.hg19)
ref_gen <- BSgenome.Hsapiens.UCSC.hg19

hg19_dpnii <- digestGenome(
    res_enz = "GATC",
    cut_pos = 0,
    name_RE = "dpnII",
    sel_chr = "chr16", # Only in chr16 to reduce example running time
    ref_gen = ref_gen,
    out_path = file.path(tempdir(), "digested_genome/")
)

Download UMI4Cats example datasets

Description

Downloads the required UMI4Cats example datasets.

Usage

downloadUMI4CexampleData(out_dir = tempdir(), verbose = TRUE, reduced = FALSE)
downloadUMI4CexampleData(out_dir = tempdir(), verbose = TRUE, reduced = FALSE)

Arguments

`out_dir`	Output directory for the datasets, defaults to tempdir().
`verbose`	Whether to print verbose messages or not. Default: TRUE.
`reduced`	Whether to use a reduced dataset to make test functions run faster.

Value

It creates the output_dir with the example UMI-4C files used by the vignette and examples. Takes advantage of the BiocFileCache package to make sure that the file has not been previously downloaded by the user.

Examples

if (interactive()) {
 # Using reduced data data to make example faster.
# Remove reduced=TRUE or set to FALSE to
# download the full dataset.

path <- downloadUMI4CexampleData(reduced = TRUE)
}
if (interactive()) {
 # Using reduced data data to make example faster.
# Remove reduced=TRUE or set to FALSE to
# download the full dataset.

path <- downloadUMI4CexampleData(reduced = TRUE)
}

Contacs with CIITA promoter

Description

An example UMI4C object showing the contacts with a viewpoint located at the CIITA gene promoter.

Usage

ex_ciita_umi4c
ex_ciita_umi4c

Format

A UMI4C object from this package.

Source

See inst/script/CIITA_process_example.R to see the code use for generating the UMI4C object.

Differential UMI4C contacts using Fisher's Exact test

Description

Using the UMIs inside query_regions performs Fisher's Exact test to calculate significant differences between contact intensities.

Usage

fisherUMI4C(
  umi4c,
  grouping = "condition",
  query_regions,
  resize = NULL,
  window_size = 5000,
  filter_low = 50,
  padj_method = "fdr",
  padj_threshold = 0.05
)
fisherUMI4C(
  umi4c,
  grouping = "condition",
  query_regions,
  resize = NULL,
  window_size = 5000,
  filter_low = 50,
  padj_method = "fdr",
  padj_threshold = 0.05
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`grouping`	Name of the column in colData used to merge the samples or replicates. If none available or want to add new groupings, run `addGrouping`. Default: "condition".
`query_regions`	`GenomicRanges` object or `data.frame` containing the region coordinates used to perform the differential analysis.
`resize`	Width in base pairs for resizing the `query_regions`. Default: no resizing.
`window_size`	If `query_regions` are not defined, wil bin region in `window_size` bp and perform the analysis using this windows.
`filter_low`	Either the minimum median UMIs requiered to perform Fisher's Exact test or `FALSE` for performing the test in all windows.
`padj_method`	Method for adjusting p-values. See `p.adjust` for the different methods.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts.

Details

This function calculates the overlap of fragment ends with either the provided query_regions or the binned region using window_size. The resulting number of UMIs in each query_region will be the sum of UMIs in all overlapping fragment ends. As a default, will filter out those regions whose median UMIs are lower than filter_low.

Finally, a contingency table for each query_reegions or window that passed the filter_low filter is created as follows:

	query_region	region
Reference	n1	N1-n1
Condition	n2	N2-n2

and the Fisher's Exact test is performed. Obtained p-values are then converted to adjusted p-values using padj_method and the results list is added to the UMI4C object.

Value

Calculates statistical differences between UMI-4C experiments.

Examples

data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

# Perform differential test
enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
resultsUMI4C(umi_dif)                        
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

# Perform differential test
enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
resultsUMI4C(umi_dif)

Format plots for UMI4C

Description

Format plots for UMI4C

Usage

formatPlotsUMI4C(plot_list, font_size)
formatPlotsUMI4C(plot_list, font_size)

Arguments

`plot_list`	List of plots generated by `plotUMI4C`
`font_size`	Base font size to use for the UMI4C plot. Default: 14.

Value

Given a named plot_list and considering the number and type of included plots, formats their axes accordingly to show the final UMI4C plot.

Get geometric mean of given coordinates

Description

Get geometric mean of given coordinates

Usage

geoMeanCoordinates(coords, scale, bait_start)
geoMeanCoordinates(coords, scale, bait_start)

Arguments

`coords`	Vector of integers representing the coordinates from which to obtain the geometric mean.
`scale`	Vector of scales indicating how many fragment where merged.
`bait_start`	Integer indicating the coordinates for the bait start.

Value

Calculates geometric mean of the provided coordinates, taking into account the distance to the viewpoint and how many restriction fragments are being merged.

Get default colors

Description

Get default colors

Usage

getColors(factors)
getColors(factors)

Arguments

factors

Name of the factors that will be used for grouping variables.

Value

Depending on the number of factors it creates different color palettes.

Get factors fro plotting

Description

Get factors fro plotting

Usage

getFactors(umi4c, grouping = NULL)
getFactors(umi4c, grouping = NULL)

Arguments

`umi4c`	UMI4C object
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.

Value

Factor vector where the first element is the reference factor.

Get normalization matrix

Description

Will return a normalization matrix.

Usage

getNormalizationMatrix(
  umi4c,
  norm_bins = 10^(3:6),
  post_smooth_win = 50,
  r_expand = 1.2
)
getNormalizationMatrix(
  umi4c,
  norm_bins = 10^(3:6),
  post_smooth_win = 50,
  r_expand = 1.2
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`norm_bins`	Numeric vector with the genomic bins to use for normalization. Default: 1K, 10K, 100K, 1Mb.
`post_smooth_win`	Numeric indicating the smoothing window to use. Default: 50.
`r_expand`	Numeric indicanting the expansion value for normalization. Default: 1.2.

Value

Creates a matrix of normalization factors using as a reference the profile specified in the UMI4C object.

Get significant interactions from a GRangesList

Description

Retrieves all significant interactions from the output of callInteractions.

Usage

getSignInteractions(gr_interactions)
getSignInteractions(gr_interactions)

Arguments

gr_interactions

GRangesList outputed by callInteractions.

Value

GRanges object with a list of significantly interacting regions.

Examples

data("ex_ciita_umi4c")
umi <- ex_ciita_umi4c
win_frags <- makeWindowFragments(umi, n_frags=8, sliding=1)

gr <- callInteractions(umi, ~condition, win_frags, padj_threshold = 0.01, zscore_threshold=2)
inter <- getSignInteractions(gr)
data("ex_ciita_umi4c")
umi <- ex_ciita_umi4c
win_frags <- makeWindowFragments(umi, n_frags=8, sliding=1)

gr <- callInteractions(umi, ~condition, win_frags, padj_threshold = 0.01, zscore_threshold=2)
inter <- getSignInteractions(gr)

Get viewpoint coordinates

Description

Finds the viewpoint coordinates for a given reference genome and sequence.

Usage

getViewpointCoordinates(
  bait_seq,
  bait_pad,
  res_enz,
  ref_gen,
  sel_seqname = NULL
)
getViewpointCoordinates(
  bait_seq,
  bait_pad,
  res_enz,
  ref_gen,
  sel_seqname = NULL
)

Arguments

`bait_seq`	Character containing the bait primer sequence.
`bait_pad`	Character containing the pad sequence (sequence between the bait primer and the restriction enzyme sequence).
`res_enz`	Character containing the restriction enzyme sequence.
`ref_gen`	A BSgenome object of the reference genome.
`sel_seqname`	A character with the chromosome name to focus the search for the viewpoint sequence.

Value

Creates a GRanges object containing the genomic position of the viewpoint.

Examples

getViewpointCoordinates(
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC",
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    sel_seqname = "chr16" # Look only in chr16
)
getViewpointCoordinates(
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC",
    ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
    sel_seqname = "chr16" # Look only in chr16
)

Group UMI4C samples

Description

Combines UMI4C samples by adding UMIs from assay(umi4c) to represent the levels in grouping.

Usage

groupSamplesUMI4C(umi4c, grouping = "condition")
groupSamplesUMI4C(umi4c, grouping = "condition")

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`grouping`	Name of the column in colData used to merge the samples or replicates. Set to NULL for skipping grouping. Default: "condition".

Value

A grouped UMI4C object.

Make windows merging restriction fragments

Description

Use a set of continuous restriction fragments to generate windows containing a fixed number of fragments (n_frags).

Usage

makeWindowFragments(input, n_frags = 8, sliding = 1)
makeWindowFragments(input, n_frags = 8, sliding = 1)

Arguments

`input`	Input object containing the restriction fragments. Should be class UMI4C (rowRanges will be extracted) or class GRanges.
`n_frags`	Number of fragments to use for generating the windows. This should include restriction fragments with 0 counts (Default: 8).
`sliding`	Numeric indicating the factor for generating sliding windows. If set to 1 (default) will use fixed windows. If set to > 0 and < 1 will use n_frags * sliding fragments to generate sliding windows.

Value

A GRanges object containing the windows of merged restriction fragments.

Examples

data("ex_ciita_umi4c")

# Without sliding windows
win_frags <- makeWindowFragments(ex_ciita_umi4c, n_frags=30, sliding=1)
win_frags

# With sliding windows (n_frags*sliding)
win_frags <- makeWindowFragments(ex_ciita_umi4c, n_frags=30, sliding=0.5)
win_frags
data("ex_ciita_umi4c")

# Without sliding windows
win_frags <- makeWindowFragments(ex_ciita_umi4c, n_frags=30, sliding=1)
win_frags

# With sliding windows (n_frags*sliding)
win_frags <- makeWindowFragments(ex_ciita_umi4c, n_frags=30, sliding=0.5)
win_frags

Differential UMI4C contacts using DESeq2 Wald Test

Description

Takes the smooth monotone fit count values and infers the differential UMI4C contacts using DESeq2 Wald Test from DESeq2 package.

Usage

nbinomWaldTestUMI4C(dds, query_regions = NULL)
nbinomWaldTestUMI4C(dds, query_regions = NULL)

Arguments

`dds`	DDS object as generated by `smoothMonotoneUMI4C` with the smooth monotone fit counts
`query_regions`	`GRanges` object or `data.frame` containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.

Details

This function back-transform fitted values to the scale of raw counts and scale them across the samples to obtain normalized factors using normalizationFactors from DESeq2 package. To detect differences between conditions, the DESeq2

Value

DDS object with the DESeq2 Wald Test results, with results columns accessible with the results function.

Plot differential contacts

Description

Plot differential contacts

Usage

plotDifferential(umi4c, grouping = NULL, colors = NULL, xlim = NULL)
plotDifferential(umi4c, grouping = NULL, colors = NULL, xlim = NULL)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.
`colors`	Named vector of colors to use for plotting for each group.
`xlim`	Limits for the plot x axis (genomic coordinates).

Value

Produces a plot of the fold changes at the differential regions analyzed ghat are contained in the UMI4C object.

Examples

data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
plotDifferential(umi_dif)
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

enh <- GRanges(c("chr16:10925006-10928900", "chr16:11102721-11103700"))
umi_dif <- fisherUMI4C(ex_ciita_umi4c, query_regions = enh, 
                       filter_low = 20, resize = 5e3)
plotDifferential(umi_dif)

Plot domainogram

Description

Plot domainogram

Usage

plotDomainogram(
  umi4c,
  dgram_function = "quotient",
  grouping = NULL,
  colors = NULL,
  xlim = NULL
)
plotDomainogram(
  umi4c,
  dgram_function = "quotient",
  grouping = NULL,
  colors = NULL,
  xlim = NULL
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`dgram_function`	Function used for calculating the fold-change in the domainogram plot, either "difference" or "quotient". Default: "quotient".
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.
`colors`	Named vector of colors to use for plotting for each group.
`xlim`	Limits for the plot x axis (genomic coordinates).

Value

Produces the domainogram plot, summarizing the merged number of UMIs at the different scales analyzed (y axis).

Examples

data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

plotDomainogram(ex_ciita_umi4c, grouping = "condition")
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

plotDomainogram(ex_ciita_umi4c, grouping = "condition")

Plot genes

Description

Plot genes in a window of interest.

Usage

plotGenes(
  window,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  xlim = NULL,
  font_size = 14
)
plotGenes(
  window,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  xlim = NULL,
  font_size = 14
)

Arguments

`window`	GRanges object with coordinates to use for selecting the genes to plot.
`TxDb`	TxDb object to use for drawing the genomic annotation.
`longest`	Logical indicating whether to plot only the longest transcripts for each gene in the gene annotation plot.
`xlim`	Limits for the plot x axis (genomic coordinates).
`font_size`	Base font size to use for the UMI4C plot. Default: 14.

Value

Produces a plot with the genes found in the provided window.

Examples

window <- GRanges("chr16:11348649-11349648")
plotGenes(
    window = window,
    TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
)
window <- GRanges("chr16:11348649-11349648")
plotGenes(
    window = window,
    TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
)

Plot interactions

Description

Plot the results of callInteractions.

Usage

plotInteractions(gr_interactions, xlim = NULL, significant = TRUE)
plotInteractions(gr_interactions, xlim = NULL, significant = TRUE)

Arguments

`gr_interactions`	GRangesList outputed by `callInteractions`.
`xlim`	Limits for the plot x axis (genomic coordinates).
`significant`	Logical indicating whether to plot only significant interactions (default: TRUE).

Value

Produces a ggplot2 plot showing the queried interactions at each analysed sample.

Plot Interactions UMI4C

Description

Plot the results of callInteractions together with the gene annotation and the trend.

Usage

plotInteractionsUMI4C(
  umi4c,
  gr_interactions,
  grouping = "condition",
  significant = TRUE,
  colors = NULL,
  xlim = NULL,
  ylim = NULL,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  rel_heights = c(0.25, 0.5, 0.25),
  font_size = 14
)
plotInteractionsUMI4C(
  umi4c,
  gr_interactions,
  grouping = "condition",
  significant = TRUE,
  colors = NULL,
  xlim = NULL,
  ylim = NULL,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  rel_heights = c(0.25, 0.5, 0.25),
  font_size = 14
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`gr_interactions`	GRangesList outputed by `callInteractions`.
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.
`significant`	Logical indicating whether to plot only significant interactions (default: TRUE).
`colors`	Named vector of colors to use for plotting for each group.
`xlim`	Limits for the plot x axis (genomic coordinates).
`ylim`	Limits of the trend y axis.
`TxDb`	TxDb object to use for drawing the genomic annotation.
`longest`	Logical indicating whether to plot only the longest transcripts for each gene in the gene annotation plot.
`rel_heights`	Numeric vector of length 3 or 4 (if differential plot) indicating the relative heights of each part of the UMI4C plot.
`font_size`	Base font size to use for the UMI4C plot. Default: 14.

Value

Combined plot with gene annotation, trend and interaction plot.

Plot adaptative smoothen trend

Description

Plot adaptative smoothen trend

Usage

plotTrend(umi4c, grouping = NULL, colors = NULL, xlim = NULL, ylim = NULL)
plotTrend(umi4c, grouping = NULL, colors = NULL, xlim = NULL, ylim = NULL)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.
`colors`	Named vector of colors to use for plotting for each group.
`xlim`	Limits for the plot x axis (genomic coordinates).
`ylim`	Limits of the trend y axis.

Value

Produces the adaptative trend plot, showing average UMIs at each position taking into account the minimum number of molecules used to merge restriction fragments.

Examples

data("ex_ciita_umi4c")

plotTrend(ex_ciita_umi4c)
data("ex_ciita_umi4c")

plotTrend(ex_ciita_umi4c)

Plot UMI4C data

Description

Produce a UMI-4C data plot containing the genes in the region, the adaptative smoothen trend and the domainogram.

Usage

plotUMI4C(
  umi4c,
  grouping = "condition",
  dgram_function = "quotient",
  dgram_plot = TRUE,
  colors = NULL,
  xlim = NULL,
  ylim = NULL,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  rel_heights = c(0.25, 0.4, 0.12, 0.23),
  font_size = 14
)
plotUMI4C(
  umi4c,
  grouping = "condition",
  dgram_function = "quotient",
  dgram_plot = TRUE,
  colors = NULL,
  xlim = NULL,
  ylim = NULL,
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  longest = TRUE,
  rel_heights = c(0.25, 0.4, 0.12, 0.23),
  font_size = 14
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C`.
`grouping`	Grouping used for the different samples. If none available or want to add new groupings, run `addGrouping`.
`dgram_function`	Function used for calculating the fold-change in the domainogram plot, either "difference" or "quotient". Default: "quotient".
`dgram_plot`	Logical indicating whether to plot the domainogram. If the UMI4C object only contains one sample will be automatically set to FALSE. Default: TRUE.
`colors`	Named vector of colors to use for plotting for each group.
`xlim`	Limits for the plot x axis (genomic coordinates).
`ylim`	Limits of the trend y axis.
`TxDb`	TxDb object to use for drawing the genomic annotation.
`longest`	Logical indicating whether to plot only the longest transcripts for each gene in the gene annotation plot.
`rel_heights`	Numeric vector of length 3 or 4 (if differential plot) indicating the relative heights of each part of the UMI4C plot.
`font_size`	Base font size to use for the UMI4C plot. Default: 14.

Value

Produces a summary plot with all the information contained in the UMI4C opject.

Examples

data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

plotUMI4C(ex_ciita_umi4c,
    dgram_plot = FALSE
)
data("ex_ciita_umi4c")
ex_ciita_umi4c <- addGrouping(ex_ciita_umi4c, grouping="condition")

plotUMI4C(ex_ciita_umi4c,
    dgram_plot = FALSE
)

Prepare UMI4C data

Description

Prepare the FastQ files for the further analysis by selecting reads with bait and adding the respective UMI identifier for each read in its header.

Usage

prepUMI4C(
  fastq_dir,
  wk_dir,
  file_pattern = NULL,
  bait_seq,
  bait_pad,
  res_enz,
  numb_reads = 1e+09
)
prepUMI4C(
  fastq_dir,
  wk_dir,
  file_pattern = NULL,
  bait_seq,
  bait_pad,
  res_enz,
  numb_reads = 1e+09
)

Arguments

`fastq_dir`	Path of the directory containing the FastQ files (compressed or uncompressed).
`wk_dir`	Working directory where to save the outputs generated by the UMI-4c analysis.
`file_pattern`	Character that can be used to filter the files you want to analyze in the `fastq_dir`.
`bait_seq`	Character containing the bait primer sequence.
`bait_pad`	Character containing the pad sequence (sequence between the bait primer and the restriction enzyme sequence).
`res_enz`	Character containing the restriction enzyme sequence.
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=1e9.

Value

Creates a compressed FASTQ file in wk_dir/prep named basename(fastq)).fq.gz, containing the filtered reads with the UMI sequence in the header. A log file with the statistics is also generated in wk_dir/logs named umi4c_stats.txt.

Examples

if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)
raw_dir <- file.path(path, "CIITA", "fastq")

prepUMI4C(
    fastq_dir = raw_dir,
    wk_dir = file.path(path, "CIITA"),
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC"
)
}
if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)
raw_dir <- file.path(path, "CIITA", "fastq")

prepUMI4C(
    fastq_dir = raw_dir,
    wk_dir = file.path(path, "CIITA"),
    bait_seq = "GGACAAGCTCCCTGCAACTCA",
    bait_pad = "GGACTTGCA",
    res_enz = "GATC"
)
}

Monotone smoothing of the DDS object VST counts

Description

Takes the variance stabilized count values and calculates a symmetric monotone fit for the distance dependency. The signal trend is fitted using the fda package. The position information about the viewpoint have to be stored in the metadata as metadata(dds)[['bait']].

Usage

smoothMonotoneUMI4C(dds, alpha = 20, penalty = 0.1)
smoothMonotoneUMI4C(dds, alpha = 20, penalty = 0.1)

Arguments

`dds`	DDS object as generated by `vstUMI4C` with the variance stabilized count values.
`alpha`	Approximate number of fragments desired for every basis function of the B-spline basis. `floor((max(number of fragments)) / alpha)` is passed to `create.bspline.basis` as nbasis argument. 4 is the minimum allowed value. Default: 20.
`penalty`	Amount of smoothing to be applied to the estimated functional parameter. Default: 0.1.

Details

This function computes the smoothing function for the VST values, based on fda package, and calculates a symmetric monotone fit counts for the distance dependency

Value

DDS object with monotone smoothed fit counts.

Split UMI4C reads

Description

Split the prepared reads using the restrition enzyme information.

Usage

splitUMI4C(wk_dir, res_enz, cut_pos, numb_reads = 1e+09, min_flen = 20)
splitUMI4C(wk_dir, res_enz, cut_pos, numb_reads = 1e+09, min_flen = 20)

Arguments

`wk_dir`	Working directory where to save the outputs generated by the UMI-4c analysis.
`res_enz`	Character containing the restriction enzyme sequence.
`cut_pos`	Numeric indicating the nucleotide position where restriction enzyme cuts (zero-based) (for example, for DpnII is 0).
`numb_reads`	Number of lines from the FastQ file to load in each loop. If having memory size problems, change it to a smaller number. Default=1e9.
`min_flen`	Minimal fragment length to use for selecting the fragments. Default=20

Value

Creates a compressed FASTQ file in wk_dir/split named basename(fastq)).fq.gz, containing the split reads based on the restriction enzyme used.

Examples

if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)

splitUMI4C(
    wk_dir = file.path(path, "CIITA"),
    res_enz = "GATC",
    cut_pos = 0
)
}
if (interactive()) {
path <- downloadUMI4CexampleData(reduced = TRUE)

splitUMI4C(
    wk_dir = file.path(path, "CIITA"),
    res_enz = "GATC",
    cut_pos = 0
)
}

Statistics UMI4C

Description

Creates a stats summary file and generates a summary plot describing statistics for processed UMI-4C samples.

Usage

statsUMI4C(wk_dir)
statsUMI4C(wk_dir)

Arguments

wk_dir

Working directory where to save the outputs generated by the UMI-4c analysis.

Value

Returns a plot summarizing the main statistics of the processed UMI-4C experiments found in wk_dir. Also, writes a file named stats_summary.txt in wk_dir/logs that summarizes all the values represented in the plot.

Examples

statsUMI4C(wk_dir = system.file("extdata", "CIITA",
    package = "UMI4Cats"
))
stats <- read.delim(system.file("extdata", "CIITA", "logs", "stats_summary.txt",
    package = "UMI4Cats"
))
head(stats)
statsUMI4C(wk_dir = system.file("extdata", "CIITA",
    package = "UMI4Cats"
))
stats <- read.delim(system.file("extdata", "CIITA", "logs", "stats_summary.txt",
    package = "UMI4Cats"
))
head(stats)

Theme

Description

Theme

Usage

theme(...)
theme(...)

Arguments

...

Additional arguments to pass to the theme call from ggplot2.

Value

ggplot2 theme.

Examples

library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    theme()
library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    theme()

Theme X blank

Description

Theme X blank

Usage

themeXblank(...)
themeXblank(...)

Arguments

...

Additional arguments to pass to the theme call from ggplot2.

Value

ggplot2 theme with a blank X axis.

Examples

library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeXblank()
library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeXblank()

Theme Y blank

Description

Theme Y blank

Usage

themeXYblank(...)
themeXYblank(...)

Arguments

...

Additional arguments to pass to the theme call from ggplot2.

Value

ggplot2 theme with a blank X and Y axis.

Examples

library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeXYblank()
library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeXYblank()

Theme Y blank

Description

Theme Y blank

Usage

themeYblank(...)
themeYblank(...)

Arguments

...

Additional arguments to pass to the theme call from ggplot2.

Value

ggplot2 theme with a blank Y axis.

Examples

library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeYblank()
library(ggplot2)

ggplot(
    iris,
    aes(Sepal.Length, Sepal.Width)
) +
    geom_point() +
    themeYblank()

UMI4C-class

Description

The UMI4C constructor is the function makeUMI4C. By using the arguments listed below, performs the necessary steps to analyze UMI-4C data and summarize it in an object of class UMI4C.

Usage

makeUMI4C(
  colData,
  viewpoint_name = "Unknown",
  grouping = "condition",
  normalized = TRUE,
  ref_umi4c = NULL,
  bait_exclusion = 3000,
  bait_expansion = 1e+06,
  scales = 5:150,
  min_win_factor = 0.02,
  sd = 2
)
makeUMI4C(
  colData,
  viewpoint_name = "Unknown",
  grouping = "condition",
  normalized = TRUE,
  ref_umi4c = NULL,
  bait_exclusion = 3000,
  bait_expansion = 1e+06,
  scales = 5:150,
  min_win_factor = 0.02,
  sd = 2
)

Arguments

`colData`	Data.frame containing the information for constructing the UMI4C experiment object. Needs to contain the following columns: sampleID. Unique identifier for the sample. condition. Condition for performing differential analysis. Can be control and treatment, two different cell types, etc. replicate. Number for identifying replicates. file. File as outputed by `umi4CatsContacts` function.
`viewpoint_name`	Character indicating the name for the used viewpoint.
`grouping`	Name of the column in colData used to merge the samples or replicates. Set to NULL for skipping grouping. Default: "condition".
`normalized`	Logical indicating whether UMI-4C profiles should be normalized to the `ref_umi4c` sample/group. Default: TRUE
`ref_umi4c`	Name of the sample or group to use as reference for normalization. By default is NULL, which means it will use the sample with less UMIs in the analyzed region. It should be a named vector, where the name corresponds to the grouping column from `colData` and the value represents the level to use as reference.
`bait_exclusion`	Region around the bait (in bp) to be excluded from the analysis. Default: 3000bp.
`bait_expansion`	Number of bp upstream and downstream of the bait to use for the analysis (region centered in bait). Default: 1Mb.
`scales`	Numeric vector containing the scales for calculating the domainogram.
`min_win_factor`	Proportion of UMIs that need to be found in a specific window for adaptative trend calculation
`sd`	Stantard deviation for adaptative trend.

Value

It returns an object of the class UMI4C.

Slots

colData

Data.frame containing the information for constructing the UMI4C experiment object. Needs to contain the following columns:

sampleID: Unique identifier for the sample.
condition: Condition for performing differential analysis. Can be control and treatment, two different cell types, etc.
replicate: Number or ID for identifying different replicates.
file: Path to the files outputed by contactsUMI4C.

rowRanges

GRanges object with the coordinates for the restriction fragment ends, their IDs and other additional annotation columns.

metadata

List containing the following elements:

bait: GRanges object representing the position of the bait used for the analysis.
scales: Numeric vector containing the scales used for calculating the domainogram.
min_win_factor: Factor for calculating the minimum molecules requiered in a window for not merging it with the next one when calculating the adaptative smoothing trend.
grouping: Columns in colData used for the different sample groupings, accessible through groupsUMI4C.
normalized: Logical indicating whether samples/groups are normalized or not.
region: GRanges with the coordinates of the genomic window used for analyzing UMI4C data.
ref_umi4c: Name of the sample or group used as reference for normalization.

assays

Matrix where each row represents a restriction fragment site and columns represent each sample or group defined in grouping. After running the makeUMI4C function, it will contain the following data:

umis: Raw number of UMIs detected by contactsUMI4C.
norm_mat: Normalization factors for each sample/group and fragment end.
trend: Adaptative smoothing trend of UMIs.
geo_coords: Geometric coordinates obtained when performing the adaptative smoothing.
scale: Scale selected for the adaptative smoothing.
sd: Stantard deviation for the adaptative smoothing trend.

dgram

List containing the domainograms for each sample. A domainogram is matrix where columns are different scales selected for merging UMI counts and rows are the restriction fragments.

groupsUMI4C

List of UMI4C objects with the specific groupings.

results

List containing the results for the differential analysis ran using fisherUMI4C.

Note

The UMI4C class extends the SummarizedExperiment class.

Examples

# Load sample processed file paths
files <- list.files(system.file("extdata", "CIITA", "count",
    package = "UMI4Cats"
),
pattern = "*_counts.tsv",
full.names = TRUE
)

# Create colData including all relevant information
colData <- data.frame(
    sampleID = gsub("_counts.tsv.gz", "", basename(files)),
    file = files,
    stringsAsFactors = FALSE
)

library(tidyr)
colData <- colData %>%
    separate(sampleID,
        into = c("condition", "replicate", "viewpoint"),
        remove = FALSE
    )

# Load UMI-4C data and generate UMI4C object
umi <- makeUMI4C(
    colData = colData,
    viewpoint_name = "CIITA",
    grouping = "condition"
)
# Load sample processed file paths
files <- list.files(system.file("extdata", "CIITA", "count",
    package = "UMI4Cats"
),
pattern = "*_counts.tsv",
full.names = TRUE
)

# Create colData including all relevant information
colData <- data.frame(
    sampleID = gsub("_counts.tsv.gz", "", basename(files)),
    file = files,
    stringsAsFactors = FALSE
)

library(tidyr)
colData <- colData %>%
    separate(sampleID,
        into = c("condition", "replicate", "viewpoint"),
        remove = FALSE
    )

# Load UMI-4C data and generate UMI4C object
umi <- makeUMI4C(
    colData = colData,
    viewpoint_name = "CIITA",
    grouping = "condition"
)

UMI4Cats object to DDS object.

Description

Transforms an UMI4C object to a DDS object

Usage

UMI4C2dds(umi4c, design = ~condition)
UMI4C2dds(umi4c, design = ~condition)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`design`	A `formula` or `matrix`. The formula expresses how the counts for each fragment end depend on the variables in `colData`. See `DESeqDataSet`.

Value

DDS object.

UMI4Cats: A package for analyzing UMI-4C chromatin contact data

Description

The UMI4Cats package provides functions for the pre-processing, analysis and visualization of UMI-4C chromatin contact data.

File preparation

There are two different functions that can be used to prepare the files for analyzing them with UMI4Cats:

demultiplexFastq. Demultiplex reads belonging to different viewpoints from a paired-end FastQ file.
digestGenome. Digest the reference genome of choice using a given restriction sequence.

Processing

The pre-processing functions are wrapped in the contactsUMI4C main function. This function will sequentially run the following steps:

prepUMI4C. Filter specific and high quality reads.
splitUMI4C. Split reads by the restriction sequence.
alignmentUMI4C. Align reads to the reference genome.
counterUMI4C. Apply UMI counting algorithm to quantify the interactions with the viewpoint.

The statistics from the samples analyzed with the contactsUMI4C function can be extracted and visualized with the function statsUMI4C.

Analysis

The analysis of UMI-4C data is wrapped in the construction of an object of UMI4C class by the creator function makeUMI4C. This function will group your samples according to the variable you provided in the grouping argument (default: "condition") and then normalize it to ref_umi4c.

Significant interactions with the viewpoint can be called when several replicates are available, using the callInteractions function. A set of query_regions, such as enhancers or open chromatin regions needs to be provided. When no candidate regions are available, one can use the function makeWindowFragments to join a fixed number of restriction fragments into windows. The results of this analysis can be visualized using plotInteractionsUMI4C and the list of significant interactions can be retrieved using getSignInteractions.

The differential analysis can be performed with fisherUMI4C or waldUMI4C functions and can be focused in a set of regions of interest by providing the query_regions argument. Both will return a UMI4C object containing the results of the differential test. You can access these results with the method resultsUMI4C.

Visualization

An integrative plot showing the results stored inside the UMI4C object can be generated with the function plotUMI4C.

Variance stabilizing transformation

Description

Using a DDS object performs a variance stabilizing transformation from DESeq2 package to the UMI4C counts

Usage

vstUMI4C(dds)
vstUMI4C(dds)

Arguments

dds

DDS object generated by UMI4C2dds

Details

This function estimate the size factors and dispersions of the counts base on DESeq for infering the VST distribution and transform raw UMI4C counts.

Value

DDS object with variance stabilizing transformation counts

DESeq2 Wald test for differential contacts

Description

Using a UMI4C object, infers the differences between conditions specified in design using a Wald Test from DESeq2 package.

Usage

waldUMI4C(
  umi4c,
  query_regions = NULL,
  subset = "sum",
  design = ~condition,
  normalized = TRUE,
  padj_method = "fdr",
  padj_threshold = 0.05
)
waldUMI4C(
  umi4c,
  query_regions = NULL,
  subset = "sum",
  design = ~condition,
  normalized = TRUE,
  padj_method = "fdr",
  padj_threshold = 0.05
)

Arguments

`umi4c`	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`query_regions`	`GRanges` object containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.
`subset`	If `query_regions` are provided, how to subset the UMI4C object: "sum" for summing raw UMIs in fragments overlapping `query_regions` (default) or "overlap" for selecting overlapping fragments.
`design`	A `formula` or `matrix`. The formula expresses how the counts for each fragment end depend on the variables in `colData`. See `DESeqDataSet`.
`normalized`	Logical indicating if the function should return normalized or raw UMI counts. Default: TRUE.
`padj_method`	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.05.

Value

UMI4C object with the DESeq2 Wald Test results, which can be accessed using resultsUMI4C.

Examples

data("ex_ciita_umi4c")

umi_dif <- waldUMI4C(ex_ciita_umi4c)
data("ex_ciita_umi4c")

umi_dif <- waldUMI4C(ex_ciita_umi4c)

Z-score calculation using residuals of trend and fit UMI4C counts

Description

Calculates the z-score and then they are converted into one-sided P-values and adjusted for multiple testing using the method of Benjamini and Hochberg

Usage

zscoreUMI4C(
  dds,
  padj_method = "fdr",
  zscore_threshold = 2,
  padj_threshold = 0.1
)
zscoreUMI4C(
  dds,
  padj_method = "fdr",
  zscore_threshold = 2,
  padj_threshold = 0.1
)

Arguments

`dds`	DDS object as generated by `smoothMonotoneUMI4C` with the smooth monotone fit counts
`padj_method`	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
`zscore_threshold`	Numeric indicating the z-score threshold to use to define significant differential contacts. Default: 2.
`padj_threshold`	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.1.

Details

This function calculates the z-score for each fragment over all samples from the residuals of the symmetric monotone fit and the median absolute deviation (mad). Z-scores are then converted into one-sided P-values using the standard Normal cumulative distribution function, and these are adjusted for multiple testing using the method of Benjamini and Hochberg

Value

DDS object with zscore,pvalue and padjusted assays

Package 'UMI4Cats'

Help Index

Get BiocFileCache object

Description

Usage

Value

Summarize BAM file

Description

Usage

Arguments

Value

Align split fastq file

Description

Usage

Arguments

Value

Count UMIs for a given bam file.

Description

Usage

Arguments

Value

Prepar fastq files at a given barcode.

Description

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Split fastq files at a given restriction site.

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Monotone smoothing of the VST counts

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Add grouping of UMI-4C samples

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Add stepping for plotting genes

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UMI4C alignment

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Adaptative smoothing of normalized trend

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Create Domainogram

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Call significant interactions

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Combine UMI4C fragments

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UMI4C Contacts Processing

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