Package 'spatzie' reference manual

Title:	Identification of enriched motif pairs from chromatin interaction data
Description:	Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments.
Authors:	Jennifer Hammelman [aut, cre, cph] , Konstantin Krismer [aut] , David Gifford [ths, cph]
Maintainer:	Jennifer Hammelman <[email protected]>
License:	GPL-3
Version:	1.13.0
Built:	2024-12-14 04:10:37 UTC
Source:	https://github.com/bioc/spatzie

Determine enriched motifs in anchors

Description

Determine whether motifs between paired bed regions have a statistically significant relationship. Options for significance are motif score correlation, motif count correlation, or hypergeometric motif co-occurrence.

Usage

anchor_pair_enrich(interaction_data, method = c("count", "score", "match"))
anchor_pair_enrich(interaction_data, method = c("count", "score", "match"))

Arguments

interaction_data

an interactionData object of paired genomic regions

method

method for co-occurrence, valid options include:

`count`:	correlation between counts (for each anchor, tally positions where motif score > $5 * 10^{-5}$ )
`score`:	correlation between motif scores (for each anchor, use the maximum score over all positions)
`match`:	association between motif matches (for each anchor, a match is defined if the is at least one position with a motif score > $5 * 10^{-5}$ )

Value

an interactionData object where obj$pair_motif_enrich contains the p-values for significance of seeing a higher co-occurrence than what we get by chance.

Score-based correlation

We assume motif scores follow a normal distribution and are independent between enhancers and promoters. We can therefore compute how correlated scores of any two transcription factor motifs are between enhancer and promoter regions using Pearson's product-moment correlation coefficient:

$r = \frac{\sum (x^{\prime}_i - \bar{x}^{\prime})(y^{\prime}_i - \bar{y}^{\prime})}{\sqrt{\sum(x^{\prime}_i - \bar{x}^{\prime})^2\sum(y^{\prime}_i - \bar{y}^{\prime})^2}}$

, where the input vectors $\boldsymbol{x}$ and $\boldsymbol{y}$ from above are transformed to vectors $\boldsymbol{x^{\prime}}$ and $\boldsymbol{y^{\prime}}$ by replacing the set of scores with the maximum score for each region:

$x^{\prime}_i = \max x_i$

$x^{\prime}_i$ is then the maximum motif score of motif $a$ in the promoter region of interaction $i$ , $y^{\prime}_i$ is the maximum motif score of motif $b$ in the enhancer region of interaction $i$ , and $\bar{x}^{\prime}$ and $\bar{y}^{\prime}$ are the sample means.

Significance is then computed by transforming the correlation coefficient $r$ to test statistic $t$ , which is Student $t$ -distributed with $n - 2$ degrees of freedom.

$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$

All p-values are calculated as one-tailed p-values of the probability that scores are greater than or equal to $r$ .

Count-based correlation

Instead of calculating the correlation of motif scores directly, the count-based correlation metric first tallies the number of instances of a given motif within an enhancer or a promoter region, which are defined as all positions in those regions with motif score p-values of less than $5 * 10^{-5}$ . Formally, the input vectors $\boldsymbol{x}$ and $\boldsymbol{y}$ are transformed to vectors $\boldsymbol{x^{\prime\prime}}$ and $\boldsymbol{y^{\prime\prime}}$ by replacing the set of scores with the cardinality of the set:

$x^{\prime\prime}_i = |x_i|$

And analogous for $y^{\prime\prime}_i$ . Finally, the correlation coefficient $r$ between $\boldsymbol{x^{\prime\prime}}$ and $\boldsymbol{y^{\prime\prime}}$ and its associated significance are calculated as described above.

Match-based association

Instance co-occurrence uses the presence or absence of a motif within an enhancer or promoter to determine a statistically significant association, thus $\boldsymbol{x^{\prime\prime\prime}}$ and $\boldsymbol{y^{\prime\prime\prime}}$ are defined by:

$x^{\prime\prime\prime}_i = \boldsymbol{1}_{x^{\prime\prime}_i > 0}$

Instance co-occurrence is computed using the hypergeometric test:

$p = \sum_{k=I_{ab}}^{P_a} \frac{binom(P_a, k) binom(n - P_a, E_b - k)}{binom(n, E_b)},$

where $I_{ab}$ is the number of interactions that contain a match for motif $a$ in the promoter and motif $b$ in the enhancer, $P_a$ is the number of promoters that contain motif $a$ ( $P_a = \sum^n_i x^{\prime\prime\prime}_i$ ), $E_b$ is the number of enhancers that contain motif $b$ ( $E_b = \sum^n_i y^{\prime\prime\prime}_i$ ), and $n$ is the total number of interactions, which is equal to the number of promoters and to the number of enhancers.

Author(s)

Jennifer Hammelman

Konstantin Krismer

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_count_corr <- anchor_pair_enrich(yy1_pd_interaction, method = "count")

## End(Not run)

res <- anchor_pair_enrich(spatzie::scan_interactions_example_filtered,
                          method = "score")

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_count_corr <- anchor_pair_enrich(yy1_pd_interaction, method = "count")

## End(Not run)

res <- anchor_pair_enrich(spatzie::scan_interactions_example_filtered,
                          method = "score")

spatzie count correlation data set

Description

This object contains genomic interactions obtained by mouse YY1 ChIA-PET scanned for mouse transcription factor motifs, filtered for motifs present in at least 10 interactions with count correlation. It serves as unit test data.

Usage

data(anchor_pair_example_count)
data(anchor_pair_example_count)

Format

An interactionData object

spatzie match association data set

Description

Usage

data(anchor_pair_example_match)
data(anchor_pair_example_match)

Format

A interactionData object

spatzie score correlation data set

Description

Usage

data(anchor_pair_example_score)
data(anchor_pair_example_score)

Format

An interactionData object

Compare pairs of motifs between two interaction datasets

Description

Compute the log-likelihood ratio that a motif pair is differential between two interaction datasets. Note that motif pair significance should have been computed using the same method for both datasets.

Usage

compare_motif_pairs(
  interaction_data1,
  interaction_data2,
  differential_p = 0.05
)
compare_motif_pairs(
  interaction_data1,
  interaction_data2,
  differential_p = 0.05
)

Arguments

`interaction_data1`	an interactionData object of paired genomic regions that has been scanned for significant motif:motif interactions
`interaction_data2`	an interactionData object of paired genomic regions that has been scanned for significant motif:motif interactions
`differential_p`	threshold for significance of differential p-value

Value

a matrix of the log likelihood ratio of motif pairs that are significantly differential between two interactionData sets

Author(s)

Jennifer Hammelman

Examples

pheatmap::pheatmap(compare_motif_pairs(spatzie::int_data_k562,
                                       spatzie::int_data_mslcl, 5e-06),
                   fontsize = 6)
pheatmap::pheatmap(compare_motif_pairs(spatzie::int_data_k562,
                                       spatzie::int_data_mslcl, 5e-06),
                   fontsize = 6)

compare_motif_pairs example

Description

This is a matrix containing example result from compare_motif_pairs. It serves as unit test data.

Usage

data(compare_pairs_example)
data(compare_pairs_example)

Format

A matrix

Filter motifs based on occurrence within interaction data

Description

Select a subset of motifs that are in at least a threshold fraction of regions. Motif subsets are selected separately for anchor one and anchor two regions.

Usage

filter_motifs(interaction_data, threshold)
filter_motifs(interaction_data, threshold)

Arguments

`interaction_data`	an interactionData object of paired genomic regions
`threshold`	fraction of interactions that should contain a motif for a motif to be considered

Value

an interactionData object where obj$anchor1_motif_indices and obj$anchor2_motif_indices have been filtered to motifs that are present in a threshold fraction of interactions

Author(s)

Jennifer Hammelman

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)

## End(Not run)

res <- filter_motifs(spatzie::scan_interactions_example, threshold = 0.1)

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)

## End(Not run)

res <- filter_motifs(spatzie::scan_interactions_example, threshold = 0.1)

Filter significant motif interactions

Description

Multiple hypothesis correction applied to filter for significant motif interactions.

Usage

filter_pair_motifs(interaction_data, method = "fdr", threshold = 0.05)
filter_pair_motifs(interaction_data, method = "fdr", threshold = 0.05)

Arguments

`interaction_data`	an interactionData object of paired genomic regions
`method`	statistical method for multiple hypothesis correction, defaults to Benjamini-Hochberg (`"fdr"`) (see `p.adjust` for options)
`threshold`	p-value threshold for significance cut-off

Value

an interactionData object where obj$pair_motif_enrich contains multiple hypothesis corrected p-values for significance of seeing a higher co-occurrence than what we get by chance and obj$pair_motif_enrich_sig contains only motifs that have at least one significant interaction.

Author(s)

Jennifer Hammelman

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_score_corr <- anchor_pair_enrich(yy1_pd_interaction, method = "score")
yy1_pd_score_corr_adj <- filter_pair_motifs(yy1_pd_score_corr)

## End(Not run)

res <- filter_pair_motifs(spatzie::anchor_pair_example_count,
                          threshold = 0.5)

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_score_corr <- anchor_pair_enrich(yy1_pd_interaction, method = "score")
yy1_pd_score_corr_adj <- filter_pair_motifs(yy1_pd_score_corr)

## End(Not run)

res <- filter_pair_motifs(spatzie::anchor_pair_example_count,
                          threshold = 0.5)

spatzie score correlation filtered data set

Description

Usage

data(filter_pairs_example)
data(filter_pairs_example)

Format

An interactionData object

Find co-enriched motif pairs in enhancer-promoter interactions

Description

Identifies co-enriched pairs of motifs in enhancer-promoter interactions selected from a data frame of general genomic interactions.

If identify_ep: Promoters and enhancers are identified using genomic annotations, where anchors close to promoter annotations (within 2500 base pairs) are considered promoters and all other anchors are considered gene-distal enhancers. Only interactions in int_raw_data between promoters and enhancers are used for motif co-enrichment analysis.

If !identify_ep: Instead of automatically identifying promoters and enhancers based on genomic annotations, all interactions in int_raw_data must be preprocessed in a way that anchor 1 contains promoters and anchor 2 contains enhancers. Motif co-enrichment analysis is performed under this assumption.

Calls functions scan_motifs, filter_motifs, and anchor_pair_enrich internally.

Usage

find_ep_coenrichment(
  int_raw_data,
  motifs_file,
  motifs_file_matrix_format = c("pfm", "ppm", "pwm"),
  genome_id = c("hg38", "hg19", "mm9", "mm10"),
  identify_ep = TRUE,
  cooccurrence_method = c("count", "score", "match"),
  filter_threshold = 0.4
)
find_ep_coenrichment(
  int_raw_data,
  motifs_file,
  motifs_file_matrix_format = c("pfm", "ppm", "pwm"),
  genome_id = c("hg38", "hg19", "mm9", "mm10"),
  identify_ep = TRUE,
  cooccurrence_method = c("count", "score", "match"),
  filter_threshold = 0.4
)

Arguments

int_raw_data

a GenomicInteractions object or a data frame with at least six columns:

column 1:	character; genomic location of interaction anchor 1 - chromosome (e.g., `"chr3"`)
column 2:	integer; genomic location of interaction anchor 1 - start coordinate
column 3:	integer; genomic location of interaction anchor 1 - end coordinate
column 4:	character; genomic location of interaction anchor 2 - chromosome (e.g., `"chr3"`)
column 5:	integer; genomic location of interaction anchor 2 - start coordinate
column 6:	integer; genomic location of interaction anchor 2 - end coordinate

motifs_file

JASPAR format matrix file containing multiple motifs to scan for, gz-zipped files allowed

motifs_file_matrix_format

type of position-specific scoring matrices in motifs_file, valid options include:

`pfm`:	position frequency matrix, elements are absolute frequencies, i.e., counts (default)
`ppm`:	position probability matrix, elements are probabilities, i.e., Laplace smoothing corrected relative frequencies
`pwm`:	position weight matrix, elements are log likelihoods

genome_id

ID of genome assembly interactions in int_raw_data were aligned to, valid options include hg19, hg38, mm9, and mm10, defaults to hg38

identify_ep

logical, set FALSE if enhancers and promoters should not be identified based on genomic annotations, but instead assumes anchor 1 contains promoters and anchor 2 contains enhancers, for all interactions in int_raw_data, defaults to TRUE, i.e., do identify enhancers and promoters of interactions in int_raw_data based on genomic interactions and filter all interactions which are not between promoters and enhancers

cooccurrence_method

method for co-occurrence, valid options include:

`count`:	correlation between counts (for each anchor, tally positions where motif score > $5 * 10^{-5}$ )
`score`:	correlation between motif scores (for each anchor, use the maximum score over all positions)
`match`:	association between motif matches (for each anchor, a match is defined if the is at least one position with a motif score > $5 * 10^{-5}$ )

See anchor_pair_enrich for details.

filter_threshold

fraction of interactions that should contain a motif for a motif to be considered, see filter_motifs, defaults to 0.4

Value

a list with the following items:

`int_data`	`GenomicInteractions` object; promoter-enhancer interactions
`int_data_motifs`:	`interactionData` object; return value of `scan_motifs`
`filtered_int_data_motifs`:	`interactionData` object; return value of `filter_motifs`
`annotation_pie_chart`:	ggplot2 plot; return value of `plotInteractionAnnotations`
`motif_cooccurrence`:	`interactionData` object; return value of `anchor_pair_enrich`

Author(s)

Jennifer Hammelman

Konstantin Krismer

Examples

## Not run: 
interactions_file <- system.file("extdata/yy1_interactions.bedpe.gz",
                                 package = "spatzie")
motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")

df <- read.table(gzfile(interactions_file), header = TRUE, sep = "\t")
res <- find_ep_coenrichment(df, motifs_file,
                            motifs_file_matrix_format = "pfm",
                            genome_id = "mm10")

## End(Not run)

## Not run: 
interactions_file <- system.file("extdata/yy1_interactions.bedpe.gz",
                                 package = "spatzie")
motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")

df <- read.table(gzfile(interactions_file), header = TRUE, sep = "\t")
res <- find_ep_coenrichment(df, motifs_file,
                            motifs_file_matrix_format = "pfm",
                            genome_id = "mm10")

## End(Not run)

Get interactions that contain a specific motif pair

Description

Select interactions that contain anchor1_motif within anchor 1 and anchor2_motif within anchor 2.

Usage

get_specific_interactions(
  interaction_data,
  anchor1_motif = NULL,
  anchor2_motif = NULL
)
get_specific_interactions(
  interaction_data,
  anchor1_motif = NULL,
  anchor2_motif = NULL
)

Arguments

`interaction_data`	an interactionData object of paired genomic regions
`anchor1_motif`	Motif name from `interactionData$anchor1_motifs`
`anchor2_motif`	Motif name from `interactionData$anchor2_motifs`

Value

a GenomicInteractions object containing a subset subset of interactions that contain an instance of anchor1_motif in anchor 1 and anchor2_motif in anchor 2

Author(s)

Jennifer Hammelman

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_count_corr <- anchor_pair_enrich(yy1_pd_interaction,
                                        method = "score")
yy1_yy1_interactions <- get_specific_interactions(
  yy1_pd_interaction,
  anchor1_motif = "YY1",
  anchor2_motif = "YY1")

## End(Not run)

res <- get_specific_interactions(spatzie::int_data_yy1,
                                 anchor1_motif = "YY1",
                                 anchor2_motif = "YY1")

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
yy1_pd_count_corr <- anchor_pair_enrich(yy1_pd_interaction,
                                        method = "score")
yy1_yy1_interactions <- get_specific_interactions(
  yy1_pd_interaction,
  anchor1_motif = "YY1",
  anchor2_motif = "YY1")

## End(Not run)

res <- get_specific_interactions(spatzie::int_data_yy1,
                                 anchor1_motif = "YY1",
                                 anchor2_motif = "YY1")

K562 Enhancer - Promoter Interactions Data Set

Description

This object contains genomic interactions obtained by human RAD21 ChIA-PET from K562 cells and serves as unit test data.

Usage

data(int_data_k562)
data(int_data_k562)

Format

An interactionData object

MSLCL Enhancer - Promoter Interactions Data Set

Description

This object contains genomic interactions obtained by human RAD21 ChIA-PET from MSLCL cells and serves as unit test data.

Usage

data(int_data_mslcl)
data(int_data_mslcl)

Format

An interactionData object

Mouse YY1 Enhancer - Promoter Interactions Data Set

Description

This object contains genomic interactions obtained by mouse YY1 ChIA-PET and serves as example and unit test data.

Usage

data(int_data_yy1)
data(int_data_yy1)

Format

An interactionData object

Mouse YY1 Enhancer - Promoter Interactions Data Set

Description

This object contains genomic interactions obtained by mouse YY1 ChIA-PET and serves as example and unit test data. The same data set is used in the vignette.

Usage

data(interactions_yy1)
data(interactions_yy1)

Format

A GenomicInteractions object

Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 enhancers

Description

This is a GenomicInteractions object containing proccessed results from YY1 ChIA-PET of interactions that contain a YY1 motif in the enhancer (anchor 2) region. It serves as unit test data.

Usage

data(interactions_yy1_enhancer)
data(interactions_yy1_enhancer)

Format

A GenomicInteractions object

Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 enhancers/promoters

Description

This is a GenomicInteractions object containing proccessed results from YY1 ChIA-PET of interactions that contain a YY1 motif in the promoter (anchor 1) region and a YY1 motif in the enhancer (anchor 2) region. It serves as unit test data.

Usage

data(interactions_yy1_ep)
data(interactions_yy1_ep)

Format

A GenomicInteractions object

Mouse YY1 Enhancer - Promoter Interactions Data Set - YY1 promoters

Description

This is a GenomicInteractions object containing proccessed results from YY1 ChIA-PET of interactions that contain a YY1 motif in the promoter (anchor 1) region. It serves as unit test data.

Usage

data(interactions_yy1_promoter)
data(interactions_yy1_promoter)

Format

A GenomicInteractions object

Plot motif occurrence

Description

Plots a histogram of motif values (either counts, instances, or scores) for anchor 1 and anchor 2 regions.

Usage

plot_motif_occurrence(
  interaction_data,
  method = c("counts", "instances", "scores")
)
plot_motif_occurrence(
  interaction_data,
  method = c("counts", "instances", "scores")
)

Arguments

`interaction_data`	an interactionData object of paired genomic regions
`method`	way to interpret motif matching for each anchor region as "counts" number of motifs per region, "instances" motif present or absent each region, or "scores" maximum motif PWM match score for each region

Value

plot containing histogram for each anchor

Author(s)

Jennifer Hammelman

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
plot_motif_occurrence(yy1_pd_interaction,"counts")

## End(Not run)

plot_motif_occurrence(spatzie::anchor_pair_example_score)

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)
yy1_pd_interaction <- filter_motifs(yy1_pd_interaction, 0.4)
plot_motif_occurrence(yy1_pd_interaction,"counts")

## End(Not run)

plot_motif_occurrence(spatzie::anchor_pair_example_score)

Interactions scanned for motifs - interactionData object

Description

This object contains genomic interactions obtained by mouse YY1 ChIA-PET scanned for mouse transcription factor motifs and serves as unit test data.

Usage

data(scan_interactions_example)
data(scan_interactions_example)

Format

An interactionData object

Interactions with motifs filtered for significance - interactionData object

Description

This object contains genomic interactions obtained by mouse YY1 ChIA-PET scanned for mouse transcription factor motifs and filtered for motifs present in at least 10

Usage

data(scan_interactions_example_filtered)
data(scan_interactions_example_filtered)

Format

An interactionData object

Scans interaction file for motif instances

Description

Uses motifmatchR to scan interaction regions for given motifs.

Usage

scan_motifs(int_data, motifs, genome)
scan_motifs(int_data, motifs, genome)

Arguments

`int_data`	a `GenomicInteractions` object of paired genomic regions
`motifs`	a TFBS tools matrix of DNA binding motifs
`genome`	BSgenome object or DNAStringSet object, must match chromosomes from interaction data file

Value

an interaction data object where obj$anchor1_motifs and obj$anchor2_motifs contain information about the scores and matches to motifs from anchor one and anchor two of interaction data genomic regions

Author(s)

Jennifer Hammelman

Examples

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)

## End(Not run)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")
left <- GenomicRanges::GRanges(
  seqnames = c("chr1", "chr1", "chr1"),
  ranges = IRanges::IRanges(start = c(1, 15, 20),
                            end = c(10, 35, 31)))
right <- GenomicRanges::GRanges(
  seqnames = c("chr1", "chr2", "chr2"),
  ranges = IRanges::IRanges(start = c(17, 47, 41),
                            end = c(28, 54, 53)))
test_interactions <- GenomicInteractions::GenomicInteractions(left, right)

# toy DNAStringSet to replace BSgenome object
seqs <- c("chr1" = "CCACTAGCCACGCGTCACTGGTTAGCGTGATTGAAACTAAATCGTATGAAAATCC",
          "chr2" = "CTACAAACTAGGAATTTAGGCAAACCTGTGTTAAAATCTTAGCTCATTCATTAAT")
toy_genome <- Biostrings::DNAStringSet(seqs, use.names = TRUE)

res <- scan_motifs(test_interactions, motifs, toy_genome)

## Not run: 
genome_id <- "BSgenome.Mmusculus.UCSC.mm9"
if (!(genome_id %in% rownames(utils::installed.packages()))) {
  BiocManager::install(genome_id, update = FALSE, ask = FALSE)
}
genome <- BSgenome::getBSgenome(genome_id)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")

yy1_pd_interaction <- scan_motifs(spatzie::interactions_yy1, motifs, genome)

## End(Not run)

motifs_file <- system.file("extdata/motifs_subset.txt.gz",
                           package = "spatzie")
motifs <- TFBSTools::readJASPARMatrix(motifs_file, matrixClass = "PFM")
left <- GenomicRanges::GRanges(
  seqnames = c("chr1", "chr1", "chr1"),
  ranges = IRanges::IRanges(start = c(1, 15, 20),
                            end = c(10, 35, 31)))
right <- GenomicRanges::GRanges(
  seqnames = c("chr1", "chr2", "chr2"),
  ranges = IRanges::IRanges(start = c(17, 47, 41),
                            end = c(28, 54, 53)))
test_interactions <- GenomicInteractions::GenomicInteractions(left, right)

# toy DNAStringSet to replace BSgenome object
seqs <- c("chr1" = "CCACTAGCCACGCGTCACTGGTTAGCGTGATTGAAACTAAATCGTATGAAAATCC",
          "chr2" = "CTACAAACTAGGAATTTAGGCAAACCTGTGTTAAAATCTTAGCTCATTCATTAAT")
toy_genome <- Biostrings::DNAStringSet(seqs, use.names = TRUE)

res <- scan_motifs(test_interactions, motifs, toy_genome)

spatzie

Description

Looks for motifs which are significantly co-enriched from enhancer-promoter interaction data, derived from assays such as as HiC, ChIA-PET, etc. It can also look for differentially enriched motif pairs between to interaction experiments.

Author(s)

Jennifer Hammelman

Konstantin Krismer

Package 'spatzie'

Help Index

Determine enriched motifs in anchors

Description

Usage

Arguments

Value

Score-based correlation

Count-based correlation

Match-based association

Author(s)

Examples

spatzie count correlation data set

Description

Usage

Format

spatzie match association data set

Description

Usage

Format

spatzie score correlation data set

Description

Usage

Format

Compare pairs of motifs between two interaction datasets

Description

Usage

Arguments

Value

Author(s)

Examples

compare_motif_pairs example

Description

Usage

Format

Filter motifs based on occurrence within interaction data

Description

Usage

Arguments

Value

Author(s)

Examples

Filter significant motif interactions

Description

Usage

Arguments

Value

Author(s)

Examples

spatzie score correlation filtered data set

Description

Usage

Format

Find co-enriched motif pairs in enhancer-promoter interactions

Description

Usage

Arguments

Value

Author(s)

Examples

Get interactions that contain a specific motif pair

Description

Usage

Arguments

Value

Author(s)

Examples

K562 Enhancer - Promoter Interactions Data Set

Description

Usage

Format

MSLCL Enhancer - Promoter Interactions Data Set

Description

Usage

Format

Mouse YY1 Enhancer - Promoter Interactions Data Set

Description

Usage

Format

Mouse YY1 Enhancer - Promoter Interactions Data Set