Wrapper Function

The differential_analysis function runs the full differential analysis workflow in one call, covering three sequential steps: statistical testing, visualization, and pathway annotation. For users who prefer fine-grained control over parameters, please refer to the detailed parameter settings below.

First, count matrices across samples are compared condition-by-condition using a two-round limma-voom framework, identifying genomic regions with significant differences in accessibility. When column clusters are provided, counts within each cluster are aggregated across pairs before testing, and results are reported per cluster. Each pairwise comparison produces a set of significant regions with associated statistics.

Next, the significant regions from each comparison are visualized as log2 count heatmaps, with columns grouped by sample. This allows direct visual inspection of accessibility patterns across conditions.

Finally, the significant regions are submitted to GREAT for pathway enrichment analysis against a user-specified MSigDB gene set collection, revealing the biological processes associated with each set of differential regions. Results are summarised in per-comparison TSV tables and an optional bubble plot.

Parameters

Parameter	Type	Default	Description	Example
`cm_path`	character vector	—	Paths to per-sample count matrices in `.feather` format, one per sample. Must have the same length as `conditions`. Each file must contain a `pos` column and one column per CRF pair.	`c(“C1.feather”, “C2.feather”, “T1.feather”, “T2.feather”)`
`conditions`	character vector	—	Condition labels for each sample in `cm_path`, in the same order. Each condition must have at least 2 replicates. All pairwise comparisons between conditions are tested.	`c(“C”, “C”, “T”, “T”)`
`sample_names`	character vector or NULL	`NULL`	Optional sample names used in output file names and heatmap column labels. Must be unique and the same length as `conditions`. If `NULL`, names are auto-generated as `<condition><replicate_index>` (e.g. `C1`, `C2`, `T1`).	`c(“Control_1”, “Control_2”, “Treat_1”, “Treat_2”)`
`out_dir`	character	`“./”`	Root output directory for all results. Differential region tables and heatmaps are written here; pathway enrichment outputs are written to `<out_dir>/pathway_enrichment/`. Created recursively if it does not exist.	`“./differential”`
`col_cluster_file_path`	character or NULL	`NULL`	Path to a TSV file with columns `pair` and `cluster`, mapping CRF pairs to column clusters. When provided, counts within each cluster are summed across pairs before testing, and heatmaps expand back to pair-level columns grouped by sample. When `NULL`, each pair is tested independently.	`“bicluster/col_table.tsv”`
`ref_genome`	character	`“hg38”`	Reference genome. Controls the TSS source for GREAT and the species for MSigDB gene set retrieval. Supported: `“hg38”`, `“mm10”`.	`“mm10”`
`norm_level`	character	`“crf”`	Controls the level at which TMM normalization factors are estimated. `“crf”` estimates factors independently within each CRF group, which is appropriate when composition is expected to remain relatively stable across conditions and only local differences are anticipated. `“global”` (recommended for most cases) estimates a single set of normalization factors across all samples before any per-group analysis, making it more robust when global composition shifts substantially between conditions (such as in CRF knockdown or knockout experiments where the target mark is depleted genome-wide).	`norm_level = “global”`
`min_support`	integer	`2`	Minimum number of non-zero replicates required within at least one condition for a genomic region to pass the initial non-zero support filter before differential testing. Only regions passing this filter are carried forward to the limma-voom modeling step and considered for significance testing. For example, with `min_support = 2`, a region is retained if it has non-zero counts in at least 2 replicates in Control or at least 2 replicates in Treatment.	`1`
`lfc_threshold`	numeric	`0.5`	Minimum absolute log2 fold-change required for a region to be called significant after model fitting. A region is considered significant only if it satisfies both `abs(logFC) > lfc_threshold` and `adj.P.Val < p_threshold`. This parameter controls the effect size requirement in the final significance decision.	`1`
`p_threshold`	numeric	`0.05`	P-value threshold for significance. Interpreted according to `p_type`.	`p_threshold = 0.05`
`p_type`	character	`“fdr”`	Which p-value to use for filtering: `“fdr”` (BH-adjusted, recommended for genome-wide analysis), `“nominal”` (raw p-value, suitable for pre-selected gene lists), or `“bonferroni”` (conservative correction).	`p_type = “nominal”`
`apply_annotation`	logical	`TRUE`	Whether to perform GREAT-based pathway enrichment annotation on significant differential regions. Set to `FALSE` to skip the pathway annotation step entirely.	`FALSE`
`plot`	logical	`TRUE`	Whether to generate visualizations. Controls both heatmap generation and the pathway enrichment bubble plot. Set to `FALSE` to suppress all plot outputs.	`FALSE`

Example Usage

library(multiEpiCore)
# Test Data
cm_root_path <- "count_matrix/"
cm_files <- c("C1_Count_Matrix_800.feather", "C2_Count_Matrix_800.feather", "T1_Count_Matrix_800.feather", "T2_Count_Matrix_800.feather")
cm_path <- file.path(cm_root_path, cm_files)

conditions <- c("C", "C", "T", "T")
sample_names <- c("C1", "C2", "T1", "T2")

out_dir <- "differential"

differential_regions(cm_path = cm_path, conditions = conditions, out_dir = out_dir, norm_level = "crf", min_support = 2, lfc_threshold = 1, p_threshold = 0.05, p_type = "fdr", apply_annotation = TRUE, plot = TRUE)

1. Differential Region Detection

The differential_regions() function identifies differential genomic regions between experimental conditions using limma-voom. An optional column cluster file can be provided to group CRF pairs into clusters. When provided, differential analysis is performed at the cluster level (for each cluster, all assigned CRF pairs are included together). When omitted, each CRF pair is analyzed as its own group.

Differential analysis is performed using the following workflow:

Normalization
TMM (trimmed mean of M-values) normalization is applied to correct for composition bias across samples. The level at which normalization factors are estimated is controlled by norm_level:
- "global" (default): A single set of TMM factors is estimated once across all samples using the full feature space (all regions × all pairs flattened into a single matrix), before any per-group analysis. This is recommended for most epigenomic comparisons and ensures that fold-change estimates are comparable across CRFs.
- "crf": TMM factors are estimated independently within each CRF group during model fitting. This is more appropriate when global composition is expected to shift substantially between conditions — for example, in CRF knockdown or knockout experiments where the target mark is depleted genome-wide.
Non-zero support filtering
Regions are filtered based on signal support across replicates. A region is retained only if it has non-zero counts in at least min_support replicates within at least one condition. This removes regions with insufficient signal for reliable modeling.
Voom transformation
The filtered count matrix is transformed to log2-CPM values via voom, which estimates the mean-variance relationship and assigns observation-level precision weights using the normalization factors determined in step 1.
Linear modeling and empirical Bayes moderation
A design matrix is constructed from the input conditions and a linear model is fitted for each region. Empirical Bayes moderation (eBayes) is applied to stabilize variance estimates across regions.
Differential region identification
Regions are defined as differential if they satisfy both:
- absolute log2 fold-change > lfc_threshold
- p-value < p_threshold (interpreted according to p_type)

Parameters

Parameter	Type	Default	Description	Example
`cm_path`	character vector	—	Paths to per-sample count matrices in `.feather` format. Each file should represent one sample, with rows as genomic regions (stored in a required `pos` column) and columns as CRF pairs. All files are harmonized to the common set of regions and CRF pairs before analysis.	`c(“C1.feather”, “C2.feather”, “T1.feather”, “T2.feather”)`
`conditions`	character vector	—	Condition labels corresponding to `cm_path`, in the same order. Each unique condition must have at least 2 replicates, because limma-voom is performed on replicated group comparisons.	`c(“Control”, “Control”, “Treatment”, “Treatment”)`
`sample_names`	character vector	`NULL`	Optional unique sample names corresponding to `cm_path`. If `NULL`, names are automatically generated from `conditions` in the form of condition + replicate index (for example, `Control1`, `Control2`).	`c(“C1”, “C2”, “T1”, “T2”)`
`out_dir`	character	`“./”`	Output directory for all result files, including full differential tables, significant-region tables, summary tables, summary plots, and significant-region count matrices.	`“diff_results”`
`col_cluster_file_path`	character	`NULL`	Optional path to a two-column TSV file with columns `pair` and `cluster`. If provided, the function runs in cluster-aware mode: CRF pairs assigned to the same cluster are summed within each sample and tested as one aggregated group. If `NULL`, the function runs in per-pair mode, where each CRF pair is tested independently.	`“col_cluster.tsv”`
`norm_level`	character	`“crf”`	Controls the level at which TMM normalization factors are estimated. `“crf”` estimates factors independently within each CRF group, which is appropriate when composition is expected to remain relatively stable across conditions and only local differences are anticipated. `“global”` (recommended for most cases) estimates a single set of normalization factors across all samples before any per-group analysis, making it more robust when global composition shifts substantially between conditions (such as in CRF knockdown or knockout experiments where the target mark is depleted genome-wide).	`norm_level = “global”`
`min_support`	integer	`2`	Minimum number of non-zero replicates required within at least one condition for a genomic region to pass the initial non-zero support filter. For example, with `min_support = 2`, a region is retained if it has non-zero counts in at least 2 replicates in Control or at least 2 replicates in Treatment.	`1`
`lfc_threshold`	numeric	`0.5`	Minimum absolute log2 fold-change required for a region to be called significant after model fitting. A region must satisfy both `abs(logFC) > lfc_threshold` and `adj.P.Val < p_threshold`.	`1`
`p_threshold`	numeric	`0.05`	P-value threshold for significance. Interpreted according to `p_type`.	`p_threshold = 0.05`
`p_type`	character	`“fdr”`	Which p-value to use for filtering: `“fdr”` (BH-adjusted, recommended for genome-wide analysis), `“nominal”` (raw p-value, suitable for pre-selected gene lists), or `“bonferroni”` (conservative correction).	`p_type = “nominal”`

Output Files

The function automatically derives all pairwise comparisons from the unique values in conditions (sorted alphabetically, in the form <test>_vs_<ref>), and creates one subdirectory per comparison under out_dir. All output files are written into the corresponding subdirectory:

All regions - <comparison>/<cluster|pair>_all.tsv
- Complete limma-voom differential analysis results for all tested genomic regions within the specified cluster and comparison.
- Each row represents one genomic region and the table includes the following columns:
  - pos: Genomic region identifier corresponding to the original pos field in the count matrix.
  - logFC: Log2 fold change between the test and reference conditions (positive = higher in test).
  - AveExpr: Average log2-CPM expression level across all samples in the comparison.
  - t: Moderated t-statistic from limma’s empirical Bayes model.
  - P.Value: Raw p-value from the moderated t-test.
  - adj.P.Val: Benjamini-Hochberg FDR-adjusted p-value.
  - B: Log-odds that the region is truly differentially expressed.

pos	logFC	AveExpr	t	P.Value	adj.P.Val	B
<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
chr9_130356001_130356800	4.0866	0.2914	11.5638	1.14e-07	0.0331	7.7404
chr10_101139201_101140000	3.8606	0.2378	10.9880	1.95e-07	0.0331	7.2410
chr10_100614401_100615200	3.9058	0.4437	10.7582	2.44e-07	0.0331	7.0467
chr5_148827201_148828000	3.8131	0.1777	10.6754	2.64e-07	0.0331	6.9650
chr15_77552001_77552800	4.0206	0.3255	10.5539	2.98e-07	0.0331	6.9716
…

This table shows differential analysis results for H3K4me1 in the test dataset.

Significant regions - <comparison>/<cluster|pair>_sig.tsv
- Filtered results containing only significant regions (FDR < threshold & |logFC| > threshold)
- Only generated when at least one significant region is detected under the specified thresholds.
Significant region counts — <comparison>/<cluster|pair>_sig_raw_counts.tsv
- Raw fragment counts and for significant regions, each with one row per region and one column per sample.
- Only generated alongside _sig.tsv when at least one significant region is detected.

pos	C1	C2	T1	T2
chr9_130356001_130356800	0	0	16.4753	7.4848
chr10_101139201_101140000	0.0215	0	11.5733	8.7793
chr10_100614401_100615200	0.1848	0	14.2299	9.7483
chr5_148827201_148828000	0	0	11.4695	7.7631
chr15_77552001_77552800	0	0	9.7289	14.0664
…

This table shows differential analysis results for H3K4me1 in the test dataset.

Significant region CPM — <comparison>/<cluster|pair>_sig_log2_cpm.tsv
- TMM-normalized log2-CPM values for significant regions, each with one row per region and one column per sample.
- Log2-CPM values are derived from voom transformation using globally estimated TMM normalization factors.
- Only generated alongside _sig.tsv when at least one significant region is detected.

pos	C1	C2	T1	T2
chr9_130356001_130356800	-1.7753	-1.6832	2.5930	2.0310
chr10_101139201_101140000	-1.7147	-1.6832	2.1014	2.2478
chr10_100614401_100615200	-1.3215	-1.6832	2.3883	2.3911
chr5_148827201_148828000	-1.7753	-1.6832	2.0889	2.0805
chr15_77552001_77552800	-1.7753	-1.6832	1.8622	2.8984
…

This table shows differential analysis results for H3K4me1 in the test dataset.

Summary table per comparison - <comparison>/summary.tsv Includes:
- cluster ID
- number of regions before filtering
- after non-zero filter
- after mean filter
- total significant
- up-regulated
- down-regulated

group	n_rows_before	n_rows_after_nonzero	n_rows_after_mean	n_sig	n_up	n_down
H3K27ac	3860350	45078	45078	0	0	0
H3K27me3	3860350	1297331	1297331	15829	8727	7102
H3K4me1	3860350	908557	908557	194	189	5
H3K4me3	3860350	528144	528144	7423	5067	2356
H3K9me2	3860350	166729	166729	0	0	0
H3K9me3	3860350	1075802	1075802	15311	8505	6806

Summary plot per comparison - <comparison>/summary.pdf

Mirror bar chart visualizing the number of differentially accessible regions per group, with up-regulated regions shown in red (above x-axis) and down-regulated regions in blue (below x-axis).

Example Usage

library(multiEpiCore)

# Test data
cm_root_path <- "count_matrix/"
cm_files <- c("C1_Count_Matrix_800.feather", "C2_Count_Matrix_800.feather", "T1_Count_Matrix_800.feather", "T2_Count_Matrix_800.feather")
cm_path <- file.path(cm_root_path, cm_files)

conditions <- c("C", "C", "T", "T")
sample_names <- c("C1", "C2", "T1", "T2")

out_dir <- "differential"

differential_regions(cm_path = cm_path, conditions = conditions, out_dir = out_dir, norm_level = "crf", min_support = 2, lfc_threshold = 1, p_threshold = 0.05, p_type = "fdr")

2. Differential Accessibility Heatmaps

The differential_heatmap() function generates per-comparison, per-group heatmaps for all significant differential regions produced by differential_regions(). It iterates over the full result object returned by differential_regions() and dispatches each comparison-group combination to differential_heatmap_single(), which renders a region × sample log2-CPM heatmap as a PDF file.

Log2-CPM values are read directly from the _sig_log2_cpm.tsv output of differential_regions(), which are derived from voom transformation using globally estimated TMM normalization factors. No additional transformation is applied.

When the underlying analysis used column clustering (col_cluster_file_path), columns in the heatmap represent individual sample:pair combinations within the cluster, grouped and titled by sample. Without clustering, each column represents one sample directly.

Parameters

Parameter	Type	Default	Description	Example
`dr_result`	nested list	—	The return value of `differential_regions()`. A two-level nested list structured as `dr_result[[grp_name]][[cmp_tag]]`, where the first level keys are cluster or pair names (e.g. `“cluster4”`, `“H3K27ac_peak1”`) and the second level keys are comparison tags (e.g. `“T_vs_C”`). Each leaf is the file path to the corresponding `_sig_counts.tsv`.	`dr_result <- differential_regions(…)`
`out_dir`	character	`“./”`	Output directory for heatmap PDFs. Created recursively if it does not exist. One PDF is written per comparison-group combination, named `<cmp_tag>_<grp_name>.pdf`.	`“heatmaps”`
`show_colnames`	logical	`FALSE`	Whether to display individual column names on the heatmap. When `FALSE`, only the sample-level column group titles are shown. Set to `TRUE` to additionally show individual pair identifiers within each sample group.	`TRUE`
`col_width_mm`	numeric	`40`	Width per individual pair column in mm. Total heatmap width is computed as `n_pairs × col_width_mm + (n_samples − 1) × 8` mm, where the second term accounts for the fixed 8 mm gap between sample groups. In cluster mode (when `col_cluster_file_path` was supplied to `differential_regions()`), each sample group contains multiple pair columns; `col_width_mm` still refers to a single pair column, so wider clusters automatically occupy proportionally more space. In non-cluster mode each sample has exactly one column, so `col_width_mm` equals the per-sample width directly.	`30`
`row_height_mm`	numeric	`0.5`	Height per region row in mm. Total heatmap body height is `n_sig_regions × row_height_mm` mm, so the PDF scales automatically with the number of significant regions. Note that values below ~0.35 mm (one pixel at 72 dpi) will be visually indistinguishable; for large region counts (>1000) a value of 0.13–0.5 mm produces dense stripe patterns comparable to a fixed-height layout.	`1.0`
`random_seed`	integer	`42`	Random seed passed to `set.seed()` before each raster rendering call to ensure pixel-level reproducibility across runs.	`123`

Output Files

Output is organized by comparison tag (cmp_tag) read from dr_result:

Heatmap - <comparison>/<cluster|pair>.pdf

Rows: Significant genomic regions
Columns: All CRF pairs across all samples
Column blocks: Grouped by sample

This figure shows differential analysis heatmap for cluster 4 in the test dataset.

Example Usage

library(multiEpiCore)

# Test data
dr_result <- list(
  H3K4me1 = list(
    T_vs_C = "differential/T_vs_C/H3K4me1_sig_log2_cpm.tsv"
  ),
  H3K27me3 = list(
    T_vs_C = "differential/T_vs_C/H3K27me3_sig_log2_cpm.tsv"
  ),
  H3K4me3 = list(
    T_vs_C = "differential/T_vs_C/H3K4me3_sig_log2_cpm.tsv"
  ),
  H3K9me3 = list(
    T_vs_C = "differential/T_vs_C/H3K9me3_sig_log2_cpm.tsv"
  )
)

differential_heatmap(
  dr_result = dr_result,
  out_dir   = "differential"
)

3. Differential Pathway Annotation

The differential_pathway_annotation() function performs pathway enrichment analysis on the significant differential regions from each comparison-group combination using GREAT (Genomic Regions Enrichment of Annotations Tool). GREAT assigns genomic regions to nearby genes via basal-plus-extension TSS domains, then tests for over-representation of gene sets using a binomial test. Gene sets are sourced from MSigDB via the msigdbr package, and can be configured to use any available collection such as Hallmark, Reactome, or GO terms.

Each comparison-group combination (e.g. T vs C within cluster 4) is tested independently. Results are written as per-comparison TSV tables and optionally visualised as a bubble plot that summarises enrichment patterns across all comparisons at a glance.

Parameters

Parameter	Type	Default	Description	Example
`dr_result`	nested list	—	The return value of `differential_regions()`. A two-level nested list structured as `dr_result[[grp_name]][[cmp_tag]]`, where each leaf is the file path to the corresponding `_sig_counts.tsv`. Each comparison-group combination becomes one GREAT query set.	`dr_result <- differential_regions(…)`
`out_dir`	character	`“./”`	Output directory for enrichment TSV tables and the bubble plot PDF. Created recursively if it does not exist. One TSV is written per query set, named `pathway_annotation_<cmp_tag>_<grp_name>.tsv`.	`“pathway”`
`ref_genome`	character	`“hg38”`	Reference genome. Controls both the TSS source used by GREAT (`“hg38”` → `TxDb.Hsapiens.UCSC.hg38.knownGene`, `“mm10”` → `TxDb.Mmusculus.UCSC.mm10.knownGene`) and the species passed to `msigdbr()`. Must be one of `“hg38”` or `“mm10”`.	`“mm10”`
`msigdb_collection`	character	`“H”`	MSigDB collection abbreviation. Common values: `“H”` (Hallmark, 50 curated biological states), `“C2”` (curated gene sets including KEGG and Reactome), `“C5”` (GO gene sets). See `msigdbr::msigdbr_collections()` for the full list.	`“C2”`
`msigdb_subcollection`	character or NULL	`NULL`	MSigDB sub-collection abbreviation. Required when `msigdb_collection` has subcollections. For example, `“CP:REACTOME”` or `“CP:KEGG_LEGACY”` under `“C2”`, and `“GO:BP”`, `“GO:MF”`, or `“GO:CC”` under `“C5”`. Leave `NULL` for collections without subcollections such as `“H”`.	`“CP:REACTOME”`
`plot`	logical	`TRUE`	Whether to generate a bubble plot PDF (`pathway_annotation.pdf`) summarising enrichment across all query sets. Each row is a pathway ordered by best adjusted p-value across query sets; bubble size encodes `log2(1 + fold_enrichment)` and color encodes `−log10(padj)`.	`FALSE`

Output Files

Output is organized by comparison tag (cmp_tag) read from dr_result, with a pathway_annotation/ subdirectory created under each:

Enrichment tables — <cmp_tag>/pathway_annotation/pathway_annotation_<grp_name>.tsv

pathway : Pathway or gene set name, with collection-specific prefixes removed (e.g. HALLMARK_ stripped for collection "H")
hits_region: Number of input regions associated with at least one gene in the gene set
fold: Fold enrichment of observed over expected region hits
p: Binomial test p-value
padj: BH-adjusted p-value across all tested gene sets
hits_gene: Number of genes in the gene set that were hit by at least one input region

pathway	hits_region	fold	p	padj	hits_gene
<chr>	<int>	<dbl>	<dbl>	<dbl>	<int>
ESTROGEN_RESPONSE_LATE	264	1.7437	0	0	103
ESTROGEN_RESPONSE_EARLY	309	1.6681	0	0	112
HEME_METABOLISM	210	1.7305	1.20e-13	2.00e-12	112
P53_PATHWAY	214	1.6654	2.28e-12	2.85e-11	99
ADIPOGENESIS	180	1.4767	4.18e-07	4.18e-06	88
APICAL_JUNCTION	202	1.4176	1.24e-06	1.04e-05	94
UV_RESPONSE_UP	160	1.4729	2.03e-06	1.41e-05	72
TNFA_SIGNALING_VIA_NFKB	255	1.3487	2.25e-06	1.41e-05	109
XENOBIOTIC_METABOLISM	153	1.4038	3.55e-05	1.97e-04	77
KRAS_SIGNALING_DN	207	1.2889	2.19e-04	1.09e-03	82
MYOGENESIS	189	1.2961	3.06e-04	1.39e-03	94
APICAL_SURFACE	68	1.5145	7.63e-04	3.18e-03	22
IL6_JAK_STAT3_SIGNALING	71	1.4481	1.82e-03	7.00e-03	38
COMPLEMENT	182	1.2447	2.18e-03	7.80e-03	85
APOPTOSIS	153	1.2603	2.98e-03	9.94e-03	78
UNFOLDED_PROTEIN_RESPONSE	86	1.3636	3.37e-03	1.01e-02	46
COAGULATION	106	1.3185	3.44e-03	1.01e-02	55
HYPOXIA	208	1.2078	4.09e-03	1.13e-02	99
PEROXISOME	83	1.3033	1.12e-02	2.95e-02	43
DNA_REPAIR	80	1.3021	1.28e-02	3.20e-02	55
ALLOGRAFT_REJECTION	148	1.1887	2.11e-02	4.99e-02	68
IL2_STAT5_SIGNALING	203	1.1559	2.20e-02	4.99e-02	103
NOTCH_SIGNALING	41	1.3528	3.66e-02	7.95e-02	22
MYC_TARGETS_V2	34	1.3812	4.17e-02	8.70e-02	19
HEDGEHOG_SIGNALING	70	1.2359	4.68e-02	9.24e-02	23
INTERFERON_GAMMA_RESPONSE	153	1.1489	4.80e-02	9.24e-02	82
…	truncated

This table shows pathway annotation result for cluster 4 in the test dataset.

Bubble plot — <cmp_tag>/pathway_annotation/pathway_annotation.pdf (when plot = TRUE)

Rows are pathways ordered by best adjusted p-value across all clusters/pairs.
Columns are clusters/pairs.
Bubble size encodes log2 fold enrichment and color encodes -log10(padj).

This figure shows pathway annotation result for cluster 4 in the test dataset.

Example Usage

library(multiEpiCore)

# Test data
dr_result <- list(
  H3K4me1 = list(
    T_vs_C = "differential/T_vs_C/H3K4me1_sig_log2_cpm.tsv"
  ),
  H3K27me3 = list(
    T_vs_C = "differential/T_vs_C/H3K27me3_sig_log2_cpm.tsv"
  ),
  H3K4me3 = list(
    T_vs_C = "differential/T_vs_C/H3K4me3_sig_log2_cpm.tsv"
  ),
  H3K9me3 = list(
    T_vs_C = "differential/T_vs_C/H3K9me3_sig_log2_cpm.tsv"
  )
)

differential_pathway_annotation(
  dr_result = dr_result,
  out_dir   = "differential/pathway_annotation"
)

Differential Analysis

Wrapper Function

Parameters

Example Usage

1. Differential Region Detection

Parameters

Output Files

Example Usage

2. Differential Accessibility Heatmaps

Parameters

Output Files

Example Usage

3. Differential Pathway Annotation

Parameters

Output Files

Example Usage