Signac

https://stuartlab.org/signac
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Signac is a comprehensive R package designed for the analysis of single-cell chromatin data,...
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### Create Motif Object Example

Source: https://stuartlab.org/signac/1.16.0/reference/createmotifobject

An example demonstrating how to create a Motif object using CreateMotifObject with a sample motif matrix.

```r
motif.matrix <- matrix(
  data = sample(c(0,1),
    size = 100,
    replace = TRUE),
  ncol = 5
)
rownames(motif.matrix) <- 1:nrow(motif.matrix)
motif <- CreateMotifObject(data = motif.matrix)
```

--------------------------------

### Install v2 Beta Release of Signac

Source: https://stuartlab.org/signac/articles/install

Installs the v2 beta release of Signac from its GitHub repository. Requires the 'remotes' package.

```r
remotes::install_github("stuart-lab/signac", ref = "v2")
```

--------------------------------

### CreateFragmentObject Example

Source: https://stuartlab.org/signac/1.16.0/reference/createfragmentobject

Example of creating a Fragment object. This demonstrates how to provide a path to a fragment file and a named character vector of cells. The verbose argument is set to FALSE to suppress messages.

```R
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
cells <- colnames(x = atac_small)
names(x = cells) <- paste0("test_", cells)
frags <- CreateFragmentObject(path = fpath, cells = cells, verbose = FALSE, tolerance = 0.5)
```

--------------------------------

### Install Development Version of Signac

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs the latest unreleased version of Signac directly from its GitHub repository using the 'remotes' package. Ensure 'remotes' is installed first.

```r
if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("stuart-lab/signac", ref = "develop")
```

--------------------------------

### Install presto Package for Differential Analysis

Source: https://stuartlab.org/signac/1.16.0/articles/pbmc_vignette

Installs the 'presto' package from GitHub, which provides a fast implementation of the Wilcoxon rank-sum test for differential accessibility analysis.

```r
if (!requireNamespace("remotes", quietly = TRUE))
  install.packages('remotes')
remotes::install_github('immunogenomics/presto')
```

--------------------------------

### Example Usage of SubsetMatrix

Source: https://stuartlab.org/signac/1.16.0/reference/subsetmatrix

Demonstrates how to use the SubsetMatrix function with the 'volcano' dataset. This example applies default filtering criteria.

```R
SubsetMatrix(mat = volcano)
```

--------------------------------

### Get First Rows of a Fragment File

Source: https://stuartlab.org/signac/1.16.0/reference/head.fragment

Use the head.Fragment method to inspect the beginning of a fragment file. It returns a data frame with columns: chrom, start, end, barcode, readCount.

```R
head(x, n = 6L, ...)
```

--------------------------------

### Install Signac using Conda

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs Signac via conda, recommended for users who manage their R packages through conda. Ensure all R packages are installed via conda in this setup.

```bash
conda install -c bioconda r-signac
```

--------------------------------

### Install Cicero from GitHub

Source: https://stuartlab.org/signac/1.16.0/articles/cicero

Installs the Cicero package from a specific GitHub branch ('monocle3') if it's not already installed. This branch is required for compatibility with Monocle 3 CellDataSet objects.

```r
# Install Cicero
if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("cole-trapnell-lab/cicero-release", ref = "monocle3")
```

--------------------------------

### GetMotifData Example

Source: https://stuartlab.org/signac/1.16.0/reference/getmotifdata

This example demonstrates how to use the GetMotifData function with a small ATAC-seq dataset. The output is a sparse matrix indicating motif enrichment across genomic regions.

```R
GetMotifData(object = atac_small)
#> 323 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'MA0025.1', 'MA0030.1', 'MA0031.1' ... ]]
#>                                         
#> chr1-713460-714823   . 1 . . 1 . . . . .
#> chr1-752422-753038   . . . . . . . . . .
#> chr1-762106-763359   . . . . 1 . . . . .
#> chr1-779589-780271   . . . 1 . . . 1 1 .
```

--------------------------------

### Example Usage of CreateMotifMatrix

Source: https://stuartlab.org/signac/1.16.0/reference/createmotifmatrix

Demonstrates how to create a motif matrix using CreateMotifMatrix with sample data. Requires JASPAR2018, TFBSTools, and BSgenome.Hsapiens.UCSC.hg19.

```r
library(JASPAR2018)
library(TFBSTools)
library(BSgenome.Hsapiens.UCSC.hg19)

pwm <- getMatrixSet(
  x = JASPAR2018,
  opts = list(species = 9606, all_versions = FALSE)
)
motif.matrix <- CreateMotifMatrix(
  features = granges(atac_small),
  pwm = pwm,
  genome = BSgenome.Hsapiens.UCSC.hg19
)
```

--------------------------------

### Install Mouse mm10 Genome and Annotation Packages

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs the BSgenome and EnsDb packages for the mouse mm10 genome assembly and its corresponding gene annotation from Bioconductor.

```r
BiocManager::install(c('BSgenome.Mmusculus.UCSC.mm10', 'EnsDb.Mmusculus.v79'))
```

--------------------------------

### Install Human hg38 Genome and Annotation Packages

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs the BSgenome and EnsDb packages for the human hg38 genome assembly and its corresponding gene annotation from Bioconductor.

```r
BiocManager::install(c('BSgenome.Hsapiens.UCSC.hg38', 'EnsDb.Hsapiens.v86'))
```

--------------------------------

### BinarizeCounts Example with Matrix

Source: https://stuartlab.org/signac/1.16.0/reference/binarizecounts

Demonstrates how to use BinarizeCounts on a simple matrix. The function converts all counts greater than 1 to 1.

```r
x <- matrix(data = sample(0:3, size = 25, replace = TRUE), ncol = 5)
BinarizeCounts(x)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    1    1    1
#> [2,]    1    1    1    0    1
#> [3,]    1    1    1    0    1
#> [4,]    0    1    1    1    1
#> [5,]    0    0    1    0    1
```

--------------------------------

### FindTopFeatures Example

Source: https://stuartlab.org/signac/1.16.0/reference/findtopfeatures

Demonstrates how to use the FindTopFeatures function to identify top features from a dataset. The output shows the count and percentile for each feature.

```R
FindTopFeatures(object = atac_small[['peaks']]['data'])
#>                          count  percentile
#> chr1-2157847-2188813 384.21692 1.000000000
#> chr1-6843960-6846894 306.71972 0.996904025
#> chr1-3815928-3820356 293.92311 0.993808050
#> chr1-2471903-2481288 291.68103 0.990712074
#> chr1-2515241-2519350 273.54125 0.987616099
#> chr1-6051145-6055407 271.58079 0.984520124
#> chr1-6659264-6664388 269.06630 0.981424149
#> chr1-8935313-8940649 257.13518 0.978328173
#> chr1-1549446-1552535 249.25680 0.975232198
#> chr1-8020317-8022548 245.27406 0.972136223
#> chr1-1708510-1715065 238.85121 0.969040248
#> chr1-2322313-2323985 234.97422 0.965944272
#> chr1-3540009-3542500 226.59025 0.962848297
#> chr1-6672613-6675785 224.61335 0.959752322
#> chr1-2343197-2348396 223.27737 0.956656347
#> chr1-2486054-2489237 220.64562 0.953560372
#> chr1-6639159-6642773 220.03083 0.950464396
#> chr1-1051006-1053102 219.01926 0.947368421
#> chr1-1849693-1853920 216.86560 0.944272446
#> chr1-6085254-6089147 209.43368 0.941176471
#> chr1-2063672-2065744 208.40462 0.938080495
#> chr1-1240091-1245762 202.41066 0.934984520
#> chr1-1309645-1311492 200.93170 0.931888545
#> chr1-1333514-1336003 199.69752 0.928792570
#> chr1-1819732-1824104 199.57829 0.925696594
#> chr1-2135022-2137621 198.43822 0.922600619
#> chr1-928630-937949   195.69880 0.919504644
#> chr1-6452328-6454679 194.68799 0.916408669
#> chr1-1839727-1841458 192.45257 0.913312693
#> chr1-7830534-7832331 191.47613 0.910216718
#> chr1-9293115-9295644 188.43304 0.907120743
#> chr1-2125466-2127403 188.28612 0.904024768
#> chr1-7841489-7844891 180.48141 0.900928793
#> chr1-2456987-2458884 178.90723 0.897832817
#> chr1-1446312-1448163 177.61112 0.894736842
#> chr1-9188154-9190941 176.17157 0.891640867
#> chr1-1655226-1656410 175.89464 0.888544892
#> chr1-713460-714823   175.20779 0.885448916
#> chr1-1259506-1261414 172.88999 0.882352941
#> chr1-3773000-3774956 170.91511 0.879256966
#> chr1-1166366-1168282 169.69997 0.876160991
#> chr1-1623610-1625141 167.78231 0.873065015
#> chr1-1341794-1343522 166.82518 0.869969040
#> chr1-1562519-1567986 165.90765 0.866873065
#> chr1-2227715-2234197 163.94501 0.863777090
#> chr1-894021-896898   163.92453 0.860681115
#> chr1-2241757-2247855 162.95554 0.857585139
#> chr1-8876634-8878709 158.30708 0.854489164
#> chr1-6258846-6260407 155.41484 0.851393189
#> chr1-8013637-8014963 153.16873 0.848297214
#> chr1-1509089-1511050 152.49880 0.845201238
#> chr1-1980856-1982929 152.22569 0.842105263
#> chr1-7763862-7766072 150.39382 0.839009288
#> chr1-2573495-2575472 146.91583 0.835913313
#> chr1-954372-958413   144.81406 0.832817337
#> chr1-8584964-8586531 144.25964 0.829721362
#> chr1-2507261-2510518 142.61074 0.826625387
#> chr1-2130011-2131484 141.84665 0.823529412
#> chr1-6613890-6615140 141.29329 0.820433437
#> chr1-2221250-2223390 137.61758 0.817337461
#> chr1-1406028-1408272 137.55682 0.814241486
#> chr1-1283373-1285555 136.39638 0.811145511
#> chr1-8085460-8087248 135.60007 0.808049536
#> chr1-1207999-1210168 134.51559 0.804953560
#> chr1-6684553-6686243 129.16952 0.801857585
#> chr1-1002063-1006231 128.99640 0.798761610
#> chr1-1589986-1591117 126.80636 0.795665635
#> chr1-6761132-6762608 126.40764 0.792569659
#> chr1-994056-995654   125.59260 0.789473684
#> chr1-973840-977754   124.71487 0.786377709
#> chr1-762106-763359   123.17802 0.783281734
#> chr1-948133-951142   122.24861 0.780185759
#> chr1-3712392-3713775 121.80754 0.777089783
#> chr1-2120455-212065 116.93195 0.773993808
#> chr1-1306174-1308201 113.16641 0.770897833
#> chr1-2082091-2083429 112.73853 0.767801858
#> chr1-3565942-3569718 110.18451 0.764705882
#> chr1-3688192-3690359 109.72059 0.761609907
#> chr1-2143708-2144981 108.88198 0.758513932
#> chr1-1147592-1153647 105.88473 0.755417957
#> chr1-1135873-1137356 105.68426 0.752321981
#> chr1-8482480-8485295 104.61046 0.749226006
#> chr1-6319594-6322140 103.52703 0.746130031
#> chr1-6295272-6296706 100.78740 0.743034056
#> chr1-1092026-1094240 100.73212 0.739938080
#> chr1-3535177-3536355 100.46039 0.736842105
#> chr1-8000672-8003534  99.37711 0.733746130
#> chr1-967806-970136    88.56877 0.730650155
#> chr1-9352328-9354391  86.97476 0.727554180
#> chr1-8762907-8763964  86.29183 0.724458204
#> chr1-6304964-6307369  84.12494 0.721362229
#> chr1-998895-1000200   82.58409 0.718266254
#> chr1-1140204-1144858  82.27853 0.715170279
#> chr1-1057192-1058404  82.26495 0.712074303
#> chr1-1695549-1696821  80.78735 0.708978328
#> chr1-3662633-3664352  74.64851 0.705882353
#> chr1-6418151-6420435  73.54881 0.702786378
#> chr1-8487258-8488478  73.21975 0.699690402
#> chr1-6187544-6188439  72.91401 0.696594427
#> chr1-901313-902847    72.70957 0.693498452
#> chr1-2454600-2456271  72.68043 0.690402477
#> chr1-1534779-1536172  72.49716 0.687306502
#> chr1-3446985-3448712  72.11667 0.684210526
#> chr1-8186838-8187845  71.40895 0.681114551
#> chr1-1800682-1801583  71.11832 0.678018576
#> chr1-2079917-2080950  68.57285 0.674922601
```

--------------------------------

### Example: Run chromVAR on Seurat Object

Source: https://stuartlab.org/signac/1.16.0/reference/runchromvar

Example of running RunChromVAR directly on a Seurat object, specifying the human genome build. This requires the BSgenome.Hsapiens.UCSC.hg19 package.

```R
library(BSgenome.Hsapiens.UCSC.hg19)
RunChromVAR(object = atac_small, genome = BSgenome.Hsapiens.UCSC.hg19)
```

--------------------------------

### Download 10x Multiome Data

Source: https://stuartlab.org/signac/1.16.0/articles/pbmc_multiomic

Download the necessary data files for the 10x multiomic dataset. Ensure you have wget installed.

```shell
wget https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5
wget https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz
wget https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz.tbi
```

--------------------------------

### Example Usage of MotifPlot

Source: https://stuartlab.org/signac/1.16.0/reference/motifplot

Demonstrates how to use MotifPlot to visualize motifs from a Seurat object. Ensure the 'atac_small' object and 'Motifs' function are available.

```r
# \donttest{
motif.obj <- Motifs(atac_small)
MotifPlot(atac_small, motifs = head(colnames(motif.obj)))
#> Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
#> of ggplot2 3.3.4.
#> ℹ The deprecated feature was likely used in the ggseqlogo package.
#>   Please report the issue at <https://github.com/omarwagih/ggseqlogo/issues>.

# }
```

--------------------------------

### Example: Creating and Assigning Fragment Objects

Source: https://stuartlab.org/signac/reference/fragments

Shows how to create a Fragment object from a file path and assign it to an assay or the entire Seurat object. Ensure the path to the fragment file is correct and that cell information is provided.

```R
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(
  path = fpath,
  cells = colnames(atac_small),
  validate.fragments = FALSE
)
#> Computing hash
Fragments(atac_small, ...)[["bins"]] <- fragments
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(
  path = fpath,
  cells = colnames(atac_small),
  validate.fragments = FALSE
)
#> Computing hash
Fragments(atac_small) <- fragments
```

--------------------------------

### Get Gene Coordinates Example

Source: https://stuartlab.org/signac/1.16.0/reference/lookupgenecoords

Example of using LookupGeneCoords to find the coordinates for a specific gene within a Seurat object. Ensure the 'atac_small' object and the 'MIR1302-10' gene are available in your environment.

```r
LookupGeneCoords(atac_small, gene = "MIR1302-10")
#> GRanges object with 1 range and 0 metadata columns:
#>       seqnames      ranges strand
#>          <Rle>   <IRanges>  <Rle>
#>   [1]     chr1 29554-31109      *
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
```

--------------------------------

### Set up Parallel Processing with 4 Cores

Source: https://stuartlab.org/signac/articles/future

Initializes the 'future' package to use 4 parallel workers. It also sets a global option for the maximum size of future globals. This setup is used for the first set of timing measurements.

```R
library(future)
plan("multicore", workers = 4)
options(future.globals.maxSize = 100000 * 1024^2)
```

--------------------------------

### Downloading benchmarking data

Source: https://stuartlab.org/signac/articles/future

These commands download the necessary data files for benchmarking the FeatureMatrix function. Ensure you have wget installed and sufficient disk space.

```bash
# download data
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_fragments.tsv.gz
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_fragments.tsv.gz.tbi
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_peaks.bed
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_singlecell.csv
```

--------------------------------

### Example Usage of GetCellsInRegion

Source: https://stuartlab.org/signac/1.16.0/reference/getcellsinregion

Demonstrates how to use GetCellsInRegion to extract cell names from a fragments file for a specified genomic region. Ensure the 'Signac' package is installed and the fragments file path is correctly set.

```R
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
GetCellsInRegion(tabix = fpath, region = "chr1-10245-762629")
#> $`chr1:10245-762629`
#>  [1] "AAAGATGAGGCTAAAT-1" "AAACTCGTCTGGCACG-1" "AAACTCGTCTGGCACG-1"
#>  [4] "AAACTGCAGTCTGTGT-1" "AAACTGCTCCTATCCG-1" "AAAGGATTCCTTACGC-1"
#>  [7] "AAAGATGAGAAGGGCG-1" "AAACGAAGTGCCCGAT-1" "AAACTGCAGTCTGTGT-1"
#> [10] "AAACGAAAGGCTTCGC-1" "AAACTCGCAGCGTCGT-1" "AAACGAAGTGCCCGAT-1"
#> [13] "AAACGAATCCTTACGC-1" "AAAGGATCATGGAGGT-1" "AAACTGCTCCTATCCG-1"
#> [16] "AAAGATGCACGTTACA-1" "AAAGATGTCCACACCT-1" "AAACGAAAGGCTTCGC-1"
#> [19] "AAACTGCAGAATCAAC-1" "AAACTCGGTTTGATCG-1" "AAAGATGCAAGTCTGT-1"
#> [22] "AAACTCGTCAGGTCTA-1" "AAACGAAAGGCTTCGC-1" "AAACTGCGTGCATTCA-1"
#> [25] "AAACTGCTCCTATCCG-1" "AAACTGCCATGTATCG-1" "AAAGGATTCCTTACGC-1"
#> [28] "AAACTGCCAAGCCAGA-1" "AAACTCGAGTACTCTG-1" "AAACTGCTCGTTCAGA-1"
#> [31] "AAAGGATGTCGTAATC-1" "AAAGGATTCCTTACGC-1" "AAACGAAGTCAGGCTC-1"
#> [34] "AAACTGCAGCTCCATA-1" "AAAGATGAGTCCAGAG-1" "AAAGATGTCCACACCT-1"
#> [37] "AAAGATGTCCACACCT-1" "AAACTGCTCATTCATC-1" "AAACGAAAGCGAGCTA-1"
#> [40] "AAACTCGGTGGATTCT-1" "AAACGAAAGGCTTCGC-1" "AAACTCGTCTGGCACG-1"
#> [43] "AAAGATGTCTAGCAGT-1" "AAAGGATCAGATGGCA-1" "AAACTGCGTACTAGAA-1"
#> [46] "AAACGAAAGGCTTCGC-1" "AAAGATGAGGCTAAAT-1" "AAAGATGAGGCTAAAT-1"
#> [49] "AAACTCGAGTACTCTG-1" "AAAGATGTCCTGAAAC-1" "AAAGGATCAAGGGAGG-1"
#> [52] "AAAGATGCAGCACATT-1" "AAACTCGGTTTGATCG-1" "AAACTCGGTGGATTCT-1"
#> [55] "AAACTCGCAAAGAGAG-1" "AAAGATGAGTCGACCC-1" "AAACTCGCATCACAGT-1"
#> [58] "AAAGGATTCTATGAGC-1" "AAAGGATGTCCCTTTG-1" "AAACTGCCAACAAACA-1"
#> [61] "AAAGATGCATGACTGT-1" "AAAGATGAGAAGGGCG-1" "AAACTCGTCCACTAGA-1"
#> [64] "AAAGATGCACGTTACA-1" "AAAGATGCAGCGTCGT-1" "AAACGAAAGGCTTCGC-1"
#> [67] "AAAGGATGTCTAAGAA-1" "AAACGAAGTTGTATCG-1" "AAAGGATAGAACCATA-1"
#> [70] "AAACTCGCATGTGGGA-1"
#> 

```

--------------------------------

### Example: Accessing Fragments from an Assay

Source: https://stuartlab.org/signac/reference/fragments

Demonstrates how to get fragment objects associated with a specific assay (e.g., 'peaks') within a Seurat object. This is useful for inspecting the fragments linked to a particular data modality.

```R
Fragments(atac_small, ...)
#> list()
Fragments(atac_small, ...)
#> list()
```

--------------------------------

### Get Cells in Region Example

Source: https://stuartlab.org/signac/reference/getcellsinregion

Demonstrates how to use GetCellsInRegion to extract cell names that have reads within a specific genomic region. Requires a Tabix object and a region string. The output is a list where keys are regions and values are vectors of cell names.

```R
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
GetCellsInRegion(tabix = fpath, region = "chr1-10245-762629")
#> $`chr1:10245-762629`
#>  [1] "AAAGATGAGGCTAAAT-1" "AAACTCGTCTGGCACG-1" "AAACTCGTCTGGCACG-1"
#>  [4] "AAACTGCAGTCTGTGT-1" "AAACTGCTCCTATCCG-1" "AAAGGATTCCTTACGC-1"
#>  [7] "AAAGATGAGAAGGGCG-1" "AAACGAAGTGCCCGAT-1" "AAACTGCAGTCTGTGT-1"
#> [10] "AAACGAAAGGCTTCGC-1" "AAACTCGCAGCGTCGT-1" "AAACGAAGTGCCCGAT-1"
#> [13] "AAACGAATCCTTACGC-1" "AAAGGATCATGGAGGT-1" "AAACTGCTCCTATCCG-1"
#> [16] "AAAGATGCACGTTACA-1" "AAAGATGTCCACACCT-1" "AAACGAAAGGCTTCGC-1"
#> [19] "AAACTGCAGAATCAAC-1" "AAACTCGGTTTGATCG-1" "AAAGATGTCAAGTCTGT-1"
#> [22] "AAACTCGTCAGGTCTA-1" "AAACGAAAGGCTTCGC-1" "AAACTGCGTGCATTCA-1"
#> [25] "AAACTGCTCCTATCCG-1" "AAACTGCCATGTATCG-1" "AAAGGATTCCTTACGC-1"
#> [28] "AAACTGCCAAGCCAGA-1" "AAACTCGAGTACTCTG-1" "AAACTGCTCGTTCAGA-1"
#> [31] "AAAGGATGTCGTAATC-1" "AAAGGATTCCTATGAGC-1" "AAACGAAGTCAGGCTC-1"
#> [34] "AAACTGCAGCTCCATA-1" "AAAGATGAGTCCAGAG-1" "AAAGATGTCCACACCT-1"
#> [37] "AAAGATGTCCACACCT-1" "AAACTGCTCATTCATC-1" "AAACGAAAGCGAGCTA-1"
#> [40] "AAACTCGGTGGATTCT-1" "AAACGAAAGGCTTCGC-1" "AAACTCGTCTGGCACG-1"
#> [43] "AAAGATGTCTAGCAGT-1" "AAAGGATCAGATGGCA-1" "AAACTGCGTACTAGAA-1"
#> [46] "AAACGAAAGGCTTCGC-1" "AAAGATGAGGCTAAAT-1" "AAAGATGAGGCTAAAT-1"
#> [49] "AAACTCGAGTACTCTG-1" "AAAGATGTCCTGAAAC-1" "AAAGGATCAAGGGAGG-1"
#> [52] "AAAGATGCAGCACATT-1" "AAACTCGGTTTGATCG-1" "AAACTCGGTGGATTCT-1"
#> [55] "AAACTCGCAAAGAGAG-1" "AAAGATGAGTCGACCC-1" "AAACTCGCATCACAGT-1"
#> [58] "AAAGGATTCTATGAGC-1" "AAAGGATGTCCCTTTG-1" "AAACTGCCAACAAACA-1"
#> [61] "AAAGATGCATGACTGT-1" "AAAGATGAGAAGGGCG-1" "AAACTCGTCCACTAGA-1"
#> [64] "AAAGATGCACGTTACA-1" "AAAGATGCAGCGTCGT-1" "AAACGAAAGGCTTCGC-1"
#> [67] "AAAGGATGTCTAAGAA-1" "AAACGAAGTTGTATCG-1" "AAAGGATAGAACCATA-1"
#> [70] "AAACTCGCATGTGGGA-1"
#> 

```

--------------------------------

### Call Peaks with Custom MACS2 Path and Output Directory

Source: https://stuartlab.org/signac/1.16.0/reference/callpeaks

This example specifies a custom path to the MACS2 executable and a custom output directory for the peak calling results. Ensure the provided 'macs2.path' is correct.

```R
pbmc <- CallPeaks(pbmc, macs2.path = "/path/to/macs2", outdir = "/path/to/output")

```

--------------------------------

### Initialize ChromatinAssay with Fragment Path

Source: https://stuartlab.org/signac/1.16.0/articles/data_structures

Creates a ChromatinAssay by directly providing the path to a fragment file. Assumes the file contains fragments for all cells in the assay and cell names match.

```R
chrom_assay <- CreateChromatinAssay(
  counts = counts,
  genome = "hg19",
  fragments = frag.path
)
```

--------------------------------

### Call Peaks using Fragment File Path

Source: https://stuartlab.org/signac/1.16.0/reference/callpeaks

This example demonstrates calling peaks by providing the path to a fragment file. The function will process the specified fragment file to identify peaks.

```R
peaks <- CallPeaks("path/to/fragments.tsv.gz")

```

--------------------------------

### Set up Parallel Processing with 10 Cores

Source: https://stuartlab.org/signac/articles/future

Reconfigures the 'future' package to use 10 parallel workers. This is done to compare performance against the previous 4-core setup.

```R
plan("multicore", workers = 10)
```

--------------------------------

### Setting up and running FeatureMatrix sequentially

Source: https://stuartlab.org/signac/articles/future

This R code block demonstrates how to load Signac, create a Fragment object, and then run the FeatureMatrix function sequentially for benchmarking purposes. It measures the time taken for multiple replicates.

```r
library(Signac)

# load data
fragments <- "../vignette_data/atac_pbmc_10k_nextgem_fragments.tsv.gz"
peaks.10k <- read.table(
  file = "../vignette_data/atac_pbmc_10k_nextgem_peaks.bed",
  col.names = c("chr", "start", "end")
)
peaks <- GenomicRanges::makeGRangesFromDataFrame(peaks.10k)
md <- read.csv("../vignette_data/atac_pbmc_10k_nextgem_singlecell.csv", row.names = 1, header = TRUE)[-1, ]
cells <- rownames(md[md[['is__cell_barcode']] == 1, ])

fragments <- CreateFragmentObject(path = fragments, cells = cells, validate.fragments = FALSE)

# set number of replicates
nrep <- 5
results <- data.frame()
process_n <- 2000

# run sequentially
timing.sequential <- c()
for (i in seq_len(nrep)) {
  start <- Sys.time()
  fmat <- FeatureMatrix(fragments = fragments, features = peaks, cells = cells, process_n = process_n)
  timing.sequential <- c(timing.sequential, as.numeric(Sys.time() - start, units = "secs"))
}
res <- data.frame(
  "setting" = rep("Sequential", nrep),
  "cores" = rep(1, nrep),
  "replicate" = seq_len(nrep),
  "time" = timing.sequential
)
results <- rbind(results, res)
```

--------------------------------

### Install Current Release of Signac

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs the latest stable version of Signac from CRAN. Requires setting repositories to include Bioconductor dependencies.

```r
setRepositories(ind=1:3) # needed to automatically install Bioconductor dependencies
install.packages("Signac")
```

--------------------------------

### Example Usage of MatchRegionStats

Source: https://stuartlab.org/signac/1.16.0/reference/matchregionstats

Demonstrates how to use the MatchRegionStats function with sample data. It matches regions based on the 'percentile' feature and selects 10 regions.

```R
metafeatures <- SeuratObject::GetAssayData(
  object = atac_small[['peaks']], layer = 'meta.features'
)
query.feature <- metafeatures[1:10, ]
features.choose <- metafeatures[11:nrow(metafeatures), ]
MatchRegionStats(
  meta.feature = features.choose,
  query.feature = query.feature,
  features.match = "percentile",
  n = 10
)
```

--------------------------------

### Example Usage of AnnotationPlot

Source: https://stuartlab.org/signac/1.16.0/reference/annotationplot

Demonstrates how to use AnnotationPlot with a Seurat object and a specified genomic region. This example plots gene annotations for a given region.

```r
# \donttest{
AnnotationPlot(object = atac_small, region = c("chr1-29554-39554"))
# > Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
# > ℹ Please use tidy evaluation idioms with `aes()`.
# > ℹ See also `vignette("ggplot2-in-packages")` for more information.
# > ℹ The deprecated feature was likely used in the Signac package.
# >   Please report the issue at <https://github.com/stuart-lab/signac/issues>.
# > Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
# > ℹ Please use `linewidth` instead.
# > ℹ The deprecated feature was likely used in the Signac package.
# >   Please report the issue at <https://github.com/stuart-lab/signac/issues>.

# }

```

--------------------------------

### Load Signac and ggplot2, and load PBMC dataset

Source: https://stuartlab.org/signac/1.16.0/articles/visualization

Load the necessary libraries and the PBMC dataset for visualization. Ensure the 'pbmc.qs2' file is accessible.

```r
library(Signac)
library(ggplot2)

# load PBMC dataset
pbmc <- qs2::qs_read("pbmc.qs2")
```

--------------------------------

### R Session Information Details

Source: https://stuartlab.org/signac/articles/visualization

Provides a detailed breakdown of the R environment, including base packages, attached packages, and loaded namespaces, along with their versions.

```text
## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Asia/Singapore
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] GenomicRanges_1.62.1 Seqinfo_1.0.0        IRanges_2.44.0      
## [4] S4Vectors_0.48.0     BiocGenerics_0.56.0  generics_0.1.4      
## [7] ggplot2_4.0.2        Signac_1.17.0       
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         dplyr_1.2.0              farver_2.1.2            
##  [4] Biostrings_2.78.0        S7_0.2.1                 bitops_1.0-9            
##  [7] fastmap_1.2.0            tweenr_2.0.3             stringfish_0.18.0       
## [10] digest_0.6.39            dotCall64_1.2            lifecycle_1.0.5         
```

--------------------------------

### Create and Assign Fragment Objects

Source: https://stuartlab.org/signac/1.16.0/reference/fragments

Create a Fragment object from a file path and assign it to an assay or the Seurat object. Ensure the path to the fragments file is correctly specified.

```r
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(
  path = fpath,
  cells = colnames(atac_small),
  validate.fragments = FALSE
)
#> Computing hash
Fragments(atac_small[["bins"]]) <- fragments
```

```r
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(
  path = fpath,
  cells = colnames(atac_small),
  validate.fragments = FALSE
)
#> Computing hash
Fragments(atac_small) <- fragments
```

--------------------------------

### Install Human hg19 Genome and Annotation Packages

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs the BSgenome and EnsDb packages for the human hg19 genome assembly and its corresponding gene annotation from Bioconductor.

```r
BiocManager::install(c('BSgenome.Hsapiens.UCSC.hg19', 'EnsDb.Hsapiens.v75'))
```

--------------------------------

### Download benchmarking data

Source: https://stuartlab.org/signac/1.16.0/articles/future

Download necessary data files for benchmarking Signac functions. These files include fragment information, peak locations, and single-cell metadata.

```bash
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_fragments.tsv.gz
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_fragments.tsv.gz.tbi
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_peaks.bed
wget https://cf.10xgenomics.com/samples/cell-atac/2.0.0/atac_pbmc_10k_nextgem/atac_pbmc_10k_nextgem_singlecell.csv
```

--------------------------------

### Downsample Features Example

Source: https://stuartlab.org/signac/reference/downsamplefeatures

Demonstrates how to use the DownsampleFeatures function to randomly downsample features from a Seurat object. This example sets the number of features to retain to 10.

```r
DownsampleFeatures(atac_small, n = 10)
```

--------------------------------

### List Fragment Class Methods

Source: https://stuartlab.org/signac/1.16.0/articles/data_structures

Displays all available methods for the 'Fragment' class in R, useful for exploring its functionality.

```R
methods(class = 'Fragment')
```

--------------------------------

### Install Older Signac Releases

Source: https://stuartlab.org/signac/1.16.0/articles/install

Installs a specific older version of Signac from the CRAN archive using the 'remotes' package. Replace '0.2.5' with the desired version number.

```r
if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")

# replace "0.2.5" with the version that you want to install
remotes::install_version(package = 'Signac', version = package_version('0.2.5'))
```

--------------------------------

### Download TF1 Dataset Files

Source: https://stuartlab.org/signac/1.16.0/articles/mito

Download ATAC and mitochondrial genome data files from Zenodo using wget.

```bash
# ATAC data
wget https://zenodo.org/record/3977808/files/TF1.filtered.fragments.tsv.gz
wget https://zenodo.org/record/3977808/files/TF1.filtered.fragments.tsv.gz.tbi
wget https://zenodo.org/record/3977808/files/TF1.filtered.narrowPeak.gz

# mitochondrial genome data
wget https://zenodo.org/record/3977808/files/TF1_filtered.A.txt.gz
wget https://zenodo.org/record/3977808/files/TF1_filtered.T.txt.gz
wget https://zenodo.org/record/3977808/files/TF1_filtered.C.txt.gz
wget https://zenodo.org/record/3977808/files/TF1_filtered.G.txt.gz
wget https://zenodo.org/record/3977808/files/TF1_filtered.chrM_refAllele.txt.gz
wget https://zenodo.org/record/3977808/files/TF1_filtered.depthTable.txt.gz
```
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