This R package provides a shell script and an R API for working with the Ramani et al. (2017) Hi-C data set.
The Ramani data set is published on NCBI's Gene Expression Omnibus (GEO) in the GEO series GSE84920 (titled 'Massively multiplex single-cell Hi-C'), which contains:
| GEO Sample | GEO Title | Cell Types |
|---|---|---|
| GSM2254215 | Combinatorial scHi-C Library ML1 | human ('HAP1', 'HeLa'), mouse ('MEF', 'Patski') |
| GSM2254216 | Combinatorial scHi-C Library ML2 | human ('HAP1', 'HeLa'), mouse ('MEF', 'Patski') |
| GSM2254217 | Combinatorial scHi-C Library ML3 | human ('GM12878', 'K562'), mouse ('MEF', 'Patski') |
| GSM2254218 | Combinatorial scHi-C Library PL1 | human ('HAP1', 'HeLa'), mouse ('MEF', 'Patski') |
| GSM2254219 | Combinatorial scHi-C Library PL2 | human ('HAP1', 'HeLa'), mouse ('MEF', 'Patski') |
| GSM2438426** | Combinatorial scHi-C Library ML4 | human ('Asynchronous', 'Nocadazole'), mouse ('Patski') |
The subsetted example GSM2254215_ML1 files in the system.file("extdata", package = "GSE84920.parser") folder were obtained by first downloading relevant GEO files and truncating using the following Bash script:
url_path="https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSM2254215&format=file"
sample=GSM2254215_ML1
types=(percentages validPairs assignments)
tmpfile=$(mktemp)
mkdir -p inst/extdata/
for type in "${types[@]}"; do
src=$sample.$type.txt.gz
dest=inst/extdata/$sample.rows=1-1000.$type.txt
curl "$url_path&file=$src" -o "$tmpfile"
zcat "$tmpfile" | head -1000 > "$dest"
gzip "$dest"
done
rm -- "$tmpfile"This R package provides functions for reading the above GEO data set, and other data with the same file formats, into R. It also includes a small subset of the ML1 data for the purpose of illustrating and testing the different functions:
> library(GSE84920.parser)
> path <- system.file("extdata", package = "GSE84920.parser")
> dir(path)
[1] "combo_hg19_mm10.genomesize"
[2] "GSM2254215_ML1.rows=1-1000_assignments.txt.gz"
[3] "GSM2254215_ML1.rows=1-1000.percentages.txt.gz"
[4] "GSM2254215_ML1.rows=1-1000.validPairs.txt.gz" Lets look at the content of each of these *.txt.gz files:
> file <- file.path(path, "GSM2254215_ML1.rows=1-1000_assignments.txt.gz")
> a <- read_assignments(file)
> a
# A tibble: 1,000 x 4
readname left_inner_barcode right_inner_barc… outer_barcode
<chr> <chr> <chr> <chr>
1 D00584:136:HMTLJBCXX:1:11… CTCTCACG CTCTCACG TCAGATGC
2 D00584:136:HMTLJBCXX:1:11… GCACCATG GCACCATG GTGTAGCA
3 D00584:136:HMTLJBCXX:1:11… AGGTGCGA AGGTGCGA GTATCTAT
4 D00584:136:HMTLJBCXX:1:11… GCCTTAGG GCCTTAGG CAGCATAT
5 D00584:136:HMTLJBCXX:1:11… CACCTGTG CACCTGTG TACTAAGC
6 D00584:136:HMTLJBCXX:1:11… CCGCTACG CCGCTACG CAGCATAT
7 D00584:136:HMTLJBCXX:1:11… GCCTCGAA GCCTCGAA GTATCTAT
8 D00584:136:HMTLJBCXX:1:11… CTGGTCAC CTGGTCAC TTGACCAT
9 D00584:136:HMTLJBCXX:1:11… CTGCGTAG CTGCGTAG TATCTTGT
10 D00584:136:HMTLJBCXX:1:11… CACGACCT CACGACCT GATGATCC
# … with 990 more rows> p <- read_percentages(file)
> p
# A tibble: 1,000 x 16
hg19_frac mm10_frac hg19_count mm10_count pair_count inner_barcode outer_barcode is_observed Col10 dpnii_1x
<dbl> <dbl> <int> <int> <int> <chr> <chr> <chr> <chr> <int>
1 1.000 0.0000356 28050 1 28052 ACCACCAC TCAGATGC True All 55603
2 0.680 0.320 19081 8970 28052 ACCACCAC TCAGATGC Randomized All 55603
3 1.000 0.0000370 27010 1 28052 ACCACCAC TCAGATGC True Long 55603
4 0.683 0.317 18444 8555 28052 ACCACCAC TCAGATGC Randomized Long 55603
5 0 1 0 1 1 CATAGCGC ACTTGATA True All 2
6 1 0 1 0 1 CATAGCGC ACTTGATA Randomized All 2
7 0 1 0 1 1 CATAGCGC ACTTGATA True Long 2
8 1 0 1 0 1 CATAGCGC ACTTGATA Randomized Long 2
9 1 0 2 0 2 GGCCGTTC GCCATTAA True All 4
10 1 0 2 0 2 GGCCGTTC GCCATTAA Randomized All 4
# … with 990 more rows, and 6 more variables: dpnii_2x <int>, dpnii_3x <int>, dpnii_4x <int>,
# cistrans_ratio <dbl>, hela_allele_frac <dbl>, celltype <chr>> file <- file.path(path, "GSM2254215_ML1.rows=1-1000.validPairs.txt.gz")
> vp <- read_validpairs(file)
> vp
# A tibble: 1,000 x 17
chr_a start_a end_a chr_b start_b end_b readname col8 col9 col10 col11 inner_barcode outer_barcode
<chr> <int> <int> <chr> <int> <int> <chr> <int> <int> <chr> <chr> <chr> <chr>
1 mous… 2.28e7 2.28e7 mous… 5.33e7 5.33e7 D00584:… 37 42 + - ATCCGCGG GTCATAGT
2 huma… 8.86e7 8.86e7 huma… 8.91e7 8.91e7 D00584:… 42 42 - - GAGGAGCA CGATGACA
3 huma… 1.27e8 1.27e8 huma… 1.27e8 1.27e8 D00584:… 42 42 + + GCTACGGT AGTCGTAT
4 huma… 4.21e7 4.21e7 huma… 4.27e7 4.27e7 D00584:… 42 42 + + AGGTGCGA ATACATGT
5 huma… 2.09e8 2.09e8 huma… 2.32e8 2.32e8 D00584:… 42 35 + - GCCTCGAA GAGTACGT
6 huma… 5.57e6 5.57e6 huma… 1.50e8 1.50e8 D00584:… 42 42 + - GCTCGCTA CTAGTGAA
7 mous… 4.12e7 4.12e7 mous… 4.12e7 4.12e7 D00584:… 42 42 + - GAGGAGCA CGTTACTT
8 mous… 6.54e7 6.54e7 mous… 6.59e7 6.59e7 D00584:… 42 42 - + TCCGGACA TGTCTGCA
9 huma… 1.82e8 1.82e8 huma… 1.82e8 1.82e8 D00584:… 42 42 + - CAGGCTTG GATATAAC
10 mous… 5.86e7 5.86e7 mous… 6.33e7 6.33e7 D00584:… 42 42 + + TCACGAGC TGAGGCAA
# … with 990 more rows, and 4 more variables: col14 <chr>, col15 <int>, col16 <chr>, col17 <int>Reading the full Ramani HiC files can take quite a while, particularly the ones with "assignments" and "validPairs" data. Because of this, you might want to import these data (once) into a local database and work with the data from there. See help("import_assignments", package = "GSE84920.parser") for an example showing how to import into an SQLite database on file, which is easy since it requires zero setup.
Footnote:
**: The ML4 files are of slightly different file format than the other sets. Specifically, the *.validPairs.txt.gz file has an additional three columns appended at the end - GSE84920.parser::read_validpairs() will not be able to read those data because of that.
The package also provides a Bash script for splitting a raw HiC-count data file into chromosome-pair files, e.g. HiC sequence data files in GSE35156 (Dixon, et al., 2012). This split.sh script is located under system.file("scripts", package = "GSE84920.parser").
$ split.sh
Split HiC Count Data File into Chromosome-Pair Files
Usage:
./split.sh [options] path/to/*.summary.txt.gz
Options:
--help Display this help
--version Display the version
--dryrun Don't run anything
--intraonly Only process intra-chromosome pairs
Examples:
./split.sh --intraonly hicData/GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary.txt.gz
This outputs *.tsv.gz files:
1. GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary_chr1_vs_chr1.tsv.gz
2. GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary_chr2_vs_chr2.tsv.gz
...
25. GSE35156_GSM862720_J1_mESC_HindIII_ori_HiC.nodup.hic.summary_chrM_vs_chrM.tsv.gz
to folder hicData/GSE35156_GSM862720/ with names
Details:
The produced files are names '<name>,<chr_i>_vs_<chr_j>.tsv.gz' where <name>
is the name of input '*.summary.txt.gz' file without the extension. The
files are written to folder 'hicData/<gse_id>_<gsm_id>/' where <gse_id> and
<gsm_id> are inferred from the <name>. This folder is automatically created,
if missing.
Version: 0.1.2
Copyright: Henrik Bengtsson (2017-2019)
License: GPL (>= 3.0)
Source: https://github.com/HenrikBengtsson/ramani
Comment: This split.sh script is unrelated to the Ramani et al. (2017) study. It might be better fitted for the TopDomData R package.
-
Ramani, V., Deng, X., Qiu, R., Gunderson, K. L., Steemers, F. J., Disteche, C. M., ..., Shendure, J. (2017). Massively multiplex single-cell Hi-C. Nature methods, 14(3), 263–266. doi:10.1038/nmeth.4155, PMC5330809.
-
Dixon JR, Selvaraj S, Yue F, Kim A, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 2012 Apr 11; 485(7398):376-80, doi: 10.1038/nature11082, PMCID: PMC3356448, PMID: 22495300.
R package GSE84920.parser is only available via GitHub and can be installed in R as:
remotes::install_github("HenrikBengtsson/GSE84920.parser", ref="master")To install the pre-release version that is available in Git branch develop on GitHub, use:
remotes::install_github("HenrikBengtsson/GSE84920.parser", ref="develop")This will install the package from source.
This Git repository uses the Git Flow branching model (the git flow extension is useful for this). The develop branch contains the latest contributions and other code that will appear in the next release, and the master branch contains the code of the latest release.
Contributing to this package is easy. Just send a pull request. When you send your PR, make sure develop is the destination branch on the GSE84920.parser repository. Your PR should pass R CMD check --as-cran, which will also be checked by Travis CI and AppVeyor CI when the PR is submitted.
| Resource | GitHub | GitHub Actions | Travis CI | AppVeyor CI |
|---|---|---|---|---|
| Platforms: | Multiple | Multiple | Linux & macOS | Windows |
| R CMD check |  |
|||
| Test coverage |  |