Note: if you use HiCDCPlus in published research, please cite:
Sahin, M., Wong, W., Zhan, Y., Van Deyze, K., Koche, R., and Leslie, C. S. (2021) HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP Nature Communications, 12(3366). 10.1038/s41467-021-23749-x
To install this package, start R and enter:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HiCDCPlus")If you are reinstalling the package, we recommend erasing the associated file cache for the package. The cache folder location can be obtained by running.
HiCDCPlus can take the outputs of the popular Hi-C
pre-processing tools such as .hic (from Juicebox), .matrix, and
.allValidPairs (from HiC-Pro). It can also be used with HTClist objects
(from Bioconductor package HiTC).
In the standard workflow, one first needs to generate genomic
features present in the HiCDCPlus model (GC content,
mappability, effective length) using the construct_features
function (see Creating genomic feature files).
This can be either done for uniformly or multiple restriction fragment
binned data.
HiCDCPlus stores counts and features in a
memory-efficient way using what we name as a gi_list
instance(see The gi_list instance).
One next feeds the genomic features in the form of a
gi_list instance using
generate_bintolen_gi_list function. Then, counts can be
added to this gi_list instance using dedicated functions
for each input Hi-C file format (add_hic_counts,
add_hicpro_matrix_counts,add_hicpro_allvalidpairs.counts).
Before modeling, 1D features from the gi_list instance
coming from the bintolen file must be expanded to 2D using
expand_1D_features function. Different transformations can
be applied to combine genomic features derived for each anchor.
At the core of HiCDCPlus is an efficient implementation
of the HiC-DC
negative binomial count model for normalization and removal of biases
(see ?HiCDCPlus). A platform-agnostic parallelizable implementation is
also available in the HiCDCPlus_parallel function for
efficient interaction calling across chromosomes. The
HiCDCPlus (or HiCDCPlus_parallel) function
outputs the significance of each interaction (pvalue and
FDR adjusted p-value qvalue) along with following estimated
from the model: 1. mu: expected interaction frequency
estimated from biases, 2. sdev: the standard deviation of
expected interaction frequencies.
Once results are obtained, they can be output into text files using
gi_list_write function or to a .hic file using
the hicdc2hicfunction (where one can pass either raw
counts, observed/expected normalized counts, -log10 P-value,
-log10 P-adjusted value, or negative binomial Z-score
normalized counts: (counts-mu)/sdev to the .hic file
To detect differential significant interactions across conditions,
HiCDCPlus also provides a modified implementation of DESeq2 using
replicate Hi-C/HiChIP datasets hicdcdiff. This function
requires a (1) definition of the experimental setup (see ?hicdcdiff),
(2) a filtered set of interactions to consider, as a text file
containing columns chr, startI, and
startJ (startI<=startJ) and (3) count data per each
condition and replicate either as gi_list instances or as
output text files generated using the gi_list_write
function that can be read as valid gi_list instances using
gi_list_read. The hicdcdiff function performs
the differential analysis and outputs genomic coordinates of pairs of
regions with corresponding logFC difference, P-value and BH
adjusted P-value (see the example in Quickstart).
We next demonstrate the standard workflow to detect significant as well as differential interactions.
In this section we show a complete workflow for identifying significant interactions and differential interactions from Hi-C data across replicate experiments. For HiChIP, the functions used are the same, but the distance thresholds used are slightly reduced (recommended Dmax = 1.5e6).
Here, we identify significant interactions from HiC data at 50kb
resolution across multiple chromosomes (in the example below, across
chromosomes 21 and 22). The following example code chunk assumes that
you have downloaded a .hic file from GSE63525
and also downloaded Juicebox command
line tools. Example below runs with GSE63525_HMEC_combined.hic
and stores the path to it into the variable hicfile_path
with features generated for restriction enzyme fragments with the
pattern "GATC" in hg19 genome.
hicfile_path<-system.file("extdata", "GSE63525_HMEC_combined_example.hic", package = "HiCDCPlus")
outdir<-tempdir(check=TRUE)
#generate features
construct_features(output_path=paste0(outdir,"/hg19_50kb_GATC"),
gen="Hsapiens",gen_ver="hg19",
sig="GATC",
bin_type="Bins-uniform",
binsize=50000,
chrs=c("chr21","chr22"))## [1] "/tmp/RtmpLtoSdP/hg19_50kb_GATC_bintolen.txt.gz"If you have a multiple enzyme cocktail used to generate Hi-C data,
you can specify multiple patterns including "N" as string
to this function (e.g., sig=c(“GATC”,“GANTC”)). If you want to analyze
data binned by multiple restriction enzyme fragments, you can change
bin_type to “Bins-RE-sites”, and binsize to the number of fragments that
you would like to merge as bin (e.g., bin_type=“Bins-RE-sites” and
binsize=10 means 10 restriction fragment binning).
#generate gi_list instance
gi_list<-generate_bintolen_gi_list(
bintolen_path=paste0(outdir,"/hg19_50kb_GATC_bintolen.txt.gz"))
#add .hic counts
gi_list<-add_hic_counts(gi_list,hic_path = hicfile_path)If you have HiC-Pro outputs instead, you can use either
add_hicpro_matrix_counts or
add_hicpro_allvalidpairs_counts depending on the file
format. add_hicpro_matrix_counts function requires .bed
output from HiC-Pro matrix generation step, together with count data in
.matrix format.
#expand features for modeling
gi_list<-expand_1D_features(gi_list)
#run HiC-DC+
set.seed(1010) #HiC-DC downsamples rows for modeling
gi_list<-HiCDCPlus(gi_list) #HiCDCPlus_parallel runs in parallel across ncores
head(gi_list)## $chr21
## GInteractions object with 27498 interactions and 8 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr21 9400000-9450000 --- chr21 9400000-9450000 |
## [2] chr21 9400000-9450000 --- chr21 9450000-9500000 |
## [3] chr21 9400000-9450000 --- chr21 9500000-9550000 |
## [4] chr21 9400000-9450000 --- chr21 9550000-9600000 |
## [5] chr21 9400000-9450000 --- chr21 9600000-9650000 |
## ... ... ... ... ... ... .
## [27494] chr21 48000000-48050000 --- chr21 48050000-48100000 |
## [27495] chr21 48000000-48050000 --- chr21 48100000-48129895 |
## [27496] chr21 48050000-48100000 --- chr21 48050000-48100000 |
## [27497] chr21 48050000-48100000 --- chr21 48100000-48129895 |
## [27498] chr21 48100000-48129895 --- chr21 48100000-48129895 |
## D counts gc len mu sdev
## <integer> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 0 199 -1.162350 -2.03355 893.45296 475.73037
## [2] 50000 6 -1.018867 -1.14588 361.44390 193.01373
## [3] 100000 1 -1.034474 -1.69222 135.53550 72.95983
## [4] 150000 7 -0.962964 -1.81222 69.29311 37.75226
## [5] 200000 0 -0.672519 -11.25302 5.75596 3.88742
## ... ... ... ... ... ... ...
## [27494] 50000 648 0.6628281 -0.0888584 500.961 267.1554
## [27495] 89947 29 0.4459603 -3.9564444 105.847 57.1815
## [27496] 0 2468 0.5083961 -0.3549451 1415.297 753.0440
## [27497] 39947 170 0.2915283 -4.2225312 245.662 131.4849
## [27498] 0 97 0.0746605 -8.0901172 259.698 138.9441
## pvalue qvalue
## <numeric> <numeric>
## [1] 0.980622 1
## [2] 0.999994 1
## [3] 0.999998 1
## [4] 0.997504 1
## [5] 1.000000 1
## ... ... ...
## [27494] 0.2491665 0.919972
## [27495] 0.9626372 1.000000
## [27496] 0.0932895 0.663891
## [27497] 0.6805805 1.000000
## [27498] 0.9185031 1.000000
## -------
## regions: 963 ranges and 2 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $chr22
## GInteractions object with 27855 interactions and 8 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr22 16050000-16100000 --- chr22 16050000-16100000 |
## [2] chr22 16050000-16100000 --- chr22 16100000-16150000 |
## [3] chr22 16050000-16100000 --- chr22 16150000-16200000 |
## [4] chr22 16050000-16100000 --- chr22 16200000-16250000 |
## [5] chr22 16050000-16100000 --- chr22 16250000-16300000 |
## ... ... ... ... ... ... .
## [27851] chr22 51100000-51150000 --- chr22 51150000-51200000 |
## [27852] chr22 51100000-51150000 --- chr22 51200000-51250000 |
## [27853] chr22 51150000-51200000 --- chr22 51150000-51200000 |
## [27854] chr22 51150000-51200000 --- chr22 51200000-51250000 |
## [27855] chr22 51200000-51250000 --- chr22 51200000-51250000 |
## D counts gc len mu sdev
## <integer> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 0 72 -0.321755 -0.551744 1673.7051 1099.5644
## [2] 50000 3 -2.018640 -0.698418 484.3915 318.7682
## [3] 100000 0 -1.017838 -0.333627 278.6914 183.7235
## [4] 150000 0 -0.741770 -1.247963 121.6267 80.6071
## [5] 200000 1 -1.069967 -0.293187 95.8473 63.6817
## ... ... ... ... ... ... ...
## [27851] 50000 586 0.878598 -0.2088985 806.215 530.048
## [27852] 100000 121 0.129452 -0.8134144 282.701 186.356
## [27853] 0 2665 0.641536 0.0178216 2220.278 1458.395
## [27854] 50000 307 -0.107610 -0.5866943 639.170 420.382
## [27855] 0 445 -0.856757 -1.1912102 1307.568 859.192
## pvalue qvalue
## <numeric> <numeric>
## [1] 0.998347 1
## [2] 0.999971 1
## [3] 1.000000 1
## [4] 1.000000 1
## [5] 0.999832 1
## ... ... ...
## [27851] 0.593328 1
## [27852] 0.815618 1
## [27853] 0.307224 1
## [27854] 0.777558 1
## [27855] 0.876441 1
## -------
## regions: 1027 ranges and 2 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengths#write normalized counts (observed/expected) to a .hic file
hicdc2hic(gi_list,hicfile=paste0(outdir,'/GSE63525_HMEC_combined_result.hic'),
mode='normcounts',gen_ver='hg19')## [1] "/tmp/RtmpLtoSdP/GSE63525_HMEC_combined_result.hic"#write results to a text file
gi_list_write(gi_list,fname=paste0(outdir,'/GSE63525_HMEC_combined_result.txt.gz'))## [1] "/tmp/RtmpLtoSdP/GSE63525_HMEC_combined_result.txt.gz"HiCDCPlus results can be converted into .hic using
hicdc2hic function. Values that should be supplied as
“mode” into the hicdc2hic function for the corresponding
score stored in the .hic file are: ‘pvalue’ for -log10 significance
p-value, ‘qvalue’ for -log10 FDR corrected p-value, ‘normcounts’ for raw
counts/expected counts, ‘zvalue’ for standardized counts (raw
counts-expected counts)/modeled standard deviation of expected counts
and ‘raw’ to pass-through raw counts.
.hic files can be further converted into .cool format using hic2cool software and be visualized using HiCExplorer.
Suppose we’re interested in finding differential interactions on
chr21 and chr22 at 50kb between NSD2 and
NTKO/TKO cells given the following .hic files available in
GSE131651:
GSE131651_NSD2_LOW_arima.hic,
GSE131651_NSD2_HIGH_arima.hic,
GSE131651_TKOCTCF_new.hic,
GSE131651_NTKOCTCF_new.hic. We first find significant
interactions in each, and save results to a file:
#generate features
construct_features(output_path=paste0(outdir,"/hg38_50kb_GATC"),
gen="Hsapiens",gen_ver="hg38",
sig="GATC",bin_type="Bins-uniform",
binsize=50000,
chrs=c("chr22"))## [1] "/tmp/RtmpLtoSdP/hg38_50kb_GATC_bintolen.txt.gz"#add .hic counts
hicfile_paths<-c(
system.file("extdata", "GSE131651_NSD2_LOW_arima_example.hic", package = "HiCDCPlus"),
system.file("extdata", "GSE131651_NSD2_HIGH_arima_example.hic", package = "HiCDCPlus"),
system.file("extdata", "GSE131651_TKOCTCF_new_example.hic", package = "HiCDCPlus"),
system.file("extdata", "GSE131651_NTKOCTCF_new_example.hic", package = "HiCDCPlus"))
indexfile<-data.frame()
for(hicfile_path in hicfile_paths){
output_path<-paste0(outdir,'/',
gsub("^(.*[\\/])", "",gsub('.hic','.txt.gz',hicfile_path)))
#generate gi_list instance
gi_list<-generate_bintolen_gi_list(
bintolen_path=paste0(outdir,"/hg38_50kb_GATC_bintolen.txt.gz"),
gen="Hsapiens",gen_ver="hg38")
gi_list<-add_hic_counts(gi_list,hic_path = hicfile_path)
#expand features for modeling
gi_list<-expand_1D_features(gi_list)
#run HiC-DC+ on 2 cores
set.seed(1010) #HiC-DC downsamples rows for modeling
gi_list<-HiCDCPlus(gi_list,ssize=0.1)
for (i in seq(length(gi_list))){
indexfile<-unique(rbind(indexfile,
as.data.frame(gi_list[[i]][gi_list[[i]]$qvalue<=0.05])[c('seqnames1',
'start1','start2')]))
}
#write results to a text file
gi_list_write(gi_list,fname=output_path)
}
#save index file---union of significants at 50kb
colnames(indexfile)<-c('chr','startI','startJ')
data.table::fwrite(indexfile,
paste0(outdir,'/GSE131651_analysis_indices.txt.gz'),
sep='\t',row.names=FALSE,quote=FALSE)We next get the union set of significant interactions and save it as
the index file, and then run hicdcdiff.
#Differential analysis using modified DESeq2 (see ?hicdcdiff)
hicdcdiff(input_paths=list(NSD2=c(paste0(outdir,'/GSE131651_NSD2_LOW_arima_example.txt.gz'),
paste0(outdir,'/GSE131651_NSD2_HIGH_arima_example.txt.gz')),
TKO=c(paste0(outdir,'/GSE131651_TKOCTCF_new_example.txt.gz'),
paste0(outdir,'/GSE131651_NTKOCTCF_new_example.txt.gz'))),
filter_file=paste0(outdir,'/GSE131651_analysis_indices.txt.gz'),
output_path=paste0(outdir,'/diff_analysis_example/'),
fitType = 'mean',
chrs = 'chr22',
binsize=50000,
diagnostics=TRUE)## $deseq2paths
## NULL
##
## $outputpaths
## [1] "/tmp/RtmpLtoSdP/diff_analysis_example/diff_resTKOoverNSD2_chr22.txt.gz"
##
## $plotpaths
## [1] "/tmp/RtmpLtoSdP/diff_analysis_example/sizefactors_chr22.pdf"
## [2] "/tmp/RtmpLtoSdP/diff_analysis_example/geomean_sizefactors_chr22.pdf"
## [3] "/tmp/RtmpLtoSdP/diff_analysis_example/plotMA_TKOoverNSD2_chr22.pdf"
## [4] "/tmp/RtmpLtoSdP/diff_analysis_example/diff_chr22_PCA.pdf"
## [5] "/tmp/RtmpLtoSdP/diff_analysis_example/dispersionplot.pdf"Suppose you provide multiple conditions in input_paths such as
input_paths=list(A=“..”,B=“..”,C=“..”), then the pairwise comparisons
reported by hicdcdiff will be B over A, C over B, C over
A.
To find TADs, we use ICE normalized Hi-C data. If you use HiC-Pro to
process counts, we suggest feeding ICE normalized .matrix files into a
gi_list instance.
gi_list<-generate_binned_gi_list(50000,chrs=c("chr21","chr22"))
gi_list<-add_hicpro_matrix_counts(gi_list,absfile_path,matrixfile_path,chrs=c("chr21","chr22")) #add paths to iced absfile and matrix files hereIf you have .hic file instead, then you can perform ICE normalization with our HiTC wrapper as follows:
hic_path<-system.file("extdata", "GSE63525_HMEC_combined_example.hic", package = "HiCDCPlus")
gi_list=hic2icenorm_gi_list(hic_path,binsize=50e3,chrs=c('chr22'),Dthreshold=400e3)You can also output a ICE normalized .hic file to the path
gsub(".hic","_icenorm.hic",hic_path) from
hic2icenorm_gi_list if you set hic_out=TRUE to
your call to this function.
HiCDCPlus converts the gi_list instance with ICE
normalized counts into TAD annotations through an implementation of
TopDom v0.0.2 (https://github.com/HenrikBengtsson/TopDom) adapted as
TopDom. We recommend call TADs with to ICE normalized counts at 50kb
resolution with window.size 10 in TopDom.
HiCDCPlus can call Juicer eigenvector function to
determine A/B compartments from .hic files.
extract_hic_eigenvectors generates text files for each
chromosome containing chromosome, start, end and compartment score
values that may need to be flipped signs for each chromosome. File paths
follow gsub(‘.hic’,’_
Genomic features can be generated using the
construct_features function. This function finds all
restriction enzyme cutsites of a given genome and genome version and
computes GC content, mappability (if a relevant .bigWig
file is provided) and effective fragment length for uniform bin or
across specified multiples of restriction enzyme cutsites of given
pattern(s).
#generate features
construct_features(output_path=paste0(outdir,"/hg19_50kb_GATC"),
gen="Hsapiens",gen_ver="hg19",
sig=c("GATC","GANTC"),bin_type="Bins-uniform",
binsize=50000,
wg_file=NULL, #e.g., 'hg19_wgEncodeCrgMapabilityAlign50mer.bigWig',
chrs=c("chr22"))## [1] "/tmp/RtmpLtoSdP/hg19_50kb_GATC_bintolen.txt.gz"#read and print
bintolen<-data.table::fread(paste0(outdir,"/hg19_50kb_GATC_bintolen.txt.gz"))
tail(bintolen,20)## bins gc len
## <char> <num> <int>
## 1: chr22-49850001-49900000 0.4833 49875
## 2: chr22-49900001-49950000 0.5126 47521
## 3: chr22-49950001-50000000 0.5139 46220
## 4: chr22-50000001-50050000 0.5472 48270
## 5: chr22-50050001-50100000 0.5241 49289
## 6: chr22-50100001-50150000 0.5014 49584
## 7: chr22-50150001-50200000 0.5466 48171
## 8: chr22-50200001-50250000 0.5232 47970
## 9: chr22-50250001-50300000 0.4675 49242
## 10: chr22-50300001-50350000 0.6117 41993
## 11: chr22-50350001-50400000 0.1997 49875
## 12: chr22-50400001-50450000 0.3623 47683
## 13: chr22-50450001-50500000 0.5526 49544
## 14: chr22-50500001-50550000 0.5126 49105
## 15: chr22-50550001-50600000 0.4790 49875
## 16: chr22-50600001-50650000 0.6103 49700
## 17: chr22-50650001-50700000 0.5927 49531
## 18: chr22-50700001-50750000 0.6473 49469
## 19: chr22-50750001-50800000 0.5409 49669
## 20: chr22-50800001-50818468 0.4792 7806
## bins gc lengi_list instanceHiCDCPlus stores features and count data in a list of
InteractionSet objects generated for each chromosome, what
we name as a gi_list instance.
A gi_list instance can be initialized through multiple
ways. One can generate a uniformly binsized gi_list
instance using generate_binned_gi_list. One can also
generate a restriction enzyme fragment binning of the genome as a
data.frame and ingest it as a gi_list instance
(see ?generate_df_gi_list) Third, one can generate some genomic features
(GC content, mappability, effective length) and restriction enzyme
fragment regions into as a bintolen file (see Creating bintolen files) and generate a
gi_list instance from this bintolen file.
Finally, one can read a gi_list instance from a file
generated by gi_list_write (see ?gi_list_read).
gi_list instanceOne can generate a uniform binsized gi_list instance for
a genome using generate_binned_gi_list:
gi_list<-generate_binned_gi_list(binsize=50000,chrs=c('chr22'),
gen="Hsapiens",gen_ver="hg19")
head(gi_list)## $chr22
## GInteractions object with 40877 interactions and 1 metadata column:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr22 0-50000 --- chr22 0-50000 |
## [2] chr22 0-50000 --- chr22 50000-100000 |
## [3] chr22 0-50000 --- chr22 100000-150000 |
## [4] chr22 0-50000 --- chr22 150000-200000 |
## [5] chr22 0-50000 --- chr22 200000-250000 |
## ... ... ... ... ... ... .
## [40873] chr22 50700000-50750000 --- chr22 50750000-50800000 |
## [40874] chr22 50700000-50750000 --- chr22 50800000-50818468 |
## [40875] chr22 50750000-50800000 --- chr22 50750000-50800000 |
## [40876] chr22 50750000-50800000 --- chr22 50800000-50818468 |
## [40877] chr22 50800000-50818468 --- chr22 50800000-50818468 |
## D
## <integer>
## [1] 0
## [2] 50000
## [3] 100000
## [4] 150000
## [5] 200000
## ... ...
## [40873] 50000
## [40874] 84234
## [40875] 0
## [40876] 34234
## [40877] 0
## -------
## regions: 1017 ranges and 0 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsgi_list instanceOne can also generate an restriction enzyme fragment binning (indeed,
any arbitrary binning) of the genome containing columns named
chr and start as a data.frame
(e.g., a data.frame read from a .bed file) and
use it to generate a gi_list instance using
generate_df_gi_list.
## $chr9
## GInteractions object with 7 interactions and 1 metadata column:
## seqnames1 ranges1 seqnames2 ranges2 | D
## <Rle> <IRanges> <Rle> <IRanges> | <integer>
## [1] chr9 1-300 --- chr9 1-300 | 0
## [2] chr9 1-300 --- chr9 300-7867 | 3933
## [3] chr9 1-300 --- chr9 7867-103938 | 55752
## [4] chr9 300-7867 --- chr9 300-7867 | 0
## [5] chr9 300-7867 --- chr9 7867-103938 | 51819
## [6] chr9 7867-103938 --- chr9 7867-103938 | 0
## [7] chr9 103938-138394717 --- chr9 103938-138394717 | 0
## -------
## regions: 4 ranges and 0 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsgi_list instance from a bintolen fileOne can generate genomic features (gc, mappability, effective length)
and restriction enzyme fragment regions as a bintolen file
(see Creating bintolen files) first and then
generate a gi_list instance from it. This instance will
readily store genomic features of the bintolen file.
#generate features
construct_features(output_path=paste0(outdir,"/hg19_50kb_GATC"),
gen="Hsapiens",gen_ver="hg19",
sig="GATC",bin_type="Bins-uniform",
binsize=50000,
wg_file=NULL, #e.g., 'hg19_wgEncodeCrgMapabilityAlign50mer.bigWig',
chrs=c("chr22"))## [1] "/tmp/RtmpLtoSdP/hg19_50kb_GATC_bintolen.txt.gz"#generate gi_list instance
gi_list<-generate_bintolen_gi_list(
bintolen_path=paste0(outdir,"/hg19_50kb_GATC_bintolen.txt.gz"))
head(gi_list)## $chr22
## GInteractions object with 40877 interactions and 1 metadata column:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr22 0-50000 --- chr22 0-50000 |
## [2] chr22 0-50000 --- chr22 50000-100000 |
## [3] chr22 0-50000 --- chr22 100000-150000 |
## [4] chr22 0-50000 --- chr22 150000-200000 |
## [5] chr22 0-50000 --- chr22 200000-250000 |
## ... ... ... ... ... ... .
## [40873] chr22 50700000-50750000 --- chr22 50750000-50800000 |
## [40874] chr22 50700000-50750000 --- chr22 50800000-50818468 |
## [40875] chr22 50750000-50800000 --- chr22 50750000-50800000 |
## [40876] chr22 50750000-50800000 --- chr22 50800000-50818468 |
## [40877] chr22 50800000-50818468 --- chr22 50800000-50818468 |
## D
## <integer>
## [1] 0
## [2] 50000
## [3] 100000
## [4] 150000
## [5] 200000
## ... ...
## [40873] 50000
## [40874] 84234
## [40875] 0
## [40876] 34234
## [40877] 0
## -------
## regions: 1017 ranges and 2 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsHiCDCPlus allows modeling with user-defined 1D (genomic features for each bin) and 2D (features belonging to an interaction) features.
Once a gi_list instance is at hand, one can ingest
counts (and 2D features) using a sparse matrix format text file
containing chr, startI, startJ
and <featurename> columns (see ?add_2D_features) for
features you would like to add. counts can be ingested this
way as well provided you have a text file containing columns named
chr, startI and startJ.
df<-data.frame(chr='chr9',start=seq(1e6,10e6,1e6))
gi_list<-generate_df_gi_list(df,Dthreshold=500e3,chrs="chr9")
feats<-data.frame(chr='chr9',
startI=seq(1e6,10e6,1e6),
startJ=seq(1e6,10e6,1e6),
counts=rpois(20,lambda=5))
gi_list[['chr9']]<-add_2D_features(gi_list[['chr9']],feats)
gi_list## $chr9
## GInteractions object with 10 interactions and 2 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr9 1000000-2000000 --- chr9 1000000-2000000 |
## [2] chr9 2000000-3000000 --- chr9 2000000-3000000 |
## [3] chr9 3000000-4000000 --- chr9 3000000-4000000 |
## [4] chr9 4000000-5000000 --- chr9 4000000-5000000 |
## [5] chr9 5000000-6000000 --- chr9 5000000-6000000 |
## [6] chr9 6000000-7000000 --- chr9 6000000-7000000 |
## [7] chr9 7000000-8000000 --- chr9 7000000-8000000 |
## [8] chr9 8000000-9000000 --- chr9 8000000-9000000 |
## [9] chr9 9000000-10000000 --- chr9 9000000-10000000 |
## [10] chr9 10000000-138394717 --- chr9 10000000-138394717 |
## D counts
## <integer> <numeric>
## [1] 0 5
## [2] 0 9
## [3] 0 7
## [4] 0 10
## [5] 0 7
## [6] 0 13
## [7] 0 9
## [8] 0 15
## [9] 0 6
## [10] 0 15
## -------
## regions: 10 ranges and 0 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsOne can also ingest 1D features using a sparse matrix format text
file containing chr, start and
<featurename> (see ?add_1D_features) and broadcast 1D
features to 2D for modeling using a user-specified function (see
?expand_1D_features). Ingesting 1D features first and then expanding has
a better memory footprint compared to using add_2D_features
directly.
df<-data.frame(chr='chr9',start=seq(1e6,10e6,1e6),end=seq(2e6,11e6,1e6))
gi_list<-generate_df_gi_list(df)
feats<-data.frame(chr='chr9',start=seq(1e6,10e6,1e6),gc=runif(10))
gi_list<-add_1D_features(gi_list,feats)
gi_list## $chr9
## GInteractions object with 27 interactions and 1 metadata column:
## seqnames1 ranges1 seqnames2 ranges2 | D
## <Rle> <IRanges> <Rle> <IRanges> | <integer>
## [1] chr9 1000000-2000000 --- chr9 1000000-2000000 | 0
## [2] chr9 1000000-2000000 --- chr9 2000000-3000000 | 1000000
## [3] chr9 1000000-2000000 --- chr9 3000000-4000000 | 2000000
## [4] chr9 2000000-3000000 --- chr9 2000000-3000000 | 0
## [5] chr9 2000000-3000000 --- chr9 3000000-4000000 | 1000000
## ... ... ... ... ... ... . ...
## [23] chr9 8000000-9000000 --- chr9 9000000-10000000 | 1000000
## [24] chr9 8000000-9000000 --- chr9 10000000-11000000 | 2000000
## [25] chr9 9000000-10000000 --- chr9 9000000-10000000 | 0
## [26] chr9 9000000-10000000 --- chr9 10000000-11000000 | 1000000
## [27] chr9 10000000-11000000 --- chr9 10000000-11000000 | 0
## -------
## regions: 10 ranges and 1 metadata column
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## DataFrame with 10 rows and 1 column
## gc
## <numeric>
## 1 0.5100410
## 2 0.6598618
## 3 0.6023221
## 4 0.4176259
## 5 0.6214595
## 6 0.0935324
## 7 0.4715000
## 8 0.7649827
## 9 0.6588052
## 10 0.3132930## $chr9
## GInteractions object with 27 interactions and 2 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 | D
## <Rle> <IRanges> <Rle> <IRanges> | <integer>
## [1] chr9 1000000-2000000 --- chr9 1000000-2000000 | 0
## [2] chr9 1000000-2000000 --- chr9 2000000-3000000 | 1000000
## [3] chr9 1000000-2000000 --- chr9 3000000-4000000 | 2000000
## [4] chr9 2000000-3000000 --- chr9 2000000-3000000 | 0
## [5] chr9 2000000-3000000 --- chr9 3000000-4000000 | 1000000
## ... ... ... ... ... ... . ...
## [23] chr9 8000000-9000000 --- chr9 9000000-10000000 | 1000000
## [24] chr9 8000000-9000000 --- chr9 10000000-11000000 | 2000000
## [25] chr9 9000000-10000000 --- chr9 9000000-10000000 | 0
## [26] chr9 9000000-10000000 --- chr9 10000000-11000000 | 1000000
## [27] chr9 10000000-11000000 --- chr9 10000000-11000000 | 0
## gc
## <numeric>
## [1] 0.253977
## [2] 0.520972
## [3] 0.426384
## [4] 0.787966
## [5] 0.693378
## ... ...
## [23] 0.9395545
## [24] 0.1689767
## [25] 0.7846432
## [26] 0.0140655
## [27] -0.7565123
## -------
## regions: 10 ranges and 1 metadata column
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsAny and all HiCDCPlus questions should be posted to the Bioconductor support site, which serves as a searchable knowledge base of questions and answers:
https://support.bioconductor.org
Posting a question and tagging with “HiCDCPlus” or “HiC-DC+” will
automatically send an alert to the package authors to respond on the
support site.
You should not email your question to the package
authors directly, as we will just reply that the question should be
posted to the Bioconductor support site instead.
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [3] BSgenome_1.75.0 rtracklayer_1.67.0
## [5] BiocIO_1.17.0 Biostrings_2.75.1
## [7] XVector_0.47.0 GenomicRanges_1.59.1
## [9] GenomeInfoDb_1.43.1 IRanges_2.41.1
## [11] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [13] generics_0.1.3 HiCDCPlus_1.15.0
## [15] rmarkdown_2.29
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3
## [3] rstudioapi_0.17.1 jsonlite_1.8.9
## [5] magrittr_2.0.3 GenomicFeatures_1.59.1
## [7] farver_2.1.2 zlibbioc_1.52.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.23.0 RCurl_1.98-1.16
## [13] base64enc_0.1-3 htmltools_0.5.8.1
## [15] S4Arrays_1.7.1 progress_1.2.3
## [17] curl_6.0.1 SparseArray_1.7.2
## [19] Formula_1.2-5 sass_0.4.9
## [21] bslib_0.8.0 htmlwidgets_1.6.4
## [23] Gviz_1.51.0 httr2_1.0.6
## [25] cachem_1.1.0 buildtools_1.0.0
## [27] GenomicAlignments_1.43.0 igraph_2.1.1
## [29] lifecycle_1.0.4 pkgconfig_2.0.3
## [31] Matrix_1.7-1 R6_2.5.1
## [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [35] MatrixGenerics_1.19.0 digest_0.6.37
## [37] colorspace_2.1-1 AnnotationDbi_1.69.0
## [39] DESeq2_1.47.1 Hmisc_5.2-0
## [41] RSQLite_2.3.8 labeling_0.4.3
## [43] filelock_1.0.3 fansi_1.0.6
## [45] httr_1.4.7 abind_1.4-8
## [47] compiler_4.4.2 bit64_4.5.2
## [49] withr_3.0.2 htmlTable_2.4.3
## [51] backports_1.5.0 BiocParallel_1.41.0
## [53] DBI_1.2.3 R.utils_2.12.3
## [55] biomaRt_2.63.0 MASS_7.3-61
## [57] rappdirs_0.3.3 DelayedArray_0.33.2
## [59] rjson_0.2.23 tools_4.4.2
## [61] foreign_0.8-87 nnet_7.3-19
## [63] R.oo_1.27.0 glue_1.8.0
## [65] restfulr_0.0.15 InteractionSet_1.35.0
## [67] grid_4.4.2 checkmate_2.3.2
## [69] cluster_2.1.6 gtable_0.3.6
## [71] R.methodsS3_1.8.2 ensembldb_2.31.0
## [73] tidyr_1.3.1 data.table_1.16.2
## [75] hms_1.1.3 xml2_1.3.6
## [77] utf8_1.2.4 pillar_1.9.0
## [79] stringr_1.5.1 splines_4.4.2
## [81] dplyr_1.1.4 BiocFileCache_2.15.0
## [83] lattice_0.22-6 deldir_2.0-4
## [85] bit_4.5.0 biovizBase_1.55.0
## [87] tidyselect_1.2.1 locfit_1.5-9.10
## [89] maketools_1.3.1 knitr_1.49
## [91] gridExtra_2.3 ProtGenerics_1.39.0
## [93] SummarizedExperiment_1.37.0 xfun_0.49
## [95] Biobase_2.67.0 matrixStats_1.4.1
## [97] stringi_1.8.4 UCSC.utils_1.3.0
## [99] lazyeval_0.2.2 yaml_2.3.10
## [101] evaluate_1.0.1 codetools_0.2-20
## [103] interp_1.1-6 tibble_3.2.1
## [105] cli_3.6.3 rpart_4.1.23
## [107] munsell_0.5.1 jquerylib_0.1.4
## [109] GenomicInteractions_1.41.0 dichromat_2.0-0.1
## [111] Rcpp_1.0.13-1 dbplyr_2.5.0
## [113] png_0.1-8 XML_3.99-0.17
## [115] parallel_4.4.2 ggplot2_3.5.1
## [117] blob_1.2.4 prettyunits_1.2.0
## [119] jpeg_0.1-10 latticeExtra_0.6-30
## [121] AnnotationFilter_1.31.0 bitops_1.0-9
## [123] VariantAnnotation_1.53.0 HiTC_1.51.0
## [125] scales_1.3.0 purrr_1.0.2
## [127] crayon_1.5.3 rlang_1.1.4
## [129] KEGGREST_1.47.0