2025-04-02
hicream is an R package designed to
perform Hi-c data differential analysis. More specifically, it performs
a pixel-level differential analysis using diffHic and,
using a two-dimensionnal connectivity constrained clustering, renders a
post hoc bound on True Discovery Proportion for each cluster. This
method allows to identify differential genomic regions and quantifies
signal in those regions.
library("hicream")## Loading required package: reticulateUsing hicream requires to load data that corresponds to
Hi-C matrices and their index, the latter in bed format. In the
following code, the paths to Hi-C matrices and the index file path are
used in the loadData function alongside the chromosome
number. The option normalize = TRUE allows to perform a
cyclic LOESS normalization. The output is an InteractionSet
object.
replicates <- 1:2
cond <- "90"
allBegins <- interaction(expand.grid(replicates, cond), sep = "-")
allBegins <- as.character(allBegins)
chromosome <- 1
nbChr <- 1
allMat <- sapply(allBegins, function(ab) {
matFile <- paste0("Rep", ab, "-chr", chromosome, "_200000.bed")
})
index <- system.file("extdata", "index.200000.longest18chr.abs.bed",
package = "hicream")
format <- rep("HiC-Pro", length(replicates) * length(cond) * nbChr)
binsize <- 200000
files <- system.file("extdata", unlist(allMat), package = "hicream")
exData <- loadData(files, index, chromosome, normalize = TRUE)## chr(0)## ## chr(0)## pighic datasetThe pighic dataset has been produced using 6 Hi-C
matrices (3 in each condition) obtained from two different developmental
stages of pig embryos (90 and 110 days of gestation) corresponding to
chromosome 1. The dataset contains two elements, the first is an output
of the loadData function corresponding to normalized data.
The second is the vector giving, for each matrix, the condition it
belongs to.
data("pighic")
head(pighic)## $data
## class: InteractionSet
## dim: 21 6
## metadata(1): width
## assays(2): counts offset
## rownames: NULL
## rowData names(2): anchor1.id anchor2.id
## colnames: NULL
## colData names(1): totals
## type: ReverseStrictGInteractions
## regions: 6
##
## $conditions
## [1] "90" "90" "90" "110" "110" "110"hicream testOnce data have been loaded, the three steps of the analysis can be performed.
performTest
functionIn this first step, the output of a loadData object is
used, with a vector indicating the condition of each sample. Here, we
use data stored in pighic. performTest outputs
the result of the diffHic pixel-level differential
analysis.
resdiff <- performTest(pighic$data, pighic$conditions)
resdiff## Testing for differential interactions using diffHic.
##
## region1 region2 p.value p.adj logFC
## 1 1125 1125 0.7318482 0.8538229 -0.01830721
## 2 1125 1126 0.0562111 0.2342558 -0.16338780
## [ reached 'max' / getOption("max.print") -- omitted 19 rows ]summary(resdiff)## Summary of diffHic results.## p.value p.adj logFC
## Min. :0.0002064 Min. :0.004334 Min. :-0.163388
## 1st Qu.:0.0669302 1st Qu.:0.234256 1st Qu.:-0.034500
## Median :0.2751093 Median :0.525209 Median : 0.003099
## Mean :0.3960997 Mean :0.580792 Mean : 0.022271
## 3rd Qu.:0.6924986 3rd Qu.:0.853823 3rd Qu.: 0.040990
## Max. :0.9537715 Max. :0.953771 Max. : 0.349596Several plotting options allow to look at the \(p\)-value map, adjusted \(p\)-value map or log-fold-change map.
plot(resdiff)
plot(resdiff, which_plot = "p.adj")
plot(resdiff, which_plot = "logFC")
The AggloClust2D function uses the output of
loadData function in order to build a connectivity graph of
pixels and perform a two-dimensionnal hierarchical clustering under the
connectivity constraint. The function renders a clustering corresponding
to the optimal number of clusters found by the elbow heuristic. However,
a clustering corresponding to any other number of clusters (chosen by
the user) can be obtained by specifying a value for the input argument
nbClust.
res2D <- AggloClust2D(pighic$data)
res2D ## Tree obtained from constrained 2D clustering.
##
## Call:
## AggloClust2D(pighic$data)
##
## Cluster method : Constrained HC with Ward linkage from sklearn
## Distance : euclidean
## Number of objects: 21
##
##
## Optimal number of clusters: 7
##
## Clustering:
## 1 1 5 2 2 2 1 1 2 4 ...summary(res2D)## Summary of 2D constrained clustering results.## Length Class Mode
## merge 40 -none- numeric
## height 20 -none- numeric
## order 21 -none- numeric
## labels 21 -none- numeric
## method 1 -none- character
## call 2 -none- call
## dist.method 1 -none- characterPlotting the output shows the tree that corresponds to the hierarchy of clusters.
plot(res2D)
Post hoc inference is performed by the postHoc function,
using the output of performTest, and a clustering (the
latter can be obtained with AggloClust2D). The user sets a
level of test confidence \(\alpha\),
typically equal to \(0.05\).
clusters <- res2D$clustering
alpha <- 0.05
resposthoc <- postHoc(resdiff, clusters, alpha)
resposthoc## Posthoc results.## Warning: `...` must be empty in `format.tbl()`
## Caused by error in `format_tbl()`:
## ! `...` must be empty.
## ✖ Problematic argument:
## • max = 10## # A tibble: 21 × 10
## # Groups: clust [7]
## region1 region2 clust TPRate p.value p.adj logFC meanlogFC varlogFC
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1125 1125 1 0 0.732 0.854 -0.0183 -0.0546 0.00670
## 2 1125 1126 1 0 0.0562 0.234 -0.163 -0.0546 0.00670
## 3 1125 1127 5 0 0.800 0.884 -0.00890 -0.00890 NA
## 4 1125 1128 2 0 0.882 0.926 -0.00775 0.0595 0.0225
## 5 1125 1129 2 0 0.0313 0.234 0.275 0.0595 0.0225
## 6 1125 1130 2 0 0.269 0.525 -0.0659 0.0595 0.0225
## 7 1126 1126 1 0 0.692 0.854 0.0280 -0.0546 0.00670
## 8 1126 1127 1 0 0.186 0.489 -0.0648 -0.0546 0.00670
## 9 1126 1128 2 0 0.409 0.716 0.0364 0.0595 0.0225
## 10 1126 1129 4 0.167 0.251 0.525 0.0487 0.0447 0.0266
## # ℹ 11 more rows
## # ℹ 1 more variable: propPoslogFC <dbl>summary(resposthoc)## Summary of post hoc results.## TPRate p.value p.adj logFC
## Min. :0.00000 Min. :0.0002064 Min. :0.004334 Min. :-0.163388
## 1st Qu.:0.00000 1st Qu.:0.0669302 1st Qu.:0.234256 1st Qu.:-0.034500
## Median :0.00000 Median :0.2751093 Median :0.525209 Median : 0.003099
## Mean :0.04762 Mean :0.3960997 Mean :0.580792 Mean : 0.022271
## 3rd Qu.:0.16667 3rd Qu.:0.6924986 3rd Qu.:0.853823 3rd Qu.: 0.040990
## Max. :0.16667 Max. :0.9537715 Max. :0.953771 Max. : 0.349596
## propPoslogFC
## Min. :0.0000
## 1st Qu.:0.5000
## Median :0.5000
## Mean :0.5238
## 3rd Qu.:0.6667
## Max. :1.0000Plotting the output of postHoc function shows the
minimal proportion of True Discoveries in each cluster.
plot(resposthoc)
sessionInfo()## R version 4.4.3 (2025-02-28)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Paris
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] hicream_0.0.1 reticulate_1.41.0.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2
## [3] farver_2.1.2 dplyr_1.1.4
## [5] viridis_0.6.5 Biostrings_2.74.1
## [7] bitops_1.0-9 RCurl_1.98-1.16
## [9] fastmap_1.2.0 GenomicAlignments_1.42.0
## [11] XML_3.99-0.18 digest_0.6.37
## [13] lifecycle_1.0.4 capushe_1.1.2
## [15] statmod_1.5.0 magrittr_2.0.3
## [17] compiler_4.4.3 rlang_1.1.5
## [19] sass_0.4.9 tools_4.4.3
## [21] yaml_2.3.10 rtracklayer_1.66.0
## [23] knitr_1.50 Rhtslib_3.2.0
## [25] labeling_0.4.3 S4Arrays_1.6.0
## [27] curl_6.2.1 DelayedArray_0.32.0
## [29] plyr_1.8.9 abind_1.4-8
## [31] BiocParallel_1.40.0 withr_3.0.2
## [33] BiocGenerics_0.52.0 grid_4.4.3
## [35] stats4_4.4.3 colorspace_2.1-1
## [37] Rhdf5lib_1.28.0 edgeR_4.4.2
## [39] ggplot2_3.5.1 scales_1.3.0
## [41] MASS_7.3-65 SummarizedExperiment_1.36.0
## [43] cli_3.6.4 rmarkdown_2.29
## [45] crayon_1.5.3 auk_0.8.0
## [47] generics_0.1.3 metapod_1.14.0
## [49] rstudioapi_0.17.1 reshape2_1.4.4
## [51] rjson_0.2.23 httr_1.4.7
## [53] rhdf5_2.50.2 cachem_1.1.0
## [55] stringr_1.5.1 splines_4.4.3
## [57] zlibbioc_1.52.0 parallel_4.4.3
## [59] restfulr_0.0.15 XVector_0.46.0
## [61] matrixStats_1.5.0 vctrs_0.6.5
## [63] Matrix_1.7-3 jsonlite_1.9.1
## [65] IRanges_2.40.1 S4Vectors_0.44.0
## [67] dendextend_1.19.0 adjclust_0.6.10
## [69] locfit_1.5-9.12 limma_3.62.2
## [71] jquerylib_0.1.4 glue_1.8.0
## [73] codetools_0.2-20 stringi_1.8.4
## [75] gtable_0.3.6 GenomeInfoDb_1.42.3
## [77] GenomicRanges_1.58.0 BiocIO_1.16.0
## [79] UCSC.utils_1.2.0 munsell_0.5.1
## [81] tibble_3.2.1 pillar_1.10.1
## [83] rhdf5filters_1.18.0 csaw_1.40.0
## [85] htmltools_0.5.8.1 GenomeInfoDbData_1.2.13
## [87] BSgenome_1.74.0 R6_2.6.1
## [89] sparseMatrixStats_1.18.0 diffHic_1.38.0
## [91] evaluate_1.0.3 lattice_0.22-6
## [93] Biobase_2.66.0 png_0.1-8
## [95] Rsamtools_2.22.0 bslib_0.9.0
## [97] Rcpp_1.0.14 InteractionSet_1.34.0
## [99] gridExtra_2.3 SparseArray_1.6.0
## [101] xfun_0.51 MatrixGenerics_1.18.0
## [103] pkgconfig_2.0.3