Introduction
The InTAD analysis is focused on the processing of generated object
that combines all input datasets. Required input data is the
following:
- epigenetic signals data.frame e.g. enhancers along with their
coordinates in GRanges format
- gene expression counts data.frame along with gene coordinates in
GRanges format
- TAD borders GRanges or loops data.frame e.g. from results of HiC
technique application
Further explained example of performing the analysis procedure is
based on H3K27ac data reflecting activity of enhancers in
medulloblastoma brain tumour descrbied in the manuscript from C.Y.Lin,
S.Erkek et al., Nature, 2016.
This dataset includes normalized enhancer signals obtained from
H3K27ac ChIP-seq data and RNA-seq gene expression RPKM counts from 25
medulloblastoma samples. The test subset is extracted from a selected
region inside chromosome 15. Additionally, the coordinates for enhancers
and genes along with specific sample annotation are provided.
The analysis starts from preparing and loading the data. Here is the
overview of integrated input test data, that can serve as a useful
example describing supported input formats:
library(InTAD)
# normalized enhancer signals table
enhSel[1:3,1:3]
## MB176 MB95 MB40
## chr15:25661165-25662833 -0.3041844 -0.7661778 -1.9551413
## chr15:25682177-25685608 4.3015286 5.0409281 5.8519724
## chr15:25709081-25711634 0.5399542 -0.1572336 -0.6773354
# enhancer signal genomic coordinates
as.data.frame(enhSelGR[1:3])
## seqnames start end width strand
## 1 chr15 25661165 25662833 1669 *
## 2 chr15 25682177 25685608 3432 *
## 3 chr15 25709081 25711634 2554 *
# gene expression normalized counts
rpkmCountsSel[1:3,1:3]
## MB176 MB95 MB40
## ENSG00000215567.4 0 0.000000 0
## ENSG00000201241.1 0 0.000000 0
## ENSG00000258463.1 0 4.183154 0
# gene coordiantes
as.data.frame(txsSel[1:3])
## seqnames start end width strand gene_id gene_name
## 1 chr15 20083769 20093074 9306 + ENSG00000215567.4 RP11-79C23.1
## 2 chr15 20088867 20088969 103 + ENSG00000201241.1 RNU6-978P
## 3 chr15 20104587 20104812 226 + ENSG00000258463.1 RP11-173D3.3
## gene_type
## 1 pseudogene
## 2 snRNA
## 3 pseudogene
# additional sample info data.frame
head(mbAnnData)
## Subgroup Age Gender Histology M.Stage
## MB176 WNT 9 F Classic M0
## MB95 Group3 3 M Classic M0
## MB40 Group4 3 M Classic M0
## MB37 SHH 1 F Desmoplastic M0
## MB38 Group4 6 M Desmoplastic M0
## MB28 SHH 1 M Desmoplastic M0
Importantly, there are specific requriements for the input datasets.
The names of samples should match in signals and gene expression
datasets.
summary(colnames(rpkmCountsSel) == colnames(enhSel))
## Mode TRUE
## logical 25
Next, the genomic regions should be provided for each signal as well
as for each gene.
# compare number of signal regions and in the input table
length(enhSelGR) == nrow(enhSel)
## [1] TRUE
The genomic regions reflecting the gene coordinates must include
“gene_id” and “gene_name” marks. These are typical GTF
format markers. One more mark “gene_type” is also useful to
perform filtering of gene expression matrix.
All the requirements are checked during the generation of the
InTADSig object. Main part of this object is MultiAssayExperiment
subset that combines signals and gene expression. Specific annotation
information about samples can be also included for further control and
visualization. In provided example for medulloblastoma samples
annotation contains various aspects such as tumour subgroup, age,
gender, etc.
inTadSig <- newSigInTAD(enhSel, enhSelGR, rpkmCountsSel, txsSel,mbAnnData)
## Created signals and genes object for 25 samples
The created object contains MultiAssayExperiment that includes both
signals and gene expression data.
## S4 InTADSig object
## Num samples: 25
## Num signals: 116
## Num genes: 2080
During the main object generation there are also available special
options to activate parallel computing based on usage of R multi-thread
librares and log2 adjustment for gene expression. The generated data
subsets can be accessed using specific call functions on the object
i.e. signals or exprs.
Notably, the main object can be also loaded from the text files
representing the input data using function loadSigInTAD. Refer
to the documetation of this function for more details.
Main data analysis using TADs
The usage of input gene expression counts matrix assumes filtering of
non- or low expressed genes. However if these counts were not filtered
before starting the InTAD analysis it’s possible to adjust gene
expression limits using function filterGeneExpr. This function
provides parameters to control minimum gene expression and type. There
is additionally a special option to compute gene expression distribution
based on usage of mclust package
in order to find suitable minimum gene expression cut limit. Here’s
example how to use this procedure:
# filter gene expression
inTadSig <- filterGeneExpr(inTadSig, checkExprDistr = TRUE)
## Initial result: 2080 genes
## Gene expression cut value: 1.79411704685513
## Filtered result: 671 genes
The analysis starts from the combination of signals and genes inside
the TADs. Since the TADs are known to be stable across various cell
types, it’s possible to use already known TADs obtained from IMR90 cells
using HiC technology (Dixon et al
2012). The human IMR90 TADs regions object is integrated into the
package.
# IMR90 hg19 TADs
head(tadGR)
## GRanges object with 6 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## chr1:770137-1250137 chr1 770137-1250137 *
## chr1:1250137-1850140 chr1 1250137-1850140 *
## chr1:1850140-2330140 chr1 1850140-2330140 *
## chr1:2330140-3650140 chr1 2330140-3650140 *
## chr1:4660140-6077413 chr1 4660140-6077413 *
## chr1:6077413-6277413 chr1 6077413-6277413 *
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengths
However, since the variance is actually observed between TAD calling
methods (i.e. described in detailed review by Rola Dali
and Mathieu Blanchette, NAR 2017 ), novel obtained TADs can be also
applied for the analysis. The requried format: GRanges object.
Composition of genes and signals in TADs is performed using function
combineInTAD that has several options. By default, it marks the
signal belonging to the TAD by largest overlap and also takes into
account genes that are not overlaping the TADs by connecting them to the
closest TAD. This can be sensetive strategy since some genomic regions
can be missed due to the limits of input HiC data and variance of
existing TAD calling methods.
# combine signals and genes in TADs
inTadSig <- combineInTAD(inTadSig, tadGR)
## Combined 768 signal-gene pairs in TADs
Final step is the correlation analysis. Various options are avialble
for this function i.e. correlation method, computation of q-value to
control the evidence strength and visualization of connection
proportions. This last option allows to show differences in gene and
signal regulations.
par(mfrow=c(1,2)) # option to combine plots in the graph
# perform correlation anlaysis
corData <- findCorrelation(inTadSig,plot.proportions = TRUE)
The result data.frame has a special format. It includes each signal,
TAD, gene and correlation information.
## peakid tad gene
## 1 chr15:25748892-25750259 chr15:25728907-27128907 ENSG00000114062.13
## 2 chr15:25748892-25750259 chr15:25728907-27128907 ENSG00000261529.1
## 3 chr15:25748892-25750259 chr15:25728907-27128907 ENSG00000206190.7
## 4 chr15:25748892-25750259 chr15:25728907-27128907 ENSG00000166206.9
## 5 chr15:25748892-25750259 chr15:25728907-27128907 ENSG00000235518.2
## name cor pvalue eucDist corRank
## 1 UBE3A 0.37789578 0.06253400 25.748716 3
## 2 RP13-487P22.1 0.21115682 0.31095743 7.360294 5
## 3 ATP10A -0.03977321 0.85028161 7.703550 6
## 4 GABRB3 0.44145787 0.02716195 21.972593 1
## 5 AC011196.3 0.36894539 0.06953544 7.381633 4
Further filtering of this result data can be performed by adjusting
p-value and correlation effect limits (i.e. p-val < 0.01, positive
correlation only).
Integration of chromatin loops
Another clear approach to find contacts between genes and epigenetic
signals is to use direct chromatin connections, so called loops. The
loops are typically derived from HiC data and there are well-known tools
that allow to perform this (e.g. FitHiC or HiCCUPS).
An example of chromatin loops visualisation from
IGV showing connection of medulloblastoma tumor specific enhancers to
genes. The loops are derived from IMR90 HiC data.
InTAD starting from version 1.9.1 also allows to use HiC loops for
the analysis. Main functions to perform this task are
combineWithLoops and findCorFromLoops.
To demonstrate this approach InTAD includes an example subset of
loops derived from IMR90
cells. This loops data.frame has a specific format where the first 6
columns represent genomic regions for both loop anchors:
(chr1,start1,end1,chr2,start2,end2):
## chr1 x1 x2 chr2 y1 y2
## 1 chr15 100470000 100480000 chr15 100670000 100680000
## 2 chr15 101170000 101180000 chr15 101410000 101420000
## 3 chr15 101170000 101180000 chr15 101800000 101810000
## 4 chr15 101175000 101180000 chr15 101540000 101545000
The loaded loops are applied to find connections between genes and
signals using function combineWithLoops. By default 6-column
loops format is expected, but the function also supports 4-column format
where for the loop anchors only middle positions are provided (as in FitHiC output):
(chr1,middlePos1,chr2,middlePos2) .
Howerer in this case loop fragment length is also required and using
the variable fragmentLength allows to activate this format. In
addition other parameters (e.g. transcription start site width,
extension of the loops) can be controlled to increase the
sensitivity.
In result the function reports how many connections are detected to
be supported by loops and saves them within the returned InTAD
object:
inTadSig <- combineWithLoops(inTadSig, loopsDfSel)
## NOTE: 6-column loops format is assumed.
## Combined 1 signal-gene pairs with loops
In this particular example only 1 connection between gene and
enhancer is found. To find if there are correlations between detected
connected signal-gene pairs the function fincCorFromLoops is
applied. It has a list of options similar to corresponding function
findCorrelation for usage of TADs (e.g. correlation method,
adjusted p-value):
loopEag <- findCorFromLoops(inTadSig,method = "spearman")
Final result also has format similar to representation of
correlations between genes and enhancers within TADs. The only
difference is that the loops supporting found connection are
included:
## peak loopStart loopEnd
## 1 chr15:25748892-25750259 chr15:25750000-25760000 chr15:27110000-27120000
## gene name cor pvalue eucDist
## 1 ENSG00000186297.7 GABRA5 0.6123077 0.001430953 12.80297
In general, the focus on loops allows to increase the specificity of
the detected connections between signals and genes in order to find
possible perspective targets for further investigation. However, ideally
it should be applied on HiC data derived from the same research target
materials (e.g. same tumor type), while TADs from other sources could be
used due to their stability.
Visualization
The package provides post-analysis visualization function: the
specific signal and gene can be selected for correlation plot
generation. Here’s example of verified medulllobastoma Group3-specifc
enhancer assoicated gene GABRA5 lying in the same TAD as the enhancer,
but not close to the gene:
# example enhancer in correlation with GABRA5
cID <- "chr15:26372163-26398073"
selCorData <- corData[corData$peakid == cID, ]
selCorData[ selCorData$name == "GABRA5", ]
## peakid tad gene name
## 430 chr15:26372163-26398073 chr15:25728907-27128907 ENSG00000186297.7 GABRA5
## cor pvalue eucDist corRank
## 430 0.878531 7.724306e-09 10.92154 1
For the plot generation it is required to provide the signal id and
gene name:
plotCorrelation(inTadSig, cID, "GABRA5",
xLabel = "RPKM gene expr log2",
yLabel = "H3K27ac enrichment log2",
colByPhenotype = "Subgroup")
## ENSG00000186297.7
Note that in the visualization it’s also possible to mark the colours
representing the samples using option colByPhenotype based on
the sample annotation information included in the generation of the main
object. In the provided example medulloblastma tumour subgroups are
marked.
Specific genomic region of interest can be also visualised to observe
the variance and impact of TADs using special function that works on
result data.frame obtained from function findCorrelation. The
resulting plot provides the location of signals in X-axis and genes in
Y-axis. Each point reflects the correlation stength based on p-value:
-log10(P-val). This visualization strategy was introduced in
the study by S.
Waszak et al, Cell, 2015 focused on investigation of chromatin
architecture in human cells.
By default only detected TADs with signals inside are visualized, but
it is also possible to include all avaialble TAD regions using special
option. Here’s the example plot covering the whole chromosome 15 region
used in the test dataset:
plotCorAcrossRef(inTadSig,corData,
targetRegion = GRanges("chr15:25000000-28000000"),
tads = tadGR)
One more option of this function allows to activaite representation
of postive correlation values from 0 to 1 instead of strength.
plotCorAcrossRef(inTadSig,corData,
targetRegion = GRanges("chr15:25000000-28000000"),
showCorVals = TRUE, tads = tadGR)
It’s also possible to focus on the connections by ignoring the
signal/gene locations and focusing only on correlation values by
adjusting for symmetery. This is typical approach used for HiC contact
data visualization in such tools as for example JuiceBox. This can be activate
by using the corresponding option:
plotCorAcrossRef(inTadSig,corData,
targetRegion = GRanges("chr15:25000000-28000000"),
showCorVals = TRUE, symmetric = TRUE, tads = tadGR)
These visualization strategies allow to investigate the impact of
TADs.
Additional documentation is available for each function via standard
R help.
Session info
Here is the output of sessionInfo() on the system on
which this document was compiled:
## R version 4.4.1 (2024-06-14)
## 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] InTAD_1.27.0 MultiAssayExperiment_1.31.5
## [3] SummarizedExperiment_1.35.5 Biobase_2.67.0
## [5] MatrixGenerics_1.17.1 matrixStats_1.4.1
## [7] GenomicRanges_1.57.2 GenomeInfoDb_1.41.2
## [9] IRanges_2.39.2 S4Vectors_0.43.2
## [11] BiocGenerics_0.53.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] Biostrings_2.75.0 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.16 GenomicAlignments_1.41.0 XML_3.99-0.17
## [10] digest_0.6.37 lifecycle_1.0.4 magrittr_2.0.3
## [13] compiler_4.4.1 rlang_1.1.4 sass_0.4.9
## [16] tools_4.4.1 utf8_1.2.4 yaml_2.3.10
## [19] rtracklayer_1.65.0 knitr_1.48 ggsignif_0.6.4
## [22] labeling_0.4.3 S4Arrays_1.5.11 mclust_6.1.1
## [25] curl_5.2.3 DelayedArray_0.31.14 plyr_1.8.9
## [28] BiocParallel_1.41.0 abind_1.4-8 withr_3.0.2
## [31] purrr_1.0.2 sys_3.4.3 grid_4.4.1
## [34] fansi_1.0.6 ggpubr_0.6.0 colorspace_2.1-1
## [37] ggplot2_3.5.1 scales_1.3.0 cli_3.6.3
## [40] rmarkdown_2.28 crayon_1.5.3 generics_0.1.3
## [43] rjson_0.2.23 httr_1.4.7 reshape2_1.4.4
## [46] BiocBaseUtils_1.9.0 qvalue_2.37.0 cachem_1.1.0
## [49] stringr_1.5.1 zlibbioc_1.51.2 splines_4.4.1
## [52] parallel_4.4.1 BiocManager_1.30.25 XVector_0.45.0
## [55] restfulr_0.0.15 vctrs_0.6.5 Matrix_1.7-1
## [58] jsonlite_1.8.9 carData_3.0-5 car_3.1-3
## [61] rstatix_0.7.2 Formula_1.2-5 maketools_1.3.1
## [64] jquerylib_0.1.4 tidyr_1.3.1 glue_1.8.0
## [67] codetools_0.2-20 stringi_1.8.4 gtable_0.3.6
## [70] BiocIO_1.17.0 UCSC.utils_1.1.0 munsell_0.5.1
## [73] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
## [76] GenomeInfoDbData_1.2.13 R6_2.5.1 evaluate_1.0.1
## [79] lattice_0.22-6 highr_0.11 Rsamtools_2.21.2
## [82] backports_1.5.0 broom_1.0.7 bslib_0.8.0
## [85] Rcpp_1.0.13 SparseArray_1.5.45 xfun_0.48
## [88] buildtools_1.0.0 pkgconfig_2.0.3
References
Dali, R.
and Blanchette, M., 2017. A critical assessment of topologically
associating domain prediction tools. Nucleic acids research, 45(6),
pp.2994-3005.
Dixon,
J.R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu,
J.S. and Ren, B., 2012. Topological domains in mammalian genomes
identified by analysis of chromatin interactions. Nature, 485(7398),
p.376.
Lin,
C.Y., Erkek, S., Tong, Y., Yin, L., Federation, A.J., Zapatka, M.,
Haldipur, P., Kawauchi, D., Risch, T., Warnatz, H.J. and Worst, B.C.,
2016. Active medulloblastoma enhancers reveal subgroup-specific cellular
origins. Nature, 530(7588), p.57.
Waszak,
S.M., Delaneau, O., Gschwind, A.R., Kilpinen, H., Raghav, S.K.,
Witwicki, R.M., Orioli, A., Wiederkehr, M., Panousis, N.I., Yurovsky, A.
and Romano-Palumbo, L., 2015. Population variation and genetic control
of modular chromatin architecture in humans. Cell, 162(5)