preciseTADhub 1.4.0
preciseTADhub is an ExperimentData R package that supplements the preciseTAD software R package. preciseTADhub offers users access to pre-trained random forest classification models used to predict TAD/loop boundary regions. The model building process introduced by preciseTAD (https://doi.org/10.1101/2020.09.03.282186) can be computationally intensive. To avoid this burden, we have provided users with 84 (2 cell lines \(\times\) 2 ground truth boundaries \(\times\) 21 autosomal chromosomes) .RDS files containing pre-trained models that can be leveraged to predict TAD and/or chromatin loop boundaries at base-level resolution using functionality provided by preciseTAD.
Each of the 84 files are stored as lists containing two objects: 1) a train object from with RF model information, and 2) a data.frame of variable importance for each genomic annotation included in the model. The file names are structured as follows:
\(i\)\(j\)\(k\)_\(l\).rds
where \(i\) denotes the chromosome that was used as a holdout {CHR1, CHR2, …, CHR21, CHR22} (i.e. for testing; meaning all other chromosomes were used for training), \(j\) denotes the cell line {GM12878, K562}, \(k\) denotes the resolution (size of genomic bins) {5kb, 10kb}, and \(l\) denotes the TAD/loop caller used to define ground truth {Arrowhead, Peakachu}.
For example the file named “CHR1_GM12878_5kb_Arrowhead.rds” is a list whose first item is a RF model that was built on data for chromosomes 2-22 (omitting CHR9; see https://doi.org/10.1101/2020.09.03.282186), binned using 5 kb bins, ground truth TAD boundaries were identified using the Arrowhead TAD caller at 5 kb on GM12878. All models included the same number of predictors including CTCF, RAD21, SMC3, and ZNF143. The second item in the list is a data.frame with variable importances for CTCF, RAD21, SMC3, and ZNF143.
The pre-trained models set up users to apply them to predict their own boundaries on chromosomes that were heldout, per the framework in the preciseTAD paper (https://doi.org/10.1101/2020.09.03.282186).
The following is an example of how to predict TAD boundaries at base-level resolution for CHR22 on GM12878, using a pre-trained model stored in preciseTADhub.
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install(c("ExperimentHub"), version = "3.12")
library(ExperimentHub)
library(preciseTAD)
library(preciseTADhub)Table 1 shows the file names and the corresponding ExperimentHub (EH) IDs. Since we want to make TAD boundary predictions on CHR22 for GM12878, we opt to read in the “CHR22_GM12878_5kb_Arrowhead.rds” file. This corresponds to the EH3895 EHID.
| FileName | EHID |
|---|---|
| CHR1_GM12878_5kb_Arrowhead.rds | EH3815 |
| CHR1_GM12878_10kb_Peakachu.rds | EH3816 |
| CHR1_K562_5kb_Arrowhead.rds | EH3817 |
| CHR1_K562_10kb_Peakachu.rds | EH3818 |
| CHR2_GM12878_5kb_Arrowhead.rds | EH3819 |
| CHR2_GM12878_10kb_Peakachu.rds | EH3820 |
| CHR2_K562_5kb_Arrowhead.rds | EH3821 |
| CHR2_K562_10kb_Peakachu.rds | EH3822 |
| CHR3_GM12878_5kb_Arrowhead.rds | EH3823 |
| CHR3_GM12878_10kb_Peakachu.rds | EH3824 |
| CHR3_K562_5kb_Arrowhead.rds | EH3825 |
| CHR3_K562_10kb_Peakachu.rds | EH3826 |
| CHR4_GM12878_5kb_Arrowhead.rds | EH3827 |
| CHR4_GM12878_10kb_Peakachu.rds | EH3828 |
| CHR4_K562_5kb_Arrowhead.rds | EH3829 |
| CHR4_K562_10kb_Peakachu.rds | EH3830 |
| CHR5_GM12878_5kb_Arrowhead.rds | EH3831 |
| CHR5_GM12878_10kb_Peakachu.rds | EH3832 |
| CHR5_K562_5kb_Arrowhead.rds | EH3833 |
| CHR5_K562_10kb_Peakachu.rds | EH3834 |
| CHR6_GM12878_5kb_Arrowhead.rds | EH3835 |
| CHR6_GM12878_10kb_Peakachu.rds | EH3836 |
| CHR6_K562_5kb_Arrowhead.rds | EH3837 |
| CHR6_K562_10kb_Peakachu.rds | EH3838 |
| CHR7_GM12878_5kb_Arrowhead.rds | EH3839 |
| CHR7_GM12878_10kb_Peakachu.rds | EH3840 |
| CHR7_K562_5kb_Arrowhead.rds | EH3841 |
| CHR7_K562_10kb_Peakachu.rds | EH3842 |
| CHR8_GM12878_5kb_Arrowhead.rds | EH3843 |
| CHR8_GM12878_10kb_Peakachu.rds | EH3844 |
| CHR8_K562_5kb_Arrowhead.rds | EH3845 |
| CHR8_K562_10kb_Peakachu.rds | EH3846 |
| CHR10_GM12878_5kb_Arrowhead.rds | EH3847 |
| CHR10_GM12878_10kb_Peakachu.rds | EH3848 |
| CHR10_K562_5kb_Arrowhead.rds | EH3849 |
| CHR10_K562_10kb_Peakachu.rds | EH3850 |
| CHR11_GM12878_5kb_Arrowhead.rds | EH3851 |
| CHR11_GM12878_10kb_Peakachu.rds | EH3852 |
| CHR11_K562_5kb_Arrowhead.rds | EH3853 |
| CHR11_K562_10kb_Peakachu.rds | EH3854 |
| CHR12_GM12878_5kb_Arrowhead.rds | EH3855 |
| CHR12_GM12878_10kb_Peakachu.rds | EH3856 |
| CHR12_K562_5kb_Arrowhead.rds | EH3857 |
| CHR12_K562_10kb_Peakachu.rds | EH3858 |
| CHR13_GM12878_5kb_Arrowhead.rds | EH3859 |
| CHR13_GM12878_10kb_Peakachu.rds | EH3860 |
| CHR13_K562_5kb_Arrowhead.rds | EH3861 |
| CHR13_K562_10kb_Peakachu.rds | EH3862 |
| CHR14_GM12878_5kb_Arrowhead.rds | EH3863 |
| CHR14_GM12878_10kb_Peakachu.rds | EH3864 |
| CHR14_K562_5kb_Arrowhead.rds | EH3865 |
| CHR14_K562_10kb_Peakachu.rds | EH3866 |
| CHR15_GM12878_5kb_Arrowhead.rds | EH3867 |
| CHR15_GM12878_10kb_Peakachu.rds | EH3868 |
| CHR15_K562_5kb_Arrowhead.rds | EH3869 |
| CHR15_K562_10kb_Peakachu.rds | EH3870 |
| CHR16_GM12878_5kb_Arrowhead.rds | EH3871 |
| CHR16_GM12878_10kb_Peakachu.rds | EH3872 |
| CHR16_K562_5kb_Arrowhead.rds | EH3873 |
| CHR16_K562_10kb_Peakachu.rds | EH3874 |
| CHR17_GM12878_5kb_Arrowhead.rds | EH3875 |
| CHR17_GM12878_10kb_Peakachu.rds | EH3876 |
| CHR17_K562_5kb_Arrowhead.rds | EH3877 |
| CHR17_K562_10kb_Peakachu.rds | EH3878 |
| CHR18_GM12878_5kb_Arrowhead.rds | EH3879 |
| CHR18_GM12878_10kb_Peakachu.rds | EH3880 |
| CHR18_K562_5kb_Arrowhead.rds | EH3881 |
| CHR18_K562_10kb_Peakachu.rds | EH3882 |
| CHR19_GM12878_5kb_Arrowhead.rds | EH3883 |
| CHR19_GM12878_10kb_Peakachu.rds | EH3884 |
| CHR19_K562_5kb_Arrowhead.rds | EH3885 |
| CHR19_K562_10kb_Peakachu.rds | EH3886 |
| CHR20_GM12878_5kb_Arrowhead.rds | EH3887 |
| CHR20_GM12878_10kb_Peakachu.rds | EH3888 |
| CHR20_K562_5kb_Arrowhead.rds | EH3889 |
| CHR20_K562_10kb_Peakachu.rds | EH3890 |
| CHR21_GM12878_5kb_Arrowhead.rds | EH3891 |
| CHR21_GM12878_10kb_Peakachu.rds | EH3892 |
| CHR21_K562_5kb_Arrowhead.rds | EH3893 |
| CHR21_K562_10kb_Peakachu.rds | EH3894 |
| CHR22_GM12878_5kb_Arrowhead.rds | EH3895 |
| CHR22_GM12878_10kb_Peakachu.rds | EH3896 |
| CHR22_K562_5kb_Arrowhead.rds | EH3897 |
| CHR22_K562_10kb_Peakachu.rds | EH3898 |
Suppose we want to read in the model that was built using CHR1-CHR21, on GM12878, using Arrowhead defined TAD boundaries at 5kb resolution. We can do this with the following wrapper function. Note: you must initialize ExperimentHub first.
#Initialize ExperimentHub
hub <- ExperimentHub()
query(hub, "preciseTADhub")## ExperimentHub with 84 records
## # snapshotDate(): 2022-04-19
## # $dataprovider: preciseTAD
## # $species: Homo sapiens
## # $rdataclass: list
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH3815"]]'
##
## title
## EH3815 | CHR1_GM12878_5kb_Arrowhead.rds
## EH3816 | CHR1_GM12878_10kb_Peakachu.rds
## EH3817 | CHR1_K562_5kb_Arrowhead.rds
## EH3818 | CHR1_K562_10kb_Peakachu.rds
## EH3819 | CHR2_GM12878_5kb_Arrowhead.rds
## ... ...
## EH3894 | CHR21_K562_10kb_Peakachu.rds
## EH3895 | CHR22_GM12878_5kb_Arrowhead.rds
## EH3896 | CHR22_GM12878_10kb_Peakachu.rds
## EH3897 | CHR22_K562_5kb_Arrowhead.rds
## EH3898 | CHR22_K562_10kb_Peakachu.rdsmyfiles <- query(hub, "preciseTADhub")
CHR22_GM12878_5kb_Arrowhead <- readEH(chr = "CHR22", cl = "GM12878", gt = "Arrowhead", source = myfiles)data("tfbsList")
# Restrict the data matrix to include only SMC3, RAD21, CTCF, and ZNF143
tfbsList_filt <- tfbsList[names(tfbsList) %in% c("Gm12878-Ctcf-Broad",
"Gm12878-Rad21-Haib",
"Gm12878-Smc3-Sydh",
"Gm12878-Znf143-Sydh")]
names(tfbsList_filt) <- c("Ctcf", "Rad21", "Smc3", "Znf143")
# Run preciseTAD
set.seed(123)
pt <- preciseTAD(genomicElements.GR = tfbsList_filt,
featureType = "distance",
CHR = "CHR22",
chromCoords = list(18000000, 19000000),
tadModel = CHR22_GM12878_5kb_Arrowhead,
threshold = 1.0,
verbose = FALSE,
parallel = NULL,
DBSCAN_params = list(30000, 3))
# flank = 5000)
# genome = "hg19")
pt## $preciseTADparams
## MinPts eps NEmean k
## 1 3 30000 4.8 5
##
## $PTBR
## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr22 18038169-18038406 *
## [2] chr22 18268086-18268246 *
## [3] chr22 18310018-18312978 *
## [4] chr22 18499231-18507447 *
## [5] chr22 18557665-18559050 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $PTBP
## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr22 18038310 *
## [2] chr22 18268166 *
## [3] chr22 18312917 *
## [4] chr22 18507320 *
## [5] chr22 18558977 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $Summaries
## $Summaries$PTBRWidth
## min max median iqr mean sd
## 1 161 8217 1386 2723 2592.6 3342.241
##
## $Summaries$PTBRCoverage
## min max median iqr mean sd
## 1 0.0230011 0.8385093 0.08730159 0.6643423 0.3392469 0.3986859
##
## $Summaries$DistanceBetweenPTBR
## min max median iqr mean sd
## 1 41772 229680 118235.5 149003.2 126980.8 95242.47
##
## $Summaries$NumSubRegions
## min max median iqr mean sd
## 1 2 16 3 1 5.6 5.85662
##
## $Summaries$SubRegionWidth
## min max median iqr mean sd
## 1 1 162 6 9.5 26.28571 45.35037
##
## $Summaries$DistBetweenSubRegions
## min max median iqr mean sd
## 1 3 2957 58 612.5 532.6087 871.3022
##
## $Summaries$NormilizedEnrichment
## [1] 4.8
##
## $Summaries$BaseProbs
## [1] NA