Contents

1 Introduction

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).

2 Getting Started

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.

2.1 Installation

#if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install(c("ExperimentHub"), version = "3.12")

library(ExperimentHub)
library(preciseTAD)
library(preciseTADhub)

2.2 Reading in a file stored on ExperimentHub

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.

File names and corresponding ExperimentHub (EH) IDs for all 84 .RDS files stored in preciseTADhub.
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(): 2021-10-18
## # $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.rds
myfiles <- query(hub, "preciseTADhub")

CHR22_GM12878_5kb_Arrowhead <- readEH(chr = "CHR22", cl = "GM12878", gt = "Arrowhead", source = myfiles)

3 Use the pre-trained model to predict TAD boundaries on the holdout chromosome (CHR22) for GM12878 between coordinates 18000000-19000000 at base-level resolution

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