knitr::opts_chunk$set(warnings = FALSE, message = FALSE)

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 \code{caret} 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).

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.

Installation

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

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

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.

| 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 | Table: File names and corresponding ExperimentHub (EH) IDs for all 84 .RDS files stored in preciseTADhub.

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")
myfiles <- query(hub, "preciseTADhub")

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

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


dozmorovlab/preciseTADhub documentation built on Aug. 14, 2021, 8:19 p.m.