In biobenkj/compartmentalizer: Higher-order chromatin domain inference in single samples from methylation arrays and single cells from scRNA-seq and scATAC-seq

knitr::opts_chunk$set(collapse = TRUE, warning = TRUE)

Quick start with example data

Input

The expected input for compartmap is a RangedSummarizedExperiment object. These can be built using the built-in function importBigWig() if starting from BigWigs (recommended for scRNA-seq) or from a feature level object like a SingleCellExperiment with the rowRanges slot populated with the GRanges for each feature (see below in the examples).

As an example for the quick start, we will load an existing example scRNA-seq data from K562. These data are derived from Johnson and Rhodes et. al 2021 STORM-seq They have already been TF-IDF transformed with transformTFIDF(). See further down if starting from bigWigs or a feature based object. See the full workflow below for more details.

set.seed(42)
library(compartmap)
data("k562_scrna_chr14", package = "compartmap")

Inferring higher-order chromatin domains at the group and single-cell level

Group level inference

Process chr14 of the example K562 scRNA-seq data and infer higher-order chromatin at 1Mb resolution:

k562_compartments <- scCompartments(
  k562_scrna_chr14,
  chr = "chr14",
  res = 1e6,
  group = TRUE,
  bootstrap = FALSE,
  genome = "hg19",
  assay = "rna"
)

Single-cell level inference

To infer higher-order domains in single cells and quantifying sign coherence with the bootstrapping procedure, you can run:

# Sub-sample to 10 cells as an example
k562_scrna_chr14.sub <- k562_scrna_chr14[,
    sample(colnames(k562_scrna_chr14), size = 10, replace = FALSE)
]

k562_compartments.boot <- scCompartments(
  k562_scrna_chr14.sub,
  chr = "chr14",
  res = 1e6,
  group = FALSE,
  bootstrap = TRUE,
  num.bootstraps = 10,
  genome = "hg19",
  assay = "rna"
)

# Flip the domain sign if the sign coherence is discordant in 80% of the bootstraps
k562_compartments.boot.fix <- fixCompartments(
  k562_compartments.boot,
  min.conf = 0.8
)

# Look at the first cell in the GRangesList object
k562_compartments.boot.fix[[1]]

Visualization of inferred chromatin domains

Once the data have been processed at either the group or single-cell level, one can visualize the results using the plotAB function in compartmap. Notably, we can include the confidence intervals and median, chromosome-wide confidence estimate derived from the bootstrap procedure for sign coherence. At 50%, this suggests that estimates are evenly split between open and closed states. This may be due to data sparsity or heterogeneity in the data. One possible approach to resolve this is to increase the number of bootstraps performed if initially set low (e.g. 10). Alternatively, it may be a region that is worth investigating for your data set.

Plot the "fixed" results in cell 1 from above with plotAB with the confidence intervals and median confidence estimate:

plotAB(
  k562_compartments.boot.fix[[1]],
  chr = "chr14",
  what = "flip.score",
  with.ci = TRUE,
  median.conf = TRUE
)

It is known that sometimes, the domains may be inverted relative to orthogonal data This is also true in Hi-C and scHi-C domain inference One can invert all domains by setting reverse = TRUE in the plotAB() call

plotAB(
  k562_compartments.boot.fix[[1]],
  chr = "chr14",
  what = "flip.score",
  with.ci = TRUE,
  median.conf = TRUE,
  reverse = TRUE
)

Extraction of domain inflections

It is often of interest to extract the chromatin domain inflection points as they transition from "open" to "closed" states to look for nearby CTCF sites, etc. We can accomplish this task using the getDomainInflections function in compartmap.

Domain inflections can be used to look for nearby CTCF sites, etc. Here we extract single-cell domain inflections:

k562_cell_1_inflections <- getDomainInflections(
  k562_compartments.boot.fix[[1]],
  what = "flip.score",
  res = 1e6,
  chrs = "chr14",
  genome = "hg19"
)

# Show the inflection points
k562_cell_1_inflections

Importing bigWigs as input to compartmap

The currently recommended input files to compartmap for scRNA-seq are single-cell bigWigs, though does work with a feature/counts based object as demonstrated in the next section. Single-cell bigWigs can be generated through several tools, such as deeptools. To import bigWigs, we can use the importBigWig() function in compartmap. This will read in a bigWig file and optionally summarize to an arbitrary bin size. The bin size used in the compartmap manuscript was 1kb and is what we do here as well.

We can import a list of bigWig files and merge them into a RangedSummarizedExperiment object:

# list the example bigWigs
bigwigs <- list.files(
    system.file("extdata", package = "compartmap"), full.names = TRUE
)

# generate the 1kb bins
data("hg19.gr", package = "compartmap")
kb_bins <- tileGenome(
  seqlengths = seqlengths(hg19.gr)["chr14"],
  tilewidth = 1000,
  cut.last.tile.in.chrom = TRUE
)

# import
bigwigs_lst <- lapply(bigwigs, function(x) {
  importBigWig(x,
    bins = kb_bins,
    summarize = TRUE, genome = "hg19"
  )
})

# combine
bigwig_rse <- do.call(cbind, bigwigs_lst)
colnames(bigwig_rse) <- c("cell_1", "cell_2")

Starting with a feature or counts-based object

In the cases where we do not have or can't start with bigWigs (e.g. scATAC), we can start with a feature-level or counts object (e.g. SingleCellExperiment). The two things that must be there are making sure rowRanges and colnames are set for each feature and cell/sample. We will use the scATAC-seq from K562 as an example of how the object should look, but will also show one way to add rowRanges to a SingleCellExperiment, which works the same way for a SummarizedExperiment object.

Loading the scATAC-seq example data pre-processed using the csaw package:

data("k562_scatac_chr14", package = "compartmap")

# show the overall object structure
# NOTE that the colnames are also there and the assay name is 'counts'
k562_scatac_chr14

Showing the rowRanges slot is a GRanges for each feature

rowRanges(k562_scatac_chr14)

But if we don't have rowRanges for something like a SingleCellExperiment we are working with, we need to generate them. Thus, we will show an example of how to add rowRanges from a GTF file to a SingleCellExperiment.

First we define a helper function for adding rowRanges (modified from https://github.com/trichelab/velocessor/blob/master/R/import_plate_txis.R). NOTE: you can modify the rtracklayer::import to not subset to gene level if using feature level information (e.g. a bed file of fragments for scATAC)

getRowRanges <- function(gtf, asys) {
  gxs <- subset(rtracklayer::import(gtf), type == "gene")
  names(gxs) <- gxs$gene_id
  granges(gxs)[rownames(asys)]
}

Import the example HEK293T SingleCellExperiment from the SMART-seq3 paper:

data("ss3_umi_sce", package = "compartmap")

# import the example GTF with the helper function
gtf_rowranges <- getRowRanges(
  system.file("extdata/grch38_91_hsapiens.gtf.gz",
    package = "compartmap"
  ),
  ss3_umi_sce
)

# add rowRanges to the SingleCellExperiment
rowRanges(ss3_umi_sce) <- gtf_rowranges

We can now proceed to the next steps and run the compartmap workflow.

Running the compartmap workflow

Once we have data in a RangedSummarizedExperiment or other type of SummarizedExperiment with the rowRanges slot filled, we can proceed through the compartmap workflow. We will use the same K562 scRNA-seq data shown in the manuscript on chromosome 14 here as the example.

# Load example K562 data imported using importBigWig
data("k562_scrna_raw", package = "compartmap")

# TF-IDF transform the signals
k562_scrna_chr14_tfidf <- transformTFIDF(assay(k562_scrna_se_chr14))

# Add back the TF-IDF counts to the object in the counts slot
assay(k562_scrna_se_chr14, "counts") <- t(k562_scrna_chr14_tfidf)

# Compute chromatin domains at the group level
k562_scrna_chr14_raw_domains <- scCompartments(k562_scrna_se_chr14,
  chr = "chr14",
  res = 1e6,
  group = TRUE,
  bootstrap = TRUE,
  num.bootstraps = 10,
  genome = "hg19",
  assay = "rna"
)

For single-cells, run:

k562_scrna_chr14_raw_domains <- scCompartments(
  k562_scrna_se_chr14,
  chr = "chr14",
  res = 1e6,
  group = FALSE,
  bootstrap = TRUE,
  num.bootstraps = 10,
  genome = "hg19",
  assay = "rna"
)

'Fix' compartments with discordant sign coherence:

k562_scrna_chr14_raw_domains.fix <- fixCompartments(k562_scrna_chr14_raw_domains)

# Plot results
plotAB(k562_scrna_chr14_raw_domains.fix,
  chr = "chr14",
  what = "flip.score",
  with.ci = TRUE,
  median.conf = TRUE
)

Higher-order chromatin interaction maps

Another interesting aspect we can derive from scRNA and scATAC is the higher-order interacting domains through denoising of the correlation matrices using a Random Matrix Theory approach. This is often represented with the "plaid-like" patterning shown in Hi-C and scHi-C approaches where stronger correlations (e.g. greater intensity of red) indicates interacting domains relative to lesser correlation. We can do something similar here through iterative denoising using Random Matrix Theory approach:

k562_scrna_chr14_rmt <- getDenoisedCorMatrix(
  k562_scrna_chr14,
  res = 1e6,
  chr = "chr14",
  genome = "hg19",
  assay = "rna",
  iter = 2
)

# Plot the interaction map
plotCorMatrix(k562_scrna_chr14_rmt, uppertri = TRUE)