Nguyen, M., Wall, B. P. G., Harrell, J. C., & Dozmorov, M. G. (2024). scHiCcompare: an R package for differential analysis of single-cell Hi-C data. bioRxiv. doi: https://doi.org/10.1101/2024.11.06.622369
scHiCcompare is designed for the imputation, joint normalization, and detection of differential chromatin interactions between two groups of chromosome-specific single-cell Hi-C datasets (scHi-C). The groups can be pre-defined based on biological conditions or created by clustering cells according to their chromatin interaction patterns. Clustering can be performed using methods like Higashi, scHiCcluster methods, etc.
scHiCcompare works with processed Hi-C data, specifically chromosome-specific chromatin interaction matrices, and accepts five-column tab-separated text files in a sparse matrix format.
The package provides two key functionalities:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("scHiCcompare")
# For the latest version install from GitHub
# devtools::install_github("dozmorovlab/scHiCcompare")
library(scHiCcompare) library(tidyr) library(ggplot2) library(gridExtra) library(lattice) library(data.table)
To use scHiCcompare, you’ll need to define two groups of cells to compare and save cell-specific scHi-C data (individual files in .txt format) in two folders.
Each cell-specific scHi-C .txt file should be formatted as modified sparse upper triangular matrices in R, which consist of five columns (chr1, start1, chr2, start2, IF). Since the full matrix of chromatin interactions is symmetric, only the upper triangular portion, including the diagonal and excluding any 0, is stored in a sparse matrix format. The required sparse matrix format of each single-cell Hi-C is:
The ‘.txt’ files need to be saved in tab-separated columns and no row names, column names, or quotes around character strings with the example format below.
#> chr1 start1 chr2 start2 IF
#> 17669 chr20 0 chr20 0 128
#> 17670 chr20 0 chr20 1000000 1
#> 17671 chr20 1000000 chr20 1000000 179
#> 17672 chr20 0 chr20 2000000 1
#> 17673 chr20 1000000 chr20 2000000 1
#> 17674 chr20 2000000 chr20 2000000 174
To run scHiCcompare(), you need two folders with condition-specific scHiC ‘.txt’ files. The condition-specific groups of cells should be pre-defined based on criteria such as experimental conditions, clustering results, or biological characteristics.
Here is an example workflow using scHiC human brain datasets (Lee et al., 2019) with ODC and MG cell types at chromosome 20 with a 1MB resolution.
For the following example sections, we will load samples of 10 single-cell Hi-C data (in ‘.txt’) for each cell type group in two example folders (ODCs_example and MGs_axample). The files follow the same format as those downloaded via download_schic() of Bandnorm. You can extract the folder path by the code below, which could be used as input for scHiCcompare() function.
## Load folder of ODC file path
ODCs_example_path <- system.file("extdata/ODCs_example",
package = "scHiCcompare"
)
## Load folder of MG file path
MGs_example_path <- system.file("extdata/MGs_example",
package = "scHiCcompare"
)
Since the data downloaded by Bandnorm has the required input format (5 columns of [chr1, start1, chr2, start2, IF]), we don’t need an extra step for data modification. If, after importing your data into R, its format does not follow the sparse upper triangular input format requirement, you need to modify the data.
The function requires two Input Parameter:
file.path.1, file.path.2 - Character strings specifying paths to folders containing scHi-C data for the first and second cell type or condition groups.select.chromosome - Integer or character indicating the chromosome to be analyzed (e.g., ‘chr1’ or ‘chr10’.)scHiCcompare(file.path.1, file.path.2, select.chromosome, main.Distances = 1:10000000, imputation = "RF", normalization = "LOESS", differential.detect = "MD.cluster", pool.style = "progressive", n.imputation = 5, maxit = 1, outlier.rm = TRUE, missPerc.threshold = 95, A.min = NULL, fprControl.logfc = 0.8, alpha = 0.05, Plot = T, Plot.normalize = F, save.output.path = NULL )
Optional Workflow Parameter include:
main.Distances - A numeric vector indicating the range of interacting genomic distances (in base pairs) between two regions (e.g., loci or bins) to focus on (e.g., 1:100000, Inf). All genomic range selections can be specified using Inf. The main.Distances vector should be proportional to the data’s resolution (e.g., for 10kb resolution: 1:10000, 1:50000, 1:100000, Inf). As the distance range and resolution increase, the percentage of ‘0’ or missing values also increases. Selecting a large distance range at high resolution (e.g., below 200kb) may increase runtime due to extreme sparsity. By default, main.Distances = 1:10000000.
imputation - A character string, either 'RF' or NULL, indicating the imputation method. If NULL is selected, the workflow will skip the imputation step. The default is 'RF' for Random Forest imputation.
normalization - A character string, either 'LOESS' or NULL, indicating the normalization method. If NULL is selected, the workflow will skip the normalization step. The default is 'LOESS'.
Optional Imputation Parameter include:
pool.style - A character string specifying the pooling style for imputation. Options are 'none', 'progressive', or 'Fibonacci'. The default is 'progressive'.
n.imputation - An integer specifying the number of multiple imputations for the imputation step. Because the final imputed values are calculated as the average of multiple imputations, increasing the number of imputations improves the accuracy of imputed values; however, it may also extend the imputation runtime. The default is 5.
maxit - An integer specifying the maximum number of iterations for the internal refinement process within a single imputation cycle. Increasing maxit can help stabilize imputed values, although it may increase the imputation runtime. The default is 1.
outlier.rm - Logical. If TRUE, outliers are removed during imputation. The default is TRUE.
missPerc.threshold - A numeric value specifying the maximum allowable percentage of missing data in pool bands outside the main.Distances to be imputed by the imputation method. A higher threshold includes more extreme sparse distances for imputation (e.g., above 95 percent), which increases memory and runtime, while a lower threshold (e.g., below 50 percent) might reduce the number of distances imputed. The default is 95.
Optional Normalization Parameter include:
A.min - Numeric value or NULL that sets the A-value quantile cutoff (eg,. 7, 10, etc) for filtering low average interaction frequencies in the outlier detection in the differential step of the hic_compare() function from HiCcompare. If not provided (NULL), A is auto-detected.Optional Differential Test Parameter include:
fprControl.logfc - Numeric value to control the false positive rate for GMM difference clusters (differential.detect) (e.g., 0.5, 0.8, 1, 1.5, etc.). Increasing fprControl.logfc may lower the false positive rate but may also reduce the number of detected chromatin interaction differences. The default is 0.8, equivalent to a 2-fold change.
alpha - Numeric value specifying the significance level for outlier detection during the differential.detect step with the hic_compare() function from HiCcompare. Default is 0.05.
Optional Output Parameter :
save.output.path - Character string specifying the directory to save outputs, including the imputed cells in the form of a sparse upper triangular format, normalization result table, and differential analysis result table. If save.output.path = NULL (the default), no files are saved.
Plot - A logical value indicating whether to plot the differential.detect results in an MD plot. Default is TRUE.
Plot.normalize - A logical value indicating whether to plot the output of MD plot showing before/after LOESS normalization. Default is FALSE.
In the following example, we will work with scHi-C data from 10 single cells in both ODC and MG cell types at a 1 MG resolution. We will focus on chromosome 20, applying the full workflow of scHiCcompare, which includes imputation, pseudo-bulk normalization, and differential analysis. Our goal is to detect differences for loci with genomic distances ranging from 1 to 10,000,000 bp. The progressive pooling style will be selected to create pool bands for the random forest imputation. For the differential analysis step, we will set the log fold change - false positive control threshold to 0.8.
The input file path was included in the package and conducted in the Prepare input folders section.
## Imputation with 'progressive' pooling result <- scHiCcompare( file.path.1 = ODCs_example_path, file.path.2 = MGs_example_path, select.chromosome = "chr20", main.Distances = 1:10000000, imputation = "RF", normalization = "LOESS", differential.detect = "MD.cluster", pool.style = "progressive", fprControl.logfc = 0.8, Plot = TRUE, Plot.normalize = TRUE )
From the visualizations above, normalization effectively reduces the irregular trend in the M values between the imputed pseudo-bulk matrices of the two cell types. At a 1MB resolution, the differential analysis reveals that most of the detected differences occur at closer genomic distances, particularly below 5MB.
Output objects from the R functionThe scHiCcompare() function will return an object that contains plots, differential results, pseudo-bulk matrices, normalized results, and imputation tables. The full differential results are available in $Differential_Analysis. Intermediate results can be accessed with $Intermediate, including the imputation result table ($Intermediate$Imputation), the pseudo-bulk matrix in sparse format ($Intermediate$PseudoBulk), and the normalization table ($Intermediate$Bulk.Normalization). These output table objects have the following structure:
$Intermediate$PseudoBulk for each condition group ($condition1 and $condition2) has a standard sparse upper triangular format with 3 columns of [region1, region2, IF].
$Intermediate$Imputation for each condition group ($condition1 and $condition2) has modified sparse upper triangular format:
$Intermediate$Bulk.Normalization has 15 columns
$Differential_Analysis has same structure as $Intermediate$Bulk.Normalization with addition of 2 differential detection results columns
You also can have the option to save the results into the chosen directory by a parameter in scHiCcompare() function. This will save the normalization result table, differential result table, and imputed cell scHi-C data (each group is a sub-folder). The sample of the saved output folder structure is:
|– Bulk_normalization_table.txt
|– Differential_analysis_table.txt
|– Imputed_{group 1’s name}/
|– Imputed_{group 2’s name}/
The normalization result Bulk_normalization_table.txt has the same format as the output object from the scHiCcompare() function, $Intermediate$Bulk.Normalization, which is shown in the structure example below.
The differential result table Differential_analysis_table.txt also has the same format as the output object $Differential_Analysis from the function.
The imputed cell’s scHiC data is saved in a folder for each group, which has a modified sparse upper triangular format of five columns [chr1, start1, chr2, start2, IF].
Below is a continuous example from Example of real anlysis above, showing how you can extract different result options from the scHiCcompare() function.
### Extract imputed differential result diff_result <- result$Differential_Analysis head(diff_result) #> chr1 start1 end1 chr2 start2 end2 bulk.IF1 bulk.IF2 D #> <char> <num> <num> <char> <num> <num> <num> <num> <num> #> 1: chr20 0 1000000 chr20 1000000 2000000 28 34 1 #> 2: chr20 1000000 2000000 chr20 2000000 3000000 29 48 1 #> 3: chr20 2000000 3000000 chr20 3000000 4000000 32 19 1 #> 4: chr20 3000000 4000000 chr20 4000000 5000000 26 26 1 #> 5: chr20 4000000 5000000 chr20 5000000 6000000 39 37 1 #> 6: chr20 5000000 6000000 chr20 6000000 7000000 38 26 1 #> M adj.bulk.IF1 bulk.adj.IF2 adj.M mc A #> <num> <num> <num> <num> <num> <num> #> 1: 0.28010792 26.23038 36.29379 0.4684842 -0.1883762 31.26209 #> 2: 0.72698151 27.16718 51.23830 0.9153577 -0.1883762 39.20274 #> 3: -0.75207249 29.97758 20.28183 -0.5636962 -0.1883762 25.12970 #> 4: 0.00000000 24.35678 27.75408 0.1883762 -0.1883762 26.05543 #> 5: -0.07594885 36.53517 39.49619 0.1124274 -0.1883762 38.01568 #> 6: -0.54748780 35.59837 27.75408 -0.3591116 -0.1883762 31.67623 #> Z Difference.cluster #> <num> <num> #> 1: 2.1142655 0 #> 2: 4.1228321 0 #> 3: -2.5250849 0 #> 4: 0.8552619 1 #> 5: 0.5138940 1 #> 6: -1.6055363 1
### Extract imputed pseudo bulk matrices normalization norm_result <- result$Intermediate$Bulk.Normalization head(norm_result) #> chr1 start1 end1 chr2 start2 end2 bulk.IF1 bulk.IF2 D #> <char> <num> <num> <char> <num> <num> <num> <num> <num> #> 1: chr20 1000000 2000000 chr20 1000000 2000000 1823 2111 0 #> 2: chr20 2000000 3000000 chr20 2000000 3000000 1931 2187 0 #> 3: chr20 3000000 4000000 chr20 3000000 4000000 1750 2114 0 #> 4: chr20 4000000 5000000 chr20 4000000 5000000 1953 2091 0 #> 5: chr20 5000000 6000000 chr20 5000000 6000000 1799 2010 0 #> 6: chr20 6000000 7000000 chr20 6000000 7000000 1808 2056 0 #> M adj.bulk.IF1 bulk.adj.IF2 adj.M mc A #> <num> <num> <num> <num> <num> <num> #> 1: 0.21161202 1932.385 1991.505 0.043476476 0.1681355 1961.945 #> 2: 0.17960506 2046.865 2063.203 0.011469514 0.1681355 2055.034 #> 3: 0.27262045 1855.005 1994.335 0.104484913 0.1681355 1924.670 #> 4: 0.09850111 2070.185 1972.637 -0.069634429 0.1681355 2021.411 #> 5: 0.16000031 1906.945 1896.222 -0.008135227 0.1681355 1901.583 #> 6: 0.18544559 1916.485 1939.618 0.017310045 0.1681355 1928.051
### Extract imputed ODC cell type table imp_ODC_table <- result$Intermediate$Imputation$condition1 head(imp_ODC_table) #> region1 region2 cell chr imp.IF_ODC.bandnorm_chr20_1 #> 1 1000000 1000000 condition1 chr20 179 #> 2 2000000 2000000 condition1 chr20 174 #> 3 3000000 3000000 condition1 chr20 194 #> 4 4000000 4000000 condition1 chr20 201 #> 5 5000000 5000000 condition1 chr20 171 #> 6 6000000 6000000 condition1 chr20 142 #> imp.IF_ODC.bandnorm_chr20_2 imp.IF_ODC.bandnorm_chr20_3 #> 1 195 192 #> 2 204 226 #> 3 207 198 #> 4 220 228 #> 5 193 208 #> 6 181 200 #> imp.IF_ODC.bandnorm_chr20_4 imp.IF_ODC.bandnorm_chr20_5 #> 1 134 164 #> 2 153 186 #> 3 136 165 #> 4 147 194 #> 5 173 156 #> 6 153 188 #> imp.IF_ODC.bandnorm_chr20_6 imp.IF_ODC.bandnorm_chr20_7 #> 1 52 204 #> 2 67 215 #> 3 50 194 #> 4 54 220 #> 5 61 210 #> 6 56 219 #> imp.IF_ODC.bandnorm_chr20_8 imp.IF_ODC.bandnorm_chr20_9 #> 1 259 249 #> 2 231 247 #> 3 212 206 #> 4 272 212 #> 5 191 244 #> 6 237 224 #> imp.IF_ODC.bandnorm_chr20_10 #> 1 195 #> 2 228 #> 3 188 #> 4 205 #> 5 192 #> 6 208
## Extract Pseudo-bulk matrix from imputed scHi-C data ## Pseudo bulk matrix in standard sparse format psudobulk_result <- result$Intermediate$PseudoBulk$condition1 head(psudobulk_result) #> region1 region2 IF #> 1 1000000 1000000 1823 #> 2 2000000 2000000 1931 #> 3 3000000 3000000 1750 #> 4 4000000 4000000 1953 #> 5 5000000 5000000 1799 #> 6 6000000 6000000 1808
Furthermore, you also have some parameter options in the function to indicate which plots to output and an option to save the results in a given directory.
There are several other functions included in scHiCcompare package.
plot_HiCmatrix_heatmap() produces a heatmap visualization for HiC and scHiC matrices. It requires, as input, a modified sparse matrix, the same format from scHiCcompare() Input with five columns of chr1, start1, chr2 start2, IF. More information can be found in its help document and the example below.
data("ODC.bandnorm_chr20_1")
plot_HiCmatrix_heatmap(scHiC.sparse = ODC.bandnorm_chr20_1, main = "scHiC matrix of a ODC cell", zlim = c(0, 5))
#> Matrix dimensions: 63x63
Imputation Diagnostic plot
plot_imputed_distance_diagnostic() generates a diagnostic visualization of imputation across genomic distances for all single cells. It compares the distribution of all cells’ interaction frequency at a given distance data before and after imputation. It requires, as input, the scHiC table format of the original and imputed scHiC datasets. ScHiC table format includes columns of genomic loci coordinates and interaction frequencies (IF) of each cell (cell, chromosome, start1, end1, IF1, IF2, IF3, etc).
The output of $Intermediate$Imputation of scHiCcompare() function is directly compatible with this format. For more details, see the sections on Output)
# Extract imputed table result imp_MG_table <- result$Intermediate$Imputation$condition2 imp_ODC_table <- result$Intermediate$Imputation$condition1
#> region1 region2 cell chr imp.IF_ODC.bandnorm_chr20_1
#> 1 1000000 1000000 condition1 chr20 179
#> 2 2000000 2000000 condition1 chr20 174
#> 3 3000000 3000000 condition1 chr20 194
#> 4 4000000 4000000 condition1 chr20 201
#> 5 5000000 5000000 condition1 chr20 171
#> 6 6000000 6000000 condition1 chr20 142
#> imp.IF_ODC.bandnorm_chr20_2 imp.IF_ODC.bandnorm_chr20_3
#> 1 195 192
#> 2 204 226
#> 3 207 198
#> 4 220 228
#> 5 193 208
#> 6 181 200
#> imp.IF_ODC.bandnorm_chr20_4 imp.IF_ODC.bandnorm_chr20_5
#> 1 134 164
#> 2 153 186
#> 3 136 165
#> 4 147 194
#> 5 173 156
#> 6 153 188
#> imp.IF_ODC.bandnorm_chr20_6 imp.IF_ODC.bandnorm_chr20_7
#> 1 52 204
#> 2 67 215
#> 3 50 194
#> 4 54 220
#> 5 61 210
#> 6 56 219
#> imp.IF_ODC.bandnorm_chr20_8 imp.IF_ODC.bandnorm_chr20_9
#> 1 259 249
#> 2 231 247
#> 3 212 206
#> 4 272 212
#> 5 191 244
#> 6 237 224
#> imp.IF_ODC.bandnorm_chr20_10
#> 1 195
#> 2 228
#> 3 188
#> 4 205
#> 5 192
#> 6 208
We need to create the table input for original IFs values in the same format. Below is a continuous example from Example of real anlysis above, showing how you can construct scHiC table for original IF values and compare them with the output of imputed IF values.
# Create scHiC table object for original ODC interaction frequencies (IF)
scHiC.table_ODC <- imp_ODC_table[c("region1", "region2", "cell", "chr")]
# List all files in the specified directory for original ODC data
file.names <- list.files(path = ODCs_example_path, full.names = TRUE, recursive = TRUE)
# Loop through each file to read and merge data
for (i in 1:length(file.names)) {
# Read the current file into a data frame
data <- read.delim(file.names[[i]])
names(data) <- c("chr", "region1", "chr2", "region2", paste0("IF_", i))
data <- data[, names(data) %in%
c("chr", "region1", "region2", paste0("IF_", i))]
# Merge the newly read data with the existing scHiC.table_ODC
scHiC.table_ODC <- merge(scHiC.table_ODC, data,
by = c("region1", "region2", "chr"), all = TRUE
)
}
# Create scHiC table object for original MG interaction frequencies (IF)
scHiC.table_MG <- imp_MG_table[c("region1", "region2", "cell", "chr")]
# List all files in the specified directory for original MG data
file.names <- list.files(path = MGs_example_path, full.names = TRUE, recursive = TRUE)
# Loop through each file to read and merge data
for (i in 1:length(file.names)) {
# Read the current file into a data frame
data <- read.delim(file.names[[i]])
names(data) <- c("chr", "region1", "chr2", "region2", paste0("IF_", i))
data <- data[, names(data) %in%
c("chr", "region1", "region2", paste0("IF_", i))]
# Merge the newly read data with the existing scHiC.table_MG
scHiC.table_MG <- merge(scHiC.table_MG, data,
by = c("region1", "region2", "chr"), all = TRUE
)
}
# plot imputed Distance Diagnostic of MG plot1 <- plot_imputed_distance_diagnostic( raw_sc_data = scHiC.table_MG, imp_sc_data = imp_MG_table, D = 1 ) plot2 <- plot_imputed_distance_diagnostic( raw_sc_data = scHiC.table_MG, imp_sc_data = imp_MG_table, D = 2 ) plot3 <- plot_imputed_distance_diagnostic( raw_sc_data = scHiC.table_MG, imp_sc_data = imp_MG_table, D = 3 ) plot4 <- plot_imputed_distance_diagnostic( raw_sc_data = scHiC.table_MG, imp_sc_data = imp_MG_table, D = 4 ) grid.arrange(plot1, plot2, plot3, plot4, ncol = 2, nrow = 2)
The diagnostic visualizations demonstrate that with a sample of only 10 single cells per group (note: this small sample size is for demonstration purposes only), the imputed values for MG closely match the original distribution only at shorter genomic distances (e.g., D1, D2). Increasing the number of single cells per group enhances imputation accuracy across distances. We recommend using a minimum of 80 single cells per group for optimal imputation performance.
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