Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets.
ChromSCape is a user-friendly interactive Shiny/R application that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets (scATAC-seq, scChIP-seq, scCUT&TAG…).
The goal of ChromSCape is to provide user a interactive interface to explore and run a complete analyse (QC, preprocessing, analysis, interpretation) on various single-cell epigenomic data. The application has multiple advantages:
To launch the application simply run:
if (!requireNamespace("BiocManager", quietly = TRUE)){
install.packages("BiocManager")
}
BiocManager::install("ChromSCape")
Load ChromSCape
print("Loading ChromSCape")
## [1] "Loading ChromSCape"
library(ChromSCape)
Launch the shiny application
launchApp()
A browser should open with the application. If the browser doesn’t open automatically, navigate to the displayed url, e.g. “Listening on http://127.0.0.1:5139”. The first time you’ll open the application, you will be guided through a small tour of the application, that you can come back to any time you like by clicking the Help button on the upper right corner.
The user interface is organized by ‘Tab’ corresponding to specific ‘step’ of the
analysis. In order to be able to access to each Tab you need to complete the
previous steps. For example, before accessing the ‘Filter & Normalize’ Tab, you
need to first complete the ‘Select & Import’ Tab.
Each one of your project is named an ‘Analysis’ and is comprised of one raw
dataset and additional objects that you have produced by going further into the
analysis. A folder named ‘ChromSCape_analyses’ which will contain all your
Analysis is produced in the output directory when you create an Analysis for the
first time. In this folder, each of your Analysis is a folder with the following
organisation:
ChromSCape_analyses/
├── Analysis_1
│ ├── annotation.txt
│ ├── Filtering_Normalize_Reduce
│ │ └── Analysis_1_(…).RData
│ ├── correlation_clustering
│ │ └── Analysis_1_(…).RData
│ ├── Diff_Analysis_Gene_Sets
│ │ └── Analysis_1_(…).RData
│ └── scChIP_raw.RData
The raw data is stored at the root of the folder, and at each main step
(‘Filtering & Normalization’, ‘Correlation Clustering’ and ‘Differential
Analysis’) the objects are saved. This enable you to close the application and
later re-load your analysis without the need of re-doing all those steps. This
also enable you to share your analysis with colleagues simply by copying your
Analysis folder.
Note: The (…) in the saved objects contained the values of the parameters
for an Analysis. If you try multiple parameter, each results will be saved this
way and all trials will accessible in the future.
Various existing technologies allow to produce single-cell genome-wide epigenomic datasets :
After sequencing a single-cell epigenomic experiment, the raw sequencing files
(.fastq) are demultiplexed, aligned against a reference genome to output
different kind of data depending on the technology. ChromSCape allows user to
input a variety of different format. Depending on the output of the
data-engineering/pre-processing pipeline used, the signal can be already
summarized into features (Count matrix, Peak-Index-Barcode files) or
stored directly in raw format (single-cell BAM or single-cell BED
files).
Anyhow the format, ChromSCape needs signal to be summarized into features. If inputting raw signal (scBAM or scBED), the application lets user summarize signal of each cells into various features:
Note that summarizing will take longer if using BAM files than BED files, and if the number of features is important (e.g. 5kbp bins, enhancers…).
Each sample should be contained into single-cell count matrix (features x cells)
in tab-separated format (extension .txt or .tsv) or comma-separated format
(.csv). The first column is genomic location in standard format (chr:start-end)
and the next columns are reads counted in each cells in the corresponding
genomic feature. Note that the first entry (row 1, column 1) must be empty. All
files should be placed in the same folder.
If inputing only a single matrix regrouping different samples, the user can
check the ‘multiple sample’ check box and specify a number of samples.
ChromSCape will automatically find the different samples based on the names of
the cells, so please make sure samples names are all quite different (e.g.
K562_.., GM12878_..).
An example of such dataset is availablefor H3K27me3 mouse scChIP-seq of paired
PDX samples at: PDX mouse cells H3K27me3 scChIP-seq matrices.
BC969404 BC893525 BC239068 BC073314 chr10:0-50000 0 0 0 0 chr10:50000-100000 0 0 0 0 chr10:100000-150000 0 0 0 0 …
This format regroups three files containing signal of all samples of one or multiple experiment:
HBCx95_BC969404 HBCx95_BC893525 HBCx22_BC239068 HBCx22_BC073314 …
chr3 197959001 197961500 chr3 198080001 198081500 chr4 53001 55500 …
459 1 1 461 1 1 556 1 2
Each BAM or BED (.bed or .bed.gz) file must be grouping signal of a single-cell, and all files must be placed in the same folder. The signal will be summarized into either bins, peaks, or around gene TSS depending on user choice.
An example of such dataset is publicly available for H3K27me3 single-cell CUT&TAG
(Kaya-Okur et al., 2019) K562 and H1 cells at: K562 H3K27me3 cells
and K562 + H1 H3K27me3 cells.
All files need to be placed in the same folders, the BED files do not need to be
unzipped.
For the optional step of Peak Calling on cluster of cells found de novo, users have to input one BAM file per sample, placed in the same folder. The barcode information of each read should be contained in a specific tag (XB, CB..) and correspond to the column names of the corresponding count matrix.
samtools view example_matrix.bam | head NS500388:436:HNG5VAFXX:2:21311:26430:3816 89 chr10 3102405 255 51M * 0 0 CTTGGTGTCTAGTGGATCTGCTGCAGTCTTCTGTTGTCAGTGCTAAATCAC EEEEE/E6EEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAAAAA NH:i:1 HI:i:1 AS:i:50 nM:i:0 XS:i:2147483647 XD:Z:GATGACAAAG XB:Z:BC969404 …
Once you have launched the application, you arrive on the “Select & Import”. Here you can selet an output directory, where your analyses will be saved. ChromSCape will automatically save the folder’s location so that you don’t have to select it each time you connect.
You must then name your analysis. The name shouldn’t contain special characters (except ’_‘). Choose either the Human (hg38) or Mouse (mm10) genome. This is used to annotate your features with the closest genes TSS for the Gene Set Analysis. Browse your computer for one or multiple matrices that will be analyzed together. To select multiple matrices, they must be placed in the same folder and then the user can select multiples matrices with (Shift + Click) or (Ctrl + Click). Finally, clicking on ’Create Analysis’ will create an analysis & import the count matrices in this analysis. This will create a folder named ‘ChromSCape_analyses’ in the output directory you specified, inside which another folder ‘Your_analysis_name’ is nested. If you create another analysis, it will also be created under ‘ChromSCape_analyses’. If you already created an analysis in a previous section, selecting the same output directory as you chose in the previous session will enable you to load your analysis.