inst/www/helpfiles/datamatrix_input.md

In vallotlab/ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

Input Data

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: features x cells tab-separated matrix. Features as rownames and cells as column names
• Sparse Matrix (10X format) - 3 files :
  • '*barcodes.tsv' : 1-column file of cell-barcode names (or .gz)
  • '*features.tsv': Tab-separated file of feature genomic location (or .gz)
  • '*matrix.mtx': 3 column space-separated file containing row index, column index and value of non-zeroes entries in the sparse matrix

Or the signal can be stored directly in raw format (single-cell BAM,single-cell BED/BED.gz) containing genomic location of deduplicated reads for one cell.

Anyhow the format, ChromSCape needs signal to be summarized into features. If inputing raw signal (scBAM or scBED), the application lets user summarize signal of each cells into various features:

• Genomic features (extended region around TSS of genes, enhancers)
• Peaks called on bulk or single-cell signal (BED file must be provided by the user)
• Genomic bins (windows of constant length, e.g. 100kbp, 50kbp, 5kbp...)

Important note: For Sparse Matrix (10X format), scBAM and scBED format, the user must have one folder per sample, and place all the folders in the same directory as follows :

Example of folder structure : scChIPseq * Sample_1 - cell_1.bed - cell_2.bed - cell_3.bed - cell_4.bed * Sample_2 - cell_1.bed - cell_2.bed - cell_3.bed - cell_4.bed

In this case, select the scChIPseq directory and ChromSCape will recognize Sample_1 and Sample_2 folders.

vallotlab/ChromSCape documentation built on Oct. 15, 2023, 1:47 p.m.