Vignette 2 -- PIC counting in Seurat/Signac workflow • PICsnATAC

content

This Vignette contains an example to use PIC counting in Seurat workflow.

required libraries

Please make sure the following libraries are installed and loaded for the analysis.

library("data.table")
library("GenomicRanges")
library("Matrix")
library("PICsnATAC")
library("Signac")
library("Seurat")

Input files

Input files for PIC counting includes:

cell barcodes with metadata (singlecell.csv)
list of peak regions (peaks.bed)
fragment files (fragment.tsv.gz)
fragment file index (fragment.tsv.gz.tbi)

Below are the step-by-step instructions for obtaining these input from 10X Cell Ranger output files. Example datasets can be downloaded from 10X Genomics website here.

Please also refer to Signac Vignettes for additional information of Signac workflow

Cell Barcodes

If we want to keep the cells from Cell Ranger filtering scheme:

meta.data <- read.csv("atac_pbmc_5k_nextgem_singlecell.csv", header = TRUE)
# -- in your own Cell Ranger output, the file name will be 'singlecell.csv'
meta.data_filtered <- meta.data[meta.data$is__cell_barcode == 1, ]
cells <- meta.data_filtered$barcode

Peak set

If we want to keep the set of peaks from Cell Ranger:

peaks <- data.table::fread("atac_pbmc_5k_nextgem_peaks.bed", header = FALSE)
# -- in your own Cell Ranger output, the file name will be 'peaks.bed'
colnames(peaks) <- c("seqname", "start", "end")
peak_sets <- GenomicRanges::makeGRangesFromDataFrame(peaks)

Note, the codes will be the same for MACS2 called peaks, just change the file name

Run PIC and save output

fragment_tsv_gz_file_location <- "atac_pbmc_5k_nextgem_fragments.tsv.gz"
pic_mat <- PIC_counting(
  cells = cells,
  fragment_tsv_gz_file_location = fragment_tsv_gz_file_location,
  peak_sets = peak_sets
)
saveRDS(pic_mat, "PIC_mat_1.rds")
## if you have multiple matrices, don't forget to save under different names

Note if you have multiple matrices, please make sure you save them under different file names

Create Seurat Object with PIC output

counts <- readRDS("PIC_mat_1.rds")

chrom_assay <- CreateChromatinAssay(
  counts = counts,
  sep = c(":", "-"),
  genome = "hg19",
  fragments = fragment_tsv_gz_file_location,
  min.cells = 10,
  min.features = 200
)

pbmc <- CreateSeuratObject(
  counts = chrom_assay,
  assay = "peaks",
  meta.data = metadata
)

Reference

If you used PIC-snATAC counting in your analysis, please cite our manuscript:

Miao Z and Kim J. Uniform quantification of single-nucleus ATAC-seq data with Paired- Insertion Counting (PIC) and a model-based insertion rate estimator. Nature Methods 2023 (In press)

Signac manuscript:

Stuart, Tim, et al. “Single-cell chromatin state analysis with Signac.” Nature methods 18.11 (2021): 1333-1341.