Signac R｜如何合并多个 Seurat 对象 (1)

引言

在本文中演示了如何合并包含单细胞染色质数据的多个 Seurat 对象。为了进行演示，将使用 10x Genomics 提供的四个 scATAC-seq PBMC 数据集：

实战

在整合多个单细胞染色质数据集的过程中，应当意识到，如果对每个数据集单独进行了峰值检测，那么检测到的峰值可能并不完全一致。为此，需要构建一个适用于所有合并数据集的共通峰值集合。

可以通过GenomicRanges包提供的功能来实现这一点。该包中的reduce函数能够将所有相互重叠的峰值进行合并。此外，disjoin函数也是一个不错的选择，它能够生成互不重叠的独立峰值集合。以下是一个图解示例，用以展示reduce和disjoin两种方法的差异：

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gr <- GRanges(seqnames = "chr1", ranges = IRanges(start = c(20, 70, 300), end = c(120, 200, 400)))
gr

## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     chr1    20-120      *
##   [2]     chr1    70-200      *
##   [3]     chr1   300-400      *
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

构建统一的峰值集合

如果在各个实验中分别鉴定了峰值，那么这些峰值可能并不完全一致。可以通过整合所有实验数据中的峰值，来形成一个统一的峰值集合，并在合并这些数据集之前，对每个实验中的峰值集合进行量化分析。

首先，需要导入每个实验的峰值位置信息，并将其转换成基因组范围内的格式。接着，利用 GenomicRanges 包中的 reduce 函数，来创建一个适用于所有数据集的峰值集合，以便在每个实验中进行量化分析。

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library(Signac)
library(Seurat)
library(GenomicRanges)
library(future)

plan("multicore", workers = 4)
options(future.globals.maxSize = 50000 * 1024^2) # for 50 Gb RAM

# read in peak sets
peaks.500 <- read.table(
  file = "pbmc500/atac_pbmc_500_nextgem_peaks.bed",
  col.names = c("chr", "start", "end")
)
peaks.1k <- read.table(
  file = "pbmc1k/atac_pbmc_1k_nextgem_peaks.bed",
  col.names = c("chr", "start", "end")
)
peaks.5k <- read.table(
  file = "pbmc5k/atac_pbmc_5k_nextgem_peaks.bed",
  col.names = c("chr", "start", "end")
)
peaks.10k <- read.table(
  file = "pbmc10k/atac_pbmc_10k_nextgem_peaks.bed",
  col.names = c("chr", "start", "end")
)

# convert to genomic ranges
gr.500 <- makeGRangesFromDataFrame(peaks.500)
gr.1k <- makeGRangesFromDataFrame(peaks.1k)
gr.5k <- makeGRangesFromDataFrame(peaks.5k)
gr.10k <- makeGRangesFromDataFrame(peaks.10k)

# Create a unified set of peaks to quantify in each dataset
combined.peaks <- reduce(x = c(gr.500, gr.1k, gr.5k, gr.10k))

# Filter out bad peaks based on length
peakwidths <- width(combined.peaks)
combined.peaks <- combined.peaks[peakwidths  < 10000 & peakwidths > 20]
combined.peaks

## GRanges object with 89951 ranges and 0 metadata columns:
##           seqnames            ranges strand
##              <Rle>         <IRanges>  <Rle>
##       [1]     chr1     565153-565499      *
##       [2]     chr1     569185-569620      *
##       [3]     chr1     713551-714783      *
##       [4]     chr1     752418-753020      *
##       [5]     chr1     762249-763345      *
##       ...      ...               ...    ...
##   [89947]     chrY 23422151-23422632      *
##   [89948]     chrY 23583994-23584463      *
##   [89949]     chrY 23602466-23602779      *
##   [89950]     chrY 28816593-28817710      *
##   [89951]     chrY 58855911-58856251      *
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

构建片段对象

为了对合并的峰值集合进行量化分析，需要针对每个实验创建一个片段对象。片段对象是一个在 Signac 中特别定义的类，它负责存储与单个片段文件相关的所有数据。

首先需要导入每个实验的细胞元数据，这样就能了解每个文件包含哪些细胞的条形码信息。之后，利用 CreateFragmentObject 函数来生成片段对象。这个函数会进行一系列验证，确保文件不仅存在于硬盘上，而且已经过压缩和索引处理，同时计算文件及其 tabix 索引的 MD5 校验值，以便于能够检测到文件在任何时间点的变更情况，并确认文件中确实包含了预期的细胞类型。

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# load metadata
md.500 <- read.table(
  file = "pbmc500/atac_pbmc_500_nextgem_singlecell.csv",
  stringsAsFactors = FALSE,
  sep = ",",
  header = TRUE,
  row.names = 1
)[-1, ] # remove the first row

md.1k <- read.table(
  file = "pbmc1k/atac_pbmc_1k_nextgem_singlecell.csv",
  stringsAsFactors = FALSE,
  sep = ",",
  header = TRUE,
  row.names = 1
)[-1, ]

md.5k <- read.table(
  file = "pbmc5k/atac_pbmc_5k_nextgem_singlecell.csv",
  stringsAsFactors = FALSE,
  sep = ",",
  header = TRUE,
  row.names = 1
)[-1, ]

md.10k <- read.table(
  file = "pbmc10k/atac_pbmc_10k_nextgem_singlecell.csv",
  stringsAsFactors = FALSE,
  sep = ",",
  header = TRUE,
  row.names = 1
)[-1, ]

# perform an initial filtering of low count cells
md.500 <- md.500[md.500$passed_filters > 500, ]
md.1k <- md.1k[md.1k$passed_filters > 500, ]
md.5k <- md.5k[md.5k$passed_filters > 500, ]
md.10k <- md.10k[md.10k$passed_filters > 1000, ] # sequenced deeper so set higher cutoff

# create fragment objects
frags.500 <- CreateFragmentObject(
  path = "pbmc500/atac_pbmc_500_nextgem_fragments.tsv.gz",
  cells = rownames(md.500)
)


frags.1k <- CreateFragmentObject(
  path = "pbmc1k/atac_pbmc_1k_nextgem_fragments.tsv.gz",
  cells = rownames(md.1k)
)

frags.5k <- CreateFragmentObject(
  path = "pbmc5k/atac_pbmc_5k_nextgem_fragments.tsv.gz",
  cells = rownames(md.5k)
)

frags.10k <- CreateFragmentObject(
  path = "pbmc10k/atac_pbmc_10k_nextgem_fragments.tsv.gz",
  cells = rownames(md.10k)
)

在各数据集中对峰值进行量化

利用 FeatureMatrix 函数，现在能够为每个样本生成一个以峰值和细胞为维度的矩阵。此函数通过 future 包支持并行计算。

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pbmc500.counts <- FeatureMatrix(
  fragments = frags.500,
  features = combined.peaks,
  cells = rownames(md.500)
)

pbmc1k.counts <- FeatureMatrix(
  fragments = frags.1k,
  features = combined.peaks,
  cells = rownames(md.1k)
)

pbmc5k.counts <- FeatureMatrix(
  fragments = frags.5k,
  features = combined.peaks,
  cells = rownames(md.5k)
)

pbmc10k.counts <- FeatureMatrix(
  fragments = frags.10k,
  features = combined.peaks,
  cells = rownames(md.10k)
)

总结

本文^[1]提供了一个详细的流程来合并单细胞染色质数据集，包括数据下载、预处理、合并以及后续的分析和可视化步骤。强调了在合并过程中创建共有峰值集合的重要性，并提供了在没有片段文件时的替代方法。
Reference [1]

Source: https://stuartlab.org/signac/articles/merging

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