scater包简介
scater是一个优秀的单细胞转录组数据分析工具包，它可以对单细胞数据进行常规的质量控制，数据的标准化与归一化，以及数据的降维与可视化分析。它主要基于SingleCellExperiment类（来自SingleCellExperiment包）来进行操作处理，因此可以与其他许多Bioconductor包（如scran，batchelor和iSEE等）相互操作。
scater包主要含有以下特性：
- Use of the
SingleCellExperimentclass as adata containerfor interoperability with a wide range of other Bioconductor packages; - Functions to import
kallistoandSalmonresults; - Simple calculation of many
quality control metricsfrom the expression data; - Many tools for
visualising scRNA-seq data, especially diagnostic plots for quality control; - Subsetting and many other methods for
filtering out problematic cells and features; - Methods for
identifying important experimental variablesandnormalising dataahead of downstream statistical analysis and modeling.
scater包的工作流程为：
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构建SingleCellExperiment对象
使用SingleCellExperiment函数导入单细胞转录组的基因表达矩阵构建一个SingleCellExperiment对象，该表达矩阵是一个行为基因，列为细胞的大型数据框。
SingleCellExperiment对象内容
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SingleCellExperiment对象常见操作
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# 导入scater包
library(scater)
# 加载示例数据
data("sc_example_counts")
data("sc_example_cell_info")
# 查看基因表达矩阵
head(sc_example_counts)
Cell_001 Cell_002 Cell_003 Cell_004 Cell_005 Cell_006 Cell_007
Gene_0001 0 123 2 0 0 0 0
Gene_0002 575 65 3 1561 2311 160 2
Gene_0003 0 0 0 0 1213 0 0
Gene_0004 0 1 0 0 0 99 476
Gene_0005 0 0 11 0 0 0 0
Gene_0006 0 0 0 0 0 0 673
Cell_008 Cell_009 Cell_010 Cell_011 Cell_012 Cell_013 Cell_014
Gene_0001 21 2 0 2624 1 1015 0
Gene_0002 1 0 0 2 0 2710 0
Gene_0003 1 0 0 2 178 0 0
Gene_0004 0 1 66 0 1 0 1
Gene_0005 0 1 0 0 2 2 0
Gene_0006 0 3094 0 0 270 2 0
Cell_015 Cell_016 Cell_017 Cell_018 Cell_019 Cell_020 Cell_021
Gene_0001 0 1 34 1 0 6 0
Gene_0002 4 0 908 673 174 622 2085
Gene_0003 0 0 0 0 1 0 3320
Gene_0004 0 906 655 1020 1 0 0
Gene_0005 0 0 0 2 0 0 3
Gene_0006 1176 0 3 0 0 0 1
# 查看样本信息
head(sc_example_cell_info)
Cell Mutation_Status Cell_Cycle Treatment
Cell_001 Cell_001 positive S treat1
Cell_002 Cell_002 positive G0 treat1
Cell_003 Cell_003 negative G1 treat1
Cell_004 Cell_004 negative S treat1
Cell_005 Cell_005 negative G1 treat2
Cell_006 Cell_006 negative G0 treat1
# 使用SingleCellExperiment函数构建SingleCellExperiment对象
example_sce <- SingleCellExperiment(
assays = list(counts = sc_example_counts),
colData = sc_example_cell_info
)
# 查看SingleCellExperiment对象
example_sce
class: SingleCellExperiment
dim: 2000 40
metadata(0):
assays(1): counts
rownames(2000): Gene_0001 Gene_0002 ... Gene_1999 Gene_2000
rowData names(0):
colnames(40): Cell_001 Cell_002 ... Cell_039 Cell_040
colData names(4): Cell Mutation_Status Cell_Cycle Treatment
reducedDimNames(0):
spikeNames(0):
View(example_sce)
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我们通常使用原始的count矩阵存储到SingleCellExperiment对象的“counts” Assay中，同时也可以使用counts函数提取SingleCellExperiment对象中的count表达矩阵。
str(counts(example_sce))
int [1:2000, 1:40] 0 575 0 0 0 0 0 0 416 12 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:2000] "Gene_0001" "Gene_0002" "Gene_0003" "Gene_0004" ...
..$ : chr [1:40] "Cell_001" "Cell_002" "Cell_003" "Cell_004" ...
head(counts(example_sce))
对于样本行和列的meta信息，我们也提供了一些常用函数来进行操作处理，如isSpike, sizeFactors, 和reducedDim等函数。
# 添加一个新列的meta信息whee
example_sce$whee <- sample(LETTERS, ncol(example_sce), replace=TRUE)
# 使用colData函数查看列的meta信息
colData(example_sce)
DataFrame with 40 rows and 5 columns
Cell Mutation_Status Cell_Cycle Treatment whee
<character> <character> <character> <character> <character>
Cell_001 Cell_001 positive S treat1 N
Cell_002 Cell_002 positive G0 treat1 T
Cell_003 Cell_003 negative G1 treat1 Y
Cell_004 Cell_004 negative S treat1 T
Cell_005 Cell_005 negative G1 treat2 C
... ... ... ... ... ...
Cell_036 Cell_036 negative G0 treat1 Q
Cell_037 Cell_037 negative G0 treat1 X
Cell_038 Cell_038 negative G0 treat2 W
Cell_039 Cell_039 negative G1 treat1 B
Cell_040 Cell_040 negative G0 treat2 Z
# 添加一个新行的meta信息
rowData(example_sce)$stuff <- runif(nrow(example_sce))
# 使用rowData函数查看行的meta信息
rowData(example_sce)
DataFrame with 2000 rows and 1 column
stuff
<numeric>
Gene_0001 0.146899100858718
Gene_0002 0.547358682611957
Gene_0003 0.381470382912084
Gene_0004 0.0698823253624141
Gene_0005 0.577666614903137
... ...
Gene_1996 0.810028552776203
Gene_1997 0.92471176572144
Gene_1998 0.73105761455372
Gene_1999 0.496801204746589
Gene_2000 0.135669085429981
# 根据基因的表达过滤掉那些在所有细胞中表达量之和为0的基因
keep_feature <- rowSums(counts(example_sce) > 0) > 0
example_sce <- example_sce[keep_feature,]
对于原始的count表达矩阵，我们也提供了一些函数对其进行数据的归一化和标准化处理。如使用calculateCPM函数计算表达量的CPM(counts-per-million)值，其结果将会存储在SingleCellExperiment对象的“cpm” Assay中，可以通过cpm函数进行访问
cpm(example_sce) <- calculateCPM(example_sce)
head(cpm(example_sce))
Cell_001 Cell_002 Cell_003 Cell_004 Cell_005 Cell_006
Gene_0001 0.00 749.529259 6.561271 0.000 0.000 0.0000
Gene_0002 1344.85 396.092698 9.841906 5558.424 2826.476 923.5422
Gene_0003 0.00 0.000000 0.000000 0.000 1483.563 0.0000
Gene_0004 0.00 6.093734 0.000000 0.000 0.000 571.4418
Gene_0005 0.00 0.000000 36.086989 0.000 0.000 0.0000
Gene_0006 0.00 0.000000 0.000000 0.000 0.000 0.0000
Cell_007 Cell_008 Cell_009 Cell_010 Cell_011 Cell_012
Gene_0001 0.000000 69.780424 2.698593 0.0000 9959.085768 5.882457
Gene_0002 6.938109 3.322877 0.000000 0.0000 7.590767 0.000000
Gene_0003 0.000000 3.322877 0.000000 0.0000 7.590767 1047.077301
Gene_0004 1651.269847 0.000000 1.349296 168.5733 0.000000 5.882457
Gene_0005 0.000000 0.000000 1.349296 0.0000 0.000000 11.764913
Gene_0006 2334.673545 0.000000 4174.723091 0.0000 0.000000 1588.263322
Cell_013 Cell_014 Cell_015 Cell_016 Cell_017 Cell_018
Gene_0001 2013.948828 0.000000 0.00000 2.64154 118.89733 2.069181
Gene_0002 5377.144161 0.000000 11.56233 0.00000 3175.25816 1392.558811
Gene_0003 0.000000 0.000000 0.00000 0.00000 0.00000 0.000000
Gene_0004 0.000000 3.334801 0.00000 2393.23554 2290.52213 2110.564617
Gene_0005 3.968372 0.000000 0.00000 0.00000 0.00000 4.138362
Gene_0006 3.968372 0.000000 3399.32534 0.00000 10.49094 0.000000
同样的，我们也可以使用normalize函数进行数据的归一化处理，它将对原始的count矩阵进行一个log2的转换处理。This is done by dividing each count by its size factor (or scaled library size, if no size factors are defined), adding a pseudo-count and log-transforming. 归一化后的结果存储在"logcounts" Assay中，可以通过logcounts函数进行访问。
# 使用normalize函数进行数据归一化
example_sce <- normalize(example_sce)
# 查看assay的信息
assayNames(example_sce)
[1] "counts" "cpm" "logcounts"
head(logcounts(example_sce))
我们可以使用calcAverage函数计算基因的平均表达量
head(calcAverage(example_sce))
Gene_0001 Gene_0002 Gene_0003 Gene_0004 Gene_0005 Gene_0006
305.551749 325.719897 183.090462 162.143201 1.231123 187.167913
使用其他的方法导入基因表达矩阵
- 对于CSV格式存储的基因表达矩阵，我们可以通过
read.table()函数或data.table包中的fread()函数进行读取。 - 对于一些大型的数据集，在读取的过程中会产生大量的缓存，需要较大的内存，因此我们可以通过Matrix包中的
readSparseCounts()函数读取大型数据集，并将其存储为一个稀疏矩阵，可以有效减小系统的读取内存。 - 对于来自10x Genomics产生的表达矩阵，我们可以通过DropletUtils包中的
read10xCounts()函数进行读取，读取后它会自动生成一个SingleCellExperiment对象 - 对于kallisto和Salmon等比对软件产生的基因表达矩阵，我们可以通过tximeta包中的
readSalmonResults()和readKallistoResults()函数进行读取。
参考来源：
http://www.bioconductor.org/packages/release/bioc/vignettes/scater/inst/doc/overview.html
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