一、介紹

• 用于單細胞RNA-seq數(shù)據(jù)
• 提供嚴格的質(zhì)量控制：將原始測序讀數(shù)處理為可用于下游分析的高質(zhì)量表達數(shù)據(jù)集
• 提供了豐富的繪圖工具套件
• R包地址：http://bioconductor.org/packages/scater

二钻心、工作流

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三苍柏、常用函數(shù)

plotColData

# 導入包
suppressMessages(library(scater))
suppressMessages(library(scRNAseq))

# 載入示例數(shù)據(jù)
data("sc_example_counts")
data("sc_example_cell_info")

# 構建 SingleCellExperiment 對象
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)

# 計算 SingleCellExperiment 對象中每個特征和細胞的質(zhì)控標準
example_sce <- calculateQCMetrics(example_sce)

# 計算 SingleCellExperiment 對象中read計數(shù)矩陣的歸一化表達值
example_sce <- normalize(example_sce)

plotColData(example_sce, y = "total_features_by_counts",
            x = "log10_total_counts", colour_by = "Mutation_Status")

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plotColData(example_sce, y = "total_features_by_counts",
            x = "log10_total_counts", colour_by = "Mutation_Status",
            size_by = "Gene_0001", shape_by = "Treatment")

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plotColData(example_sce, y = "Treatment",
            x = "log10_total_counts", colour_by = "Mutation_Status")

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plotColData(example_sce, y = "total_features_by_counts",
            x = "Cell_Cycle", colour_by = "Mutation_Status")

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plotExplanatoryVariables

解釋變量（ExplanatoryVariables）：https://www.statisticshowto.datasciencecentral.com/explanatory-variable/

解釋變量是一種自變量扛邑。這兩個術語通常可互換使用溅潜。但是潦牛，是兩者之間的細微差別凹嘲。當一個變量是獨立的宛畦，它不影響在所有的任何其他變量。當變量不是獨立的時候，它是一個解釋變量。 它在臨床研究中非常重要熬的。對于大多數(shù)情況，特別是在統(tǒng)計數(shù)據(jù)中崭闲，這兩個術語基本相同。

假設您有兩個變量來解釋體重增加：快餐和蘇打水。雖然你可能認為吃快餐和喝蘇打水是相互獨立的罗岖，但它們并不是真的炕淮。那是因為快餐店鼓勵你在用餐時買蘇打水。如果你停在某個地方買蘇打水晶疼，那里經(jīng)常會有很多快餐選擇酒贬，比如熱狗。雖然這些變量并非完全相互獨立翠霍，但它們確實會對體重增加產(chǎn)生影響锭吨。它們被稱為解釋變量，因為它們可能為體重增加提供一些解釋寒匙。

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
plotExplanatoryVariables(example_sce)

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plotExpression

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- calculateQCMetrics(example_sce)
sizeFactors(example_sce) <- colSums(counts(example_sce))
example_sce <- normalize(example_sce)

# 前十五個基因的表達值
plotExpression(example_sce, 1:15)

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plotExpression(example_sce, c("Gene_0001", "Gene_0004"), x="Mutation_Status")

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plotExpression(example_sce, c("Gene_0001", "Gene_0004"), x="Gene_0002")

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plotExpression(example_sce, 1:6, colour_by = "Mutation_Status")

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plotExpression(example_sce, 1:6, colour_by = "Mutation_Status",
               shape_by = "Treatment", size_by = "Gene_0010")

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plotExpression(example_sce, 1:4, "Gene_0004", show_smooth = TRUE)

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plotExprsFreqVsMean

表達頻率（即表達細胞的百分比）Vs SingleCellExperiment對象中每個特征的平均表達水平

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
example_sce <- calculateQCMetrics(example_sce,
                                  feature_controls = list(set1 = 1:500))
plotExprsFreqVsMean(example_sce)

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plotExprsFreqVsMean(example_sce, size_by = "is_feature_control")

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plotExprsVsTxLength

Plot mean expression values for all features in a SingleCellExperiment object against transcript length values.

data("sc_example_counts")
data("sc_example_cell_info")
rd <- DataFrame(gene_id = rownames(sc_example_counts),
                feature_id = paste("feature", rep(1:500, each = 4), sep = "_"),
                median_tx_length = rnorm(2000, mean = 5000, sd = 500),
                other = sample(LETTERS, 2000, replace = TRUE)
)
rownames(rd) <- rownames(sc_example_counts)
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info, rowData = rd
)
example_sce <- normalize(example_sce)
plotExprsVsTxLength(example_sce, "median_tx_length")

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plotExprsVsTxLength(example_sce, "median_tx_length", show_smooth = TRUE)

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plotExprsVsTxLength(example_sce, "median_tx_length", show_smooth = TRUE,
                    colour_by = "other", show_exprs_sd = TRUE)

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## using matrix of tx length values in assays(object)
mat <- matrix(rnorm(ncol(example_sce) * nrow(example_sce), mean = 5000,
                    sd = 500), nrow = nrow(example_sce))
dimnames(mat) <- dimnames(example_sce)
assay(example_sce, "tx_len") <- mat
plotExprsVsTxLength(example_sce, "tx_len", show_smooth = TRUE,
                    length_is_assay = TRUE, show_exprs_sd = TRUE)

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## using a vector of tx length values
plotExprsVsTxLength(example_sce,
                    data.frame(rnorm(2000, mean = 5000, sd = 500)))

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plotHeatmap

Create a heatmap of expression values for each cell and specified features in a SingleCellExperiment
object.

example(normalizeSCE) # borrowing the example objects in here.
plotHeatmap(example_sce, features=rownames(example_sce)[1:10])

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plotHeatmap(example_sce, features=rownames(example_sce)[1:10],
            center=TRUE, symmetric=TRUE)

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plotHeatmap(example_sce, features=rownames(example_sce)[1:10],
            colour_columns_by=c("Mutation_Status", "Cell_Cycle"))

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plotHighestExprs

Plot the features with the highest average expression across all cells, along with their expression in
each individual cell.

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- calculateQCMetrics(example_sce,
                                  feature_controls = list(set1 = 1:500)
)
plotHighestExprs(example_sce, colour_cells_by ="total_features_by_counts")

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plotHighestExprs(example_sce, controls = NULL)

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plotHighestExprs(example_sce, colour_cells_by="Mutation_Status")

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plotPlatePosition

Plots cells in their position on a plate, coloured by metadata variables or feature expression values
from a SingleCellExperiment object.

## prepare data
data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
example_sce <- calculateQCMetrics(example_sce)
## define plate positions
example_sce$plate_position <- paste0(
  rep(LETTERS[1:5], each = 8),
  rep(formatC(1:8, width = 2, flag = "0"), 5)
)
## plot plate positions
plotPlatePosition(example_sce, colour_by = "Mutation_Status")

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plotPlatePosition(example_sce, shape_by = "Treatment", colour_by = "Gene_0004")

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plotPlatePosition(example_sce, shape_by = "Treatment", size_by = "Gene_0001",
                  colour_by = "Cell_Cycle")

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plotQC

Produce QC diagnostic plots

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info)
example_sce <- normalize(example_sce)
example_sce <- calculateQCMetrics(example_sce)
plotQC(example_sce, type="high", colour_cells_by="Mutation_Status")

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plotReducedDim

Plot cell-level reduced dimension results stored in a SingleCellExperiment object.

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
example_sce <- runPCA(example_sce, ncomponents=5)
plotReducedDim(example_sce, "PCA")

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plotReducedDim(example_sce, "PCA", colour_by="Cell_Cycle")

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plotReducedDim(example_sce, "PCA", colour_by="Gene_0001")

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plotReducedDim(example_sce, "PCA", ncomponents=5)

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plotReducedDim(example_sce, "PCA", ncomponents=5, colour_by="Cell_Cycle",
               shape_by="Treatment")

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plotRLE

Produce a relative log expression (RLE) plot of one or more transformations of cell expression values.

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
plotRLE(example_sce, colour_by = "Mutation_Status", style = "minimal")

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plotRLE(example_sce, colour_by = "Mutation_Status", style = "full",
        outlier.alpha = 0.1, outlier.shape = 3, outlier.size = 0)

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plotRowData

Plot row-level (i.e., gene) metadata from a SingleCellExperiment object.

data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
example_sce <- calculateQCMetrics(example_sce,
                                  feature_controls = list(ERCC=1:40))
example_sce <- normalize(example_sce)
plotRowData(example_sce, y="n_cells_by_counts", x="log10_total_counts")

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plotRowData(example_sce, y="n_cells_by_counts",
            size_by ="log10_total_counts",
            colour_by = "is_feature_control")

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plotScater

Plot the relative proportion of the library size that is accounted for by the most highly expressed features for each cell in a SingleCellExperiment object.

## Set up an example SingleCellExperiment
data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
  assays = list(counts = sc_example_counts),
  colData = sc_example_cell_info
)
plotScater(example_sce)

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plotScater(example_sce, exprs_values = "counts", colour_by = "Cell_Cycle")

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plotScater(example_sce, block1 = "Treatment", colour_by = "Cell_Cycle")

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cpm(example_sce) <- calculateCPM(example_sce, use_size_factors = FALSE)
plotScater(example_sce, exprs_values = "cpm", block1 = "Treatment",
           block2 = "Mutation_Status", colour_by = "Cell_Cycle")

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Reduced dimension plots

PCA

## Set up an example SingleCellExperiment
data("sc_example_counts")
data("sc_example_cell_info")
example_sce <- SingleCellExperiment(
assays = list(counts = sc_example_counts),
colData = sc_example_cell_info
)
example_sce <- normalize(example_sce)
## Examples plotting PC1 and PC2
plotPCA(example_sce)

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plotPCA(example_sce, colour_by = "Cell_Cycle")

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plotPCA(example_sce, colour_by = "Cell_Cycle", shape_by = "Treatment")

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plotPCA(example_sce, colour_by = "Cell_Cycle", shape_by = "Treatment",
size_by = "Mutation_Status")

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## Force legend to appear for shape:
example_subset <- example_sce[, example_sce$Treatment == "treat1"]
plotPCA(example_subset, colour_by = "Cell_Cycle", shape_by = "Treatment",
by_show_single = TRUE)

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## Examples plotting more than 2 PCs
plotPCA(example_sce, ncomponents = 4, colour_by = "Treatment",
shape_by = "Mutation_Status")

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## Same for TSNE:
plotTSNE(example_sce, run_args=list(perplexity = 10))

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## Same for DiffusionMaps:
plotDiffusionMap(example_sce)

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## Same for MDS plots:
plotMDS(example_sce)

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