dittoSeq 是一款对单细胞和批量 RNA 测序数据进行分析和可视化的工具，提供了多种可视化效果，并且允许自定义。
对于单细胞数据，dittoSeq 直接处理在其他软件包（Seurat、scater、scran 等）中预处理的数据。对于批量 RNAseq 数据，dittoSeq 的导入函数会将各种不同结构的批量 RNAseq 数据转换为 dittoSeq 帮助程序和可视化函数可以使用的集合结构
1. 
数据
dittoSeq 本身可与 Seurat 和 SingleCellExperiment 对象配合使用，只需加载数据即可
# 安装并加载所需的R包
# BiocManager::install("dittoSeq")
library(dittoSeq)
library(dplyr)
load("sce.Rdata") # 加载自己的单细胞数据
dittoPlot(sce,
"MS4A1", # 感兴趣的基因
group.by = "orig.ident", # 讲将细胞/样本分组的选项
plots = c("vlnplot", "boxplot"), # 设置绘图类型，包括：jitter, boxplot, vlnplot, ridgeplot。注意顺序
boxplot.fill = F, # 是否显示异常值
boxplot.color = 'white', # 箱线图线条颜色
theme = theme(axis.text = element_text(size = 12, color = 'black'),
axis.line = element_line(size = 1),
axis.title.y = element_text(size = 15, color = 'black'),
plot.title = element_text(size=15, hjust=0.5, color = 'black')),
ylab = 'Expression',
y.breaks = seq(0, 5, 1),
max = 5, min = 0,
xlab = '',
x.labels.rotate =F,
main = "MS4A1",
legend.show = F)
dittoSeq-1
1.2 细胞比例图
# seurat等单细胞可视化工具在查看细胞比例柱状图时，需先进行细胞比例计算，这里可以直接作图。数据可以表示为百分比或计数，由"scale"输入控制的
p1 <- dittoBarPlot(sce, "Major.CellType", group.by = "orig.ident")
p2 <- dittoBarPlot(sce, "Major.CellType", group.by = "orig.ident", scale = "count")
p1|p2
dittoSeq-2
# dittoFreqPlot可按每种细胞类型分面，sample.by可将组内的细胞分组，group.by成单独的样本
dittoFreqPlot(sce, "Major.CellType", sample.by = "orig.ident", group.by = "class")
dittoSeq-3
2. 
数据
# 加载数据（制作模拟的表达数据和条件数据）
exp <- matrix(rpois(20000, 5), ncol=20)
colnames(exp) <- paste0("donor", seq_len(ncol(exp)))
rownames(exp) <- paste0("gene", seq_len(nrow(exp)))
logexp <- logexp <- log2(exp + 1)
logexp[1:3,1:6]
## donor1 donor2 donor3 donor4 donor5 donor6
## gene1 2.584963 1.584963 3.000000 2.000000 2.584963 2.584963
## gene2 1.000000 2.807355 2.584963 2.584963 2.807355 1.584963
## gene3 2.000000 3.000000 2.000000 2.807355 1.584963 3.000000
pca <- matrix(rnorm(20000), nrow=20)
conditions <- factor(rep(1:4, 5))
sex <- c(rep("M", 9), rep("F", 11))
# dittoSeq 本身可处理存储为 SummarizedExperiment 对象的批量 RNAseq 数据
library(SummarizedExperiment)
bulkSE <- SummarizedExperiment(
assays = list(counts = exp,
logcounts = logexp), # 每个相当于一个表达矩阵，可存储counts矩阵、TPM矩阵和FPKM矩阵等
colData = data.frame(conditions = conditions,
sex = sex) # 可添加行信息（基因信息，比如gene id, gene name, gene type等）和列信息（样本信息，比如生存时间、生存状态等）
)
# 或者，也可以使用 importDittoBulk() 函数将以其他形式存储的批量数据转换为 SingleCellExperiment 结构
bulkSCE <- importDittoBulk(
x = list(counts = exp,
logcounts = logexp), # x可以是 DGEList、DESeqDataSet、SummarizedExperiment 或数据矩阵列表
metadata = data.frame(conditions = conditions,
sex = sex),
reductions = list(pca = pca)
)
# meta数据和降维也可以后续添加
bulkSCE$conditions <- conditions
bulkSCE$sex <- sex
bulkSCE <- addDimReduction(
object = bulkSCE,
embeddings = pca,
name = "pca",
key = "PC")
# 可视化
dittoDimPlot(bulkSCE, "sex", size = 3, do.ellipse = TRUE)
dittoBarPlot(bulkSCE, "sex", group.by = "conditions")
dittoBoxPlot(bulkSCE, "gene1", group.by = "sex")
dittoHeatmap(bulkSCE, getGenes(bulkSCE)[1:10],
annot.by = c("conditions", "sex"))
dittoSeq-4
3. 
设置
3.1 dittoDimPlot & dittoScatterPlot
# dittoScatterPlot() 的轴是基因表达数据或meta数据，dittoDimPlot() 的轴是降维，如 tsne、pca、umap 或类似数据
dittoScatterPlot(
object = sce,
x.var = "nCount_RNA", y.var = "nFeature_RNA",
color.var = "percent.mito")
dittoDimPlot(sce, "cluster",
do.label = TRUE,
labels.repel = FALSE,
add.trajectory.lineages = list(
c("9","3"),
c("8","7","2","4"),
c("8","7","1"),
c("5","11","6"),
c("10","0")),
trajectory.cluster.meta = "cluster")
dittoSeq-5
3.2 dittoDimHex & dittoScatterHex
# 与“Plot”版本类似，绘图区域被分成六边形箱
dittoScatterHex(
object = sce,
x.var = "PPY", y.var = "INS",
color.var = "label",
colors = c(1:4,7), max.density = 15)
dittoDimHex(sce, "INS")
dittoSeq-6
3.3 dittoPlot (and dittoRidgePlot + dittoBoxPlot wrappers)
# dittoPlot() 是主函数，dittoRidgePlot() 和 dittoBoxPlot() 本质上只是将输入图的默认值从 c(“jitter”, “vlnplot”) 调整为c(“ridgeplot”) 或 or c(“boxplot”,“jitter”)
dittoPlot(sce, "ENO1", group.by = "label",
plots = c("jitter", "vlnplot", "boxplot"), # <- order matters
# change the color and size of jitter points
jitter.color = "blue", jitter.size = 0.5,
# change the outline color and width, and remove the fill of boxplots
boxplot.color = "white", boxplot.width = 0.1,
boxplot.fill = FALSE,
# change how the violin plot widths are normalized across groups
vlnplot.scaling = "count"
)
dittoRidgePlot(sce, "ENO1", group.by = "label")
dittoBoxPlot(sce, "ENO1", group.by = "label")
dittoSeq-7
3.4 dittoHeatmap
# Pick Genes
genes <- c("SST", "REG1A", "PPY", "INS", "CELA3A", "PRSS2", "CTRB1",
"CPA1", "CTRB2" , "REG3A", "REG1B", "PRSS1", "GCG", "CPB1",
"SPINK1", "CELA3B", "CLPS", "OLFM4", "ACTG1", "FTL")
dittoHeatmap(sce, genes,
annot.by = c("label", "donor"),
order.by = "donor",
scaled.to.max = TRUE,
show_colnames = FALSE,
show_rownames = FALSE)
# Highlight certain genes
dittoHeatmap(sce, genes,
annot.by = c("label", "donor"),
highlight.features = genes[1:3],
complex = TRUE)
dittoSeq-8
3.5 Multi-Plotters (创建多个图，或将多个变量的数据汇总在一个图中)
3.5.1 dittoDotPlot
delta.genes <- c(
"SST", "RBP4", "LEPR", "PAPPA2", "LY6H",
"CBLN4", "GPX3", "BCHE", "HHEX", "DPYSL3",
"SERPINA1", "SEC11C", "ANXA2", "CHGB", "RGS2",
"FXYD6", "KCNIP1", "SMOC1", "RPL10", "LRFN5")
dittoDotPlot(sce, vars = delta.genes,
group.by = "label",
scale = FALSE)
dittoSeq-9
3.5.2 multi_dittoPlot & dittoPlotVarsAcrossGroups
multi_dittoPlot(sce, delta.genes[1:6],
group.by = "label",
vlnplot.lineweight = 0.2, jitter.size = 0.3)
dittoPlotVarsAcrossGroups(sce, delta.genes,
group.by = "label",
main = "Delta-cell Markers")
dittoSeq-10
3.5.3 multi_dittoDimPlot & multi_dittoDimPlotVaryCells
multi_dittoDimPlot(sce, delta.genes[1:6])
multi_dittoDimPlotVaryCells(sce, delta.genes[1],
vary.cells.meta = "label")
dittoSeq-11
