导读
CATALYST流式细胞数据分析工具包，plotExprHeatmap函数能进行怎样的可视化。
plotExprHeatmap文档：https://rdrr.io/bioc/CATALYST/man/plotExprHeatmap.html
依赖安装CATALYST
BiocManager::install("CATALYST")
library("CATALYST")
一、输入数据
data(PBMC_fs, PBMC_panel, PBMC_md)
sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
sce <- cluster(sce)
二、"first": scale & trim then aggregate
median scaled & trimmed expression by cluster
中位数标准化：https://www.plob.org/article/829.html
# median scaled & trimmed expression by cluster
plotExprHeatmap(sce,
by = "cluster_id", k = "meta8",
scale = "first", q = 0.05, bars = FALSE)
三、"last": aggregate then scale & trim
scale each marker between 0 and 1
after aggregation (without trimming)
plotExprHeatmap(sce,
scale = "last", q = 0,
bars = TRUE, perc = TRUE,
hm_pal = hcl.colors(10, "YlGnBu", rev = TRUE))
四、"never": aggregate only
raw (un-scaled) median expression by cluster-sample
plotExprHeatmap(sce,
features = "pp38", by = "both", k = "meta10",
scale = "never", row_anno = FALSE, bars = FALSE)
五、Include subset of samples and specific annotations
# include only subset of samples
sub <- filterSCE(sce,
patient_id != "Patient",
sample_id != "Ref3")
# includes specific annotations &
# split into CDx & all other markers
is_cd <- grepl("CD", rownames(sce))
plotExprHeatmap(sub,
rownames(sce)[is_cd],
row_anno = "condition",
bars = FALSE)
plotExprHeatmap(sub,
rownames(sce)[!is_cd],
row_anno = "patient_id",
bars = FALSE)
