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2.3.5 工具 | 2018-iSEE:单细胞数据可视化辅助网页工具

刘小泽写于2021.10.27

前言

题目:iSEE: Interactive SummarizedExperiment Explorer
日期:2018-06-14
期刊:F1000Research
链接:https://f1000research.com/articles/7-741
GitHub:https://github.com/iSEE/iSEE
这篇文章虽然发表的比较早,但最近看到其中一个作者Federico Marini和大佬们交流hdf5数据支持的问题,所以还是简单了解一下这个工具吧
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image-20211027125344809
他们讨论的结果是
library(zellkonverter)
sce_h5ad <- readH5AD("file_as_anndata.h5ad")
assayNames(sce_h5ad) <- "logcounts"
HDF5Array::saveHDF5SummarizedExperiment(sce_Bcells_h5ad, dir = "see_as_hdf5")
#And to read that in ->
sce_read_in_again <- HDF5Array::loadHDF5SummarizedExperiment("see_as_hdf5")
library(iSEE)
iSEE(sce_read_in_again)

设计初衷

重在数据展示,而非数据分析
它不是单纯为某一个课题设计的网页工具,而只要是SummarizedExperiment它就可以支持可视化,而我们知道单细胞数据不仅仅是seurat格式,还有很大部分是SummarizedExperiment 。当然,除了单细胞,SummarizedExperiment 在其他领域(比如甲基化)也有涉及,因此这个工具可以无缝衔接支持此格式的R包下游,用来展示rowdata、metadata等。可以说,它最大的亮点就是兼容性和可拓展性
那么它为何对SummarizedExperiment格式这么偏爱呢?
就像我之前在公众号里介绍的,这个对象可以整合 基因组信息(行)以及样本信息(列),并且可以容纳多种表达量类型(比如raw count、normalized count),甚至后期分析的结果也可以存储(比如降维结果)
image-20211027130031898

工具结构

image-20211027130553046
  • Column data plots, for visualising sample metadata stored in the colData slot of the SummarizedExperiment object.
  • Feature assay plots, for visualising experimental observations for a particular feature (e.g. gene) across samples from any assay in the SummarizedExperiment object.
  • Row statistics tables, to present the contents of the rowData slot of the SummarizedExperiment object.
  • Row data plots, for visualising feature metadata stored in the rowData slot of the SummarizedExperiment object.
  • Heatmaps, to visualise assay data for multiple features where samples are ordered by one or more colData fields.
  • Reduced dimension plots, which display any two dimensions from pre-computed dimensionality reduction results (e.g., from PCA or t-SNE). These results are taken from the reducedDim slot if this is available in the object supplied to iSEE.
还设置了大量的参数调节,比如可以对这个数据的列数据进行选取:
image-20211027130653904
目前提供了一些数据作为示例:
  • http://shiny.imbei.uni-mainz.de:3838/iSEE
  • https://marionilab.cruk.cam.ac.uk/iSEE_allen
  • https://marionilab.cruk.cam.ac.uk/iSEE_tcga
  • https://marionilab.cruk.cam.ac.uk/iSEE_pbmc4k
  • https://marionilab.cruk.cam.ac.uk/iSEE_cytof
还支持TCGA数据的可视化
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image-20211027133643944

至于怎么实现的可视化

作者提供了一些rmd作为参考:https://github.com/iSEE/iSEE_instances
image-20211027134159378
# 上游分析得到sce对象
# Once the processing steps above are done, we can call `iSEE` with the subsampled `SingleCellExperiment` object.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("iSEE")
# or also...
BiocManager::install("iSEE", dependencies = TRUE)
if (require(iSEE)) {
iSEE(sce)
}