A toolbox to explore group-/batch-specific bias and data integration in single-cell RNA-seq (scRNA-seq) datasets.
Data integration and batch effect correction belong to the major challenges in scRNA-seq. A variety of tools and methods have been developed to address them in different ways. To apply those it is key to understand their effect as well as the underlying technical variation in the data. Thus new tools and metrics are needed, that help to explore, quantify and compare batch effects in the context of data integration and batch effect removal. Similar to biological triggers and signals, batch effects can affect cells in different ways. To explore them with cell-specific metrics can help us to better understand, correct and interpret them.
Here we provide a toolbox to explore and compare group effects in single-cell RNA-seq data. It has two major applications:
For this purpose it introduces two new metrics:
Besides this, several exploratory plotting functions enable evaluation of key integration and mixing features.
To run CellMixS, open R and install using BiocManager with the following commands:
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install("almutlue/CellMixS")
The main metrics
ldfDiff use a
SingleCellExperiment object as input.
You need to specify the batch variable as defined in the
colData, the number of k-nearest neighbours to include
k and optional the reduced dimensions to use
sce_cms <- cms(sce, k = 70, group = "batch")
ldfDiff compares the dataset structure before and after integration you need to specify unaligned and aligned
sce_ldf <- ldfDiff(sce_pre_list, sce_combined, group = "batch", k = 70)
Please have a look into the vignette for details.
You can explore batch effects by visualizing metrics and batches aside.
The histogram of
cms score can be read like a p.value histogram and is flat for random batch mixing (batch100).
If a batch related bias is present a high number of low
cms scores can be seen (batch0).
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