Annotate single cells using SingleR
Usage
RunSingleR(
srt_query,
srt_ref,
query_group = NULL,
ref_group = NULL,
query_assay = "RNA",
ref_assay = "RNA",
genes = "de",
de.method = "wilcox",
sd.thresh = 1,
de.n = NULL,
aggr.ref = FALSE,
aggr.args = list(),
quantile = 0.8,
fine.tune = TRUE,
tune.thresh = 0.05,
prune = TRUE,
BPPARAM = BiocParallel::bpparam()
)Arguments
- srt_query
An object of class Seurat to be annotated with cell types.
- srt_ref
An object of class Seurat storing the reference cells.
- query_group
A character vector specifying the column name in the `srt_query` metadata that represents the cell grouping.
- ref_group
A character vector specifying the column name in the `srt_ref` metadata that represents the cell grouping.
- query_assay
A character vector specifying the assay to be used for the query data. Defaults to the default assay of the `srt_query` object.
- ref_assay
A character vector specifying the assay to be used for the reference data. Defaults to the default assay of the `srt_ref` object.
- genes
"genes" parameter in
SingleRfunction.- de.method
"de.method" parameter in
SingleRfunction.- sd.thresh
Deprecated and ignored.
- de.n
An integer scalar specifying the number of DE genes to use when
genes="de". Ifde.method="classic", defaults to500 * (2/3) ^ log2(N)whereNis the number of unique labels. Otherwise, defaults to 10.- aggr.ref, aggr.args
Arguments controlling the aggregation of the references prior to annotation, see
trainSingleR.- quantile, fine.tune, tune.thresh, prune
Further arguments to pass to
classifySingleR.- BPPARAM
A BiocParallelParam object specifying how parallelization should be performed in other steps, see
?trainSingleRand?classifySingleRfor more details.
Examples
data("panc8_sub")
# Simply convert genes from human to mouse and preprocess the data
genenames <- make.unique(capitalize(rownames(panc8_sub), force_tolower = TRUE))
panc8_sub <- RenameFeatures(panc8_sub, newnames = genenames)
#> Rename features for the assay: RNA
panc8_sub <- check_srtMerge(panc8_sub, batch = "tech")[["srtMerge"]]
#> [2023-11-21 07:51:19.069847] Spliting srtMerge into srtList by column tech... ...
#> [2023-11-21 07:51:19.233309] Checking srtList... ...
#> Data 1/5 of the srtList is raw_normalized_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 1/5 of the srtList...
#> Data 2/5 of the srtList is raw_normalized_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 2/5 of the srtList...
#> Data 3/5 of the srtList is raw_normalized_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 3/5 of the srtList...
#> Data 4/5 of the srtList is raw_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 4/5 of the srtList...
#> Data 5/5 of the srtList is raw_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 5/5 of the srtList...
#> Use the separate HVF from srtList...
#> Number of available HVF: 2000
#> [2023-11-21 07:51:21.056182] Finished checking.
# Annotation
data("pancreas_sub")
pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-11-21 07:51:22.816783] Start Standard_SCP
#> [2023-11-21 07:51:22.817021] Checking srtList... ...
#> Data 1/1 of the srtList is raw_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 1/1 of the srtList...
#> Use the separate HVF from srtList...
#> Number of available HVF: 2000
#> [2023-11-21 07:51:23.426502] Finished checking.
#> [2023-11-21 07:51:23.426677] Perform ScaleData on the data...
#> [2023-11-21 07:51:23.499636] Perform linear dimension reduction (pca) on the data...
#> Warning: The following arguments are not used: force.recalc
#> Warning: The following arguments are not used: force.recalc
#> [2023-11-21 07:51:24.168432] Perform FindClusters (louvain) on the data...
#> [2023-11-21 07:51:24.242835] Reorder clusters...
#> [2023-11-21 07:51:24.303697] Perform nonlinear dimension reduction (umap) on the data...
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> [2023-11-21 07:51:32.530625] Standard_SCP done
#> Elapsed time: 9.71 secs
pancreas_sub <- RunSingleR(
srt_query = pancreas_sub, srt_ref = panc8_sub,
query_group = "Standardclusters", ref_group = "celltype",
)
#> Detected srt_query data type: log_normalized_counts
#> Detected srt_ref data type: log_normalized_counts
#> Perform SingleRCluster on the data...
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#> Warning: missing values for 'group'
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#> Error in `rownames<-`(`*tmp*`, value = new_rownames): missing values not allowed in rownames
CellDimPlot(pancreas_sub, group.by = "singler_annotation")
#> Error in CellDimPlot(pancreas_sub, group.by = "singler_annotation"): singler_annotation is not in the meta.data of srt object.
pancreas_sub <- RunSingleR(
srt_query = pancreas_sub, srt_ref = panc8_sub,
query_group = NULL, ref_group = "celltype"
)
#> Detected srt_query data type: log_normalized_counts
#> Detected srt_ref data type: log_normalized_counts
#> Perform SingleRCell on the data...
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#> Error: No cell overlap between new meta data and Seurat object
CellDimPlot(pancreas_sub, group.by = "singler_annotation")
#> Error in CellDimPlot(pancreas_sub, group.by = "singler_annotation"): singler_annotation is not in the meta.data of srt object.