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Run KNN prediction

Source: R/RunKNNPredict.R
RunKNNPredict.Rd

This function performs KNN prediction to annotate cell types based on reference scRNA-seq or bulk RNA-seq data.

Usage

RunKNNPredict(
  srt_query,
  srt_ref = NULL,
  bulk_ref = NULL,
  query_group = NULL,
  ref_group = NULL,
  query_assay = NULL,
  ref_assay = NULL,
  query_reduction = NULL,
  ref_reduction = NULL,
  query_dims = 1:30,
  ref_dims = 1:30,
  query_collapsing = !is.null(query_group),
  ref_collapsing = TRUE,
  return_full_distance_matrix = FALSE,
  features = NULL,
  features_type = c("HVF", "DE"),
  feature_source = "both",
  nfeatures = 2000,
  DEtest_param = list(max.cells.per.ident = 200, test.use = "wilcox"),
  DE_threshold = "p_val_adj < 0.05",
  nn_method = NULL,
  distance_metric = "cosine",
  k = 30,
  filter_lowfreq = 0,
  prefix = "KNNPredict",
  verbose = TRUE
)

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.

bulk_ref

A cell atlas matrix, where cell types are represented by columns and genes are represented by rows. Either srt_ref or bulk_ref must be provided.

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. Default is the default assay of the srt_query object.

ref_assay

A character vector specifying the assay to be used for the reference data. Default is the default assay of the srt_ref object.

query_reduction

A character vector specifying the dimensionality reduction method used for the query data. If NULL, the function will use the default reduction method specified in the srt_query object.

ref_reduction

A character vector specifying the dimensionality reduction method used for the reference data. If NULL, the function will use the default reduction method specified in the srt_ref object.

query_dims

A numeric vector specifying the dimensions to be used for the query data. Default is the first 30 dimensions.

ref_dims

A numeric vector specifying the dimensions to be used for the reference data. Default is the first 30 dimensions.

query_collapsing

A boolean value indicating whether the query data should be collapsed to group-level average expression values. If TRUE, the function will calculate the average expression values for each group in the query data and the annotation will be performed separately for each group. Otherwise it will use the raw expression values for each cell.

ref_collapsing

A boolean value indicating whether the reference data should be collapsed to group-level average expression values. If TRUE, the function will calculate the average expression values for each group in the reference data and the annotation will be performed separately for each group. Otherwise it will use the raw expression values for each cell.

return_full_distance_matrix

A boolean value indicating whether the full distance matrix should be returned. If TRUE, the function will return the distance matrix used for the KNN prediction, otherwise it will only return the annotated cell types.

features

A character vector specifying the features to be used for the KNN prediction. If NULL, all the features in the query and reference data will be used.

features_type

A character vector specifying the type of features to be used for the KNN prediction. Must be one of "HVF" (highly variable features) or "DE" (differentially expressed features). Default is "HVF".

feature_source

The source of the features to be used. Must be one of "both", "query", or "ref". Default is "both".

nfeatures

An integer specifying the maximum number of features to be used for the KNN prediction. Default is 2000.

DEtest_param

A list of parameters to be passed to the differential expression test function if features_type is set to "DE". Default is list(max.cells.per.ident = 200, test.use = "wilcox").

DE_threshold

Threshold used to filter the DE features. If using "roc" test, DE_threshold should be needs to be reassigned. e.g. "power > 0.5". Default is "p_val < 0.05".

nn_method

A character string specifying the nearest neighbor search method to use. Options are "raw", "annoy", "rann", and "cpp". If "raw" is selected, the function will use the brute-force method to find the nearest neighbors. If "annoy" is selected, the function will use the Annoy library for approximate nearest neighbor search. If "rann" is selected, the function will use the RANN library for approximate nearest neighbor search. If "cpp" is selected, the function will use the compiled exact top-k search. If not provided, the function will use "cpp" for Euclidean or cosine distance, otherwise it will choose the search method based on the size of the query and reference datasets.

distance_metric

A character vector specifying the distance metric to be used for calculating similarity between cells. Must be one of "cosine", "euclidean", "manhattan", or "hamming". Default is "cosine".

k

A number of nearest neighbors to be considered for the KNN prediction. Default is 30.

filter_lowfreq

An integer specifying the threshold for filtering low-frequency cell types from the predicted results. Cell types with a frequency lower than filter_lowfreq will be labelled as "unreliable". Default is 0, which means no filtering will be performed.

prefix

A character vector specifying the prefix to be added to the resulting annotations. Default is "KNNPredict".

verbose

Whether to print the message. Default is TRUE.

See also

RunKNNMap, RunSingleR, CellCorHeatmap

Examples

# Annotate cells using bulk RNA-seq data
data(pancreas_sub)
data(ref_scMCA)
pancreas_sub <- standard_scop(pancreas_sub)

# Set the number of threads for RcppParallel
# details see: ?RcppParallel::setThreadOptions
# if (requireNamespace("RcppParallel", quietly = TRUE)) {
#   RcppParallel::setThreadOptions()
# }
pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  bulk_ref = ref_scMCA
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)

# Removal of low credible cell types from the predicted results
pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  bulk_ref = ref_scMCA,
  filter_lowfreq = 30
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)

# Annotate clusters using bulk RNA-seq data
pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  query_group = "SubCellType",
  bulk_ref = ref_scMCA
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)

# Annotate using single cell RNA-seq data
data(panc8_sub)
# Simply convert genes from human to mouse and preprocess the data
genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
panc8_sub <- SeuratObject::JoinLayers(panc8_sub)
pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype"
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)
FeatureDimPlot(
  pancreas_sub,
  features = "KNNPredict_simil"
)

pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  ref_collapsing = FALSE
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)
FeatureDimPlot(
  pancreas_sub,
  features = "KNNPredict_prob"
)

pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "SubCellType",
  ref_group = "celltype"
)
CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)
FeatureDimPlot(
  pancreas_sub,
  features = "KNNPredict_simil"
)

# Annotate with DE gene instead of HVF
pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  features_type = "DE",
  feature_source = "ref",
  DEtest_param = list(cores = 2)
)

CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)

FeatureDimPlot(
  pancreas_sub,
  features = "KNNPredict_simil"
)

pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "SubCellType",
  ref_group = "celltype",
  features_type = "DE",
  feature_source = "both",
  DEtest_param = list(cores = 2)
)

CellDimPlot(
  pancreas_sub,
  group.by = "KNNPredict_classification",
  label = TRUE
)

FeatureDimPlot(
  pancreas_sub,
  features = "KNNPredict_simil"
)