Projection of cluster labels

Use a fast KNN classifier to predict labels from a smaller giotto object

doClusterProjection(
  target_gobject,
  target_cluster_label_name = "knn_labels",
  spat_unit = NULL,
  feat_type = NULL,
  source_gobject,
  source_cluster_labels = NULL,
  reduction = "cells",
  reduction_method = "pca",
  reduction_name = "pca",
  dimensions_to_use = 1:10,
  knn_k = 10,
  prob = FALSE,
  algorithm = c("kd_tree", "cover_tree", "brute"),
  return_gobject = TRUE
)

Arguments

target_gobject

target giotto object

target_cluster_label_name

name for predicted clusters

spat_unit

spatial unit

feat_type

feature type

source_gobject

source giotto object with annotation data

source_cluster_labels

annotation/labels to use to train KNN classifier

reduction

reduction on cells or features (default = cells)

reduction_method

shared reduction method (default = pca space)

reduction_name

name of shared reduction space (default name = 'pca')

dimensions_to_use

dimensions to use in shared reduction space (default = 1:10)

knn_k

number of k-neighbors to train a KNN classifier

prob

output probabilities together with label predictions

algorithm

nearest neighbor search algorithm

return_gobject

return giotto object

Value

giotto object (default) or data.table with cell metadata

Details

Function to train a KNN with knn. The training data is obtained from the source giotto object (source_gobject) using existing annotations within the cell metadata. Cells without annotation/labels from the target giotto object (target_gobject) will receive predicted labels (and optional probabilities with prob = TRUE).

**IMPORTANT** This projection assumes that you're using the same dimension reduction space (e.g. PCA) and number of dimensions (e.g. first 10 PCs) to train the KNN classifier as you used to create the initial annotations/labels in the source Giotto object.

Altogether this is a convenience function that allow you to work with very big data as you can predict cell labels on a smaller & subsetted Giotto object and then project the cell labels to the remaining cells in the target Giotto object.

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> 
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"
x <- pDataDT(g)
g_small <- subsetGiotto(g, cell_ids = sample(x$cell_ID, 300))
doClusterProjection(
    target_gobject = g, source_gobject = g_small,
    source_cluster_labels = "leiden_clus"
)