Description Usage Arguments Value Examples
Aggregate multiple integration metrics across multiple integration runs, ie from different batch correction algorithms, or different parameters for the same algorithms
1 | calc_sumZscore(metric_df_list, batch_key)
|
metric_df_list |
a list of data.frames generated by applying |
batch_key |
name of batch column in metadata used when generating run_all_metrics |
a vector of aggregated, z-scored metrics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | library(scPOP)
data(sceiad_subset_data)
features <- sceiad_subset_data[, paste0('scviDim_', 1:8)]
metadata_1 <- sceiad_subset_data[,c('Barcode', 'cluster', 'subcluster',
'batch', 'CellType', 'CellType_predict')]
## scramble example dataset to generate multiple integration runs
metadata_2 <- metadata_1
metadata_2$batch <- sample(metadata_2$batch, length(metadata_2$batch))
metadata_2$CellType_predict <- sample(metadata_2$CellType_predict,
length(metadata_2$CellType_predict))
metadata_2$cluster <- sample(metadata_2$cluster, length(metadata_2$cluster))
metadata_3 <- metadata_1
metadata_3$batch <- sample(metadata_3$batch, length(metadata_3$batch))
metadata_3$CellType_predict <- sample(metadata_3$CellType_predict,
length(metadata_3$CellType_predict))
metadata_3$cluster <- sample(metadata_3$cluster, length(metadata_3$cluster))
integration_data_list <- list( metadata_1, metadata_2, metadata_3)
metric_df_list <- lapply(integration_data_list, function(x)
run_all_metrics(reduction = features,
metadata = x,
batch_key = 'batch',
label1_key = 'CellType_predict',
label2_key = 'cluster',
run_name = 'example',
quietly =TRUE
)
)
calc_sumZscore(metric_df_list,'batch' )
|
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