View source: R/annotate_registered_clusters.R
annotate_registered_clusters | R Documentation |
Once you have computed the enrichment t-statistics for your sc/snRNA-seq data
using registration_wrapper()
and related functions, you can then use
layer_stat_cor()
and layer_stat_cor_plot()
to perform the spatial
registartion of your sc/snRNA-seq data. This function helps interpret that
matrix and assign layer labels to your clusters.
annotate_registered_clusters(
cor_stats_layer,
confidence_threshold = 0.25,
cutoff_merge_ratio = 0.25
)
cor_stats_layer |
The output of |
confidence_threshold |
A |
cutoff_merge_ratio |
A |
If you change the input modeling_results
to layer_stat_cor()
then the
interpretation of this function could change. For example, maybe you have
your own spatially-resolved transcriptomics data that doesn't have to be
about DLPFC layers.
A data.frame
with 3 columns. Your cluster
s, the layer_confidence
which depends on confidence_threshold
, and the layer_label
.
Other Layer correlation functions:
layer_stat_cor()
,
layer_stat_cor_plot()
## Obtain the necessary data
if (!exists("modeling_results")) {
modeling_results <- fetch_data(type = "modeling_results")
}
## Compute the correlations
cor_stats_layer <- layer_stat_cor(
tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
modeling_results,
model_type = "enrichment"
)
## Obtain labels
annotate_registered_clusters(cor_stats_layer)
## More relaxed merging threshold
annotate_registered_clusters(cor_stats_layer, cutoff_merge_ratio = 1)
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