R/annotate_registered_clusters.R
annotate_registered_clusters.RdOnce 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
)The output of layer_stat_cor().
A numeric(1) specifying the minimum correlation
that a given cluster must have against any of the layers (by default) to
be considered as having a 'good' assignment. Otherwise, the confidence will
be 'poor' and the final label will have an asterisk.
A numeric(1) specifying the threshold for merging
or not layer assignments (by default). This is a proportion of the difference
between the current correlation and the next highest given the units of the
next highest correlation. Defaults to a difference of 25% of the next highest
correlation: if the observed difference is lower than this threshold, then we
keep merging. Higher values will lead to more layers (by default) being
merged.
A data.frame with 3 columns. Your clusters, the layer_confidence
which depends on confidence_threshold, and the layer_label.
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.
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")
}
#> 2024-05-03 14:23:02.214292 loading file /github/home/.cache/R/BiocFileCache/d0b17ec1af6_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
## 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)
#> cluster layer_confidence layer_label
#> 1 22 (3) good WM
#> 2 3 (3) good WM
#> 3 23 (3) good WM
#> 4 17 (3) good WM
#> 5 21 (3) good WM
#> 6 7 (4) good L1
#> 7 5 (4) good L1
#> 8 9 (5) poor L1/WM*
#> 9 26 (5) poor L1*
#> 10 1 (4) poor L1/WM*
#> 11 24 (6) poor L6*
#> 12 13 (1) good L6
#> 13 10 (1) good L6
#> 14 27 (1) good L6
#> 15 29 (2) poor L4*
#> 16 14 (2) good L4
#> 17 15 (2) poor L5/4*
#> 18 18 (2) poor L4*
#> 19 2 (1) poor L5*
#> 20 31 (1) good L5
#> 21 8 (1) poor L2*
#> 22 16 (2) poor L2*
#> 23 28 (2) poor L2*
#> 24 30 (2) poor L2*
#> 25 20 (2) poor L3/5/4*
#> 26 11 (2) poor L4/3*
#> 27 25 (2) poor L2/4/3*
#> 28 4 (1) good L3/4
#> 29 12 (1) good L4
#> 30 6 (1) good L2
#> 31 19 (1) good L3
## More relaxed merging threshold
annotate_registered_clusters(cor_stats_layer, cutoff_merge_ratio = 1)
#> cluster layer_confidence layer_label
#> 1 22 (3) good WM
#> 2 3 (3) good WM
#> 3 23 (3) good WM
#> 4 17 (3) good WM
#> 5 21 (3) good WM
#> 6 7 (4) good L1
#> 7 5 (4) good L1
#> 8 9 (5) poor L1/WM*
#> 9 26 (5) poor L1/WM*
#> 10 1 (4) poor L1/WM*
#> 11 24 (6) poor L6/WM/1*
#> 12 13 (1) good L6
#> 13 10 (1) good L6
#> 14 27 (1) good L6
#> 15 29 (2) poor L4/3*
#> 16 14 (2) good L4/5/3
#> 17 15 (2) poor L5/4*
#> 18 18 (2) poor L4/5/3*
#> 19 2 (1) poor L5*
#> 20 31 (1) good L5
#> 21 8 (1) poor L2/3*
#> 22 16 (2) poor L2/3*
#> 23 28 (2) poor L2/3*
#> 24 30 (2) poor L2/3/4/5/1*
#> 25 20 (2) poor L3/5/4*
#> 26 11 (2) poor L4/3/2/5*
#> 27 25 (2) poor L2/4/3/5*
#> 28 4 (1) good L3/4
#> 29 12 (1) good L4/5/3
#> 30 6 (1) good L2/3
#> 31 19 (1) good L3/2/4