Layer modeling correlation of statistics

```
layer_stat_cor(
stats,
modeling_results = fetch_data(type = "modeling_results"),
model_type = names(modeling_results)[1],
reverse = FALSE,
top_n = NULL
)
```

- stats
A data.frame where the row names are Ensembl gene IDs, the column names are labels for clusters of cells or cell types, and where each cell contains the given statistic for that gene and cell type. These statistics should be computed similarly to the modeling results from the data we provide. For example, like the

`enrichment`

t-statistics that are derived from comparing one layer against the rest. The`stats`

will be matched and then correlated with our statistics.- modeling_results
Defaults to the output of

`fetch_data(type = 'modeling_results')`

. This is a list of tables with the columns`f_stat_*`

or`t_stat_*`

as well as`p_value_*`

and`fdr_*`

plus`ensembl`

. The column name is used to extract the statistic results, the p-values, and the FDR adjusted p-values. Then the`ensembl`

column is used for matching in some cases. See`fetch_data()`

for more details.- model_type
A named element of the

`modeling_results`

list. By default that is either`enrichment`

for the model that tests one human brain layer against the rest (one group vs the rest),`pairwise`

which compares two layers (groups) denoted by`layerA-layerB`

such that`layerA`

is greater than`layerB`

, and`anova`

which determines if any layer (group) is different from the rest adjusting for the mean expression level. The statistics for`enrichment`

and`pairwise`

are t-statistics while the`anova`

model ones are F-statistics.- reverse
A

`logical(1)`

indicating whether to multiply by`-1`

the input statistics and reverse the`layerA-layerB`

column names (using the`-`

) into`layerB-layerA`

.- top_n
An

`integer(1)`

specifying whether to filter to the top n marker genes. The default is`NULL`

in which case no filtering is done.

A correlation matrix between `stats`

and our statistics using only
the Ensembl gene IDs present in both tables. The columns are sorted using
a hierarchical cluster.

Check https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/dlpfc_snRNAseq_annotation.R for a full analysis from which this family of functions is derived from.

Other Layer correlation functions:
`annotate_registered_clusters()`

,
`layer_stat_cor_plot()`

```
## Obtain the necessary data
if (!exists("modeling_results")) {
modeling_results <- fetch_data(type = "modeling_results")
}
#> snapshotDate(): 2022-10-31
#> 2023-03-17 21:37:25 loading file /github/home/.cache/R/BiocFileCache/37c3a25ecd4_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"
)
## Explore the correlation matrix
head(cor_stats_layer[, seq_len(3)])
#> WM Layer6 Layer5
#> 22 (3) 0.6824669 -0.009192291 -0.1934265
#> 3 (3) 0.7154122 -0.070042729 -0.2290574
#> 23 (3) 0.6637885 -0.031467704 -0.2018306
#> 17 (3) 0.6364983 -0.094216046 -0.2026147
#> 21 (3) 0.6281443 -0.050336358 -0.1988774
#> 7 (4) 0.1850724 -0.197283175 -0.2716890
summary(cor_stats_layer)
#> WM Layer6 Layer5 Layer4
#> Min. :-0.46352 Min. :-0.197283 Min. :-0.27169 Min. :-0.253477
#> 1st Qu.:-0.26653 1st Qu.:-0.071919 1st Qu.:-0.14318 1st Qu.:-0.152714
#> Median :-0.19813 Median :-0.039558 Median : 0.03858 Median : 0.003875
#> Mean :-0.02982 Mean : 0.004476 Mean : 0.01355 Mean : 0.019215
#> 3rd Qu.: 0.12482 3rd Qu.: 0.018964 3rd Qu.: 0.16692 3rd Qu.: 0.160288
#> Max. : 0.71541 Max. : 0.457031 Max. : 0.30194 Max. : 0.425598
#> Layer3 Layer2 Layer1
#> Min. :-0.36105 Min. :-0.31419 Min. :-0.29670
#> 1st Qu.:-0.10007 1st Qu.:-0.06673 1st Qu.:-0.12129
#> Median : 0.05998 Median : 0.01664 Median :-0.01590
#> Mean : 0.02022 Mean : 0.01056 Mean :-0.01750
#> 3rd Qu.: 0.13638 3rd Qu.: 0.11008 3rd Qu.: 0.03523
#> Max. : 0.56413 Max. : 0.50734 Max. : 0.63940
## Repeat with top_n set to 10
summary(layer_stat_cor(
tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
modeling_results,
model_type = "enrichment",
top_n = 10
))
#> WM Layer6 Layer5
#> Min. :-0.419078 Min. :-0.245585 Min. :-0.309621
#> 1st Qu.:-0.223879 1st Qu.:-0.148746 1st Qu.:-0.177987
#> Median :-0.104689 Median :-0.034822 Median : 0.049559
#> Mean :-0.003598 Mean :-0.008681 Mean :-0.004698
#> 3rd Qu.: 0.040547 3rd Qu.: 0.036607 3rd Qu.: 0.145510
#> Max. : 0.733922 Max. : 0.586829 Max. : 0.393224
#> Layer4 Layer3 Layer2
#> Min. :-0.333112 Min. :-0.3983700 Min. :-0.206511
#> 1st Qu.:-0.119284 1st Qu.:-0.1264219 1st Qu.:-0.101970
#> Median :-0.004004 Median :-0.0000972 Median :-0.017683
#> Mean : 0.007484 Mean : 0.0055855 Mean : 0.009139
#> 3rd Qu.: 0.116301 3rd Qu.: 0.1143531 3rd Qu.: 0.092592
#> Max. : 0.458987 Max. : 0.6718714 Max. : 0.472751
#> Layer1
#> Min. :-0.263452
#> 1st Qu.:-0.149696
#> Median :-0.064278
#> Mean :-0.001268
#> 3rd Qu.: 0.032686
#> Max. : 0.728782
```