This function combines the output of sig_genes_extract() from all the layer-level (group-level) modeling results and builds the data required for functions such as layer_boxplot().

sig_genes_extract_all(
  n = 10,
  modeling_results = fetch_data(type = "modeling_results"),
  sce_layer = fetch_data(type = "sce_layer")
)

Arguments

n

The number of the top ranked genes to extract.

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. Typically this is the set of reference statistics used in layer_stat_cor().

sce_layer

Defaults to the output of fetch_data(type = 'sce_layer'). This is a SingleCellExperiment object with the spot-level Visium data compressed via pseudo-bulking to the layer-level (group-level) resolution. See fetch_data() for more details.

Value

A DataFrame-class with the extracted statistics in long format. The specific columns are described further in the vignette.

See also

Other Layer modeling functions: layer_boxplot(), sig_genes_extract()

Examples


## Obtain the necessary data
if (!exists("modeling_results")) {
    modeling_results <- fetch_data(type = "modeling_results")
}
#> 2024-12-13 19:42:20.719143 loading file /github/home/.cache/R/BiocFileCache/5c656d46b9_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer")
#> 2024-12-13 19:42:21.836976 loading file /github/home/.cache/R/BiocFileCache/5c643a903ea_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1

## top 10 genes for all models
sig_genes_extract_all(
    modeling_results = modeling_results,
    sce_layer = sce_layer
)
#> DataFrame with 510 rows and 12 columns
#>           top  model_type        test        gene      stat        pval
#>     <integer> <character> <character> <character> <numeric>   <numeric>
#> 1           1  enrichment          WM       NDRG1   16.3053 1.25896e-26
#> 2           2  enrichment          WM      PTP4A2   16.1469 2.25133e-26
#> 3           3  enrichment          WM        AQP1   15.9927 3.97849e-26
#> 4           4  enrichment          WM       PAQR6   15.1971 7.86258e-25
#> 5           5  enrichment          WM      ANP32B   14.9798 1.80183e-24
#> ...       ...         ...         ...         ...       ...         ...
#> 506         6       anova        noWM        HOPX   157.180 3.16423e-33
#> 507         7       anova        noWM      CLSTN2   148.428 1.55814e-32
#> 508         8       anova        noWM      TUBA1B   135.620 1.89089e-31
#> 509         9       anova        noWM      HS3ST2   135.387 1.98244e-31
#> 510        10       anova        noWM        ETV1   130.017 6.03393e-31
#>             fdr gene_index         ensembl         in_rows in_rows_top20
#>       <numeric>  <integer>     <character>   <IntegerList> <IntegerList>
#> 1   2.51372e-22      10404 ENSG00000104419   1,110,113,... 1,110,113,...
#> 2   2.51372e-22        487 ENSG00000184007           2,126         2,126
#> 3   2.96145e-22       8201 ENSG00000240583   3,104,112,... 3,104,112,...
#> 4   4.38948e-21       1501 ENSG00000160781           4,130         4,130
#> 5   8.04735e-21      10962 ENSG00000136938           5,123         5,123
#> ...         ...        ...             ...             ...           ...
#> 506 1.17767e-29       5291 ENSG00000171476 234,248,256,...              
#> 507 4.97068e-29       4638 ENSG00000158258 390,415,424,...              
#> 508 4.91887e-28      13893 ENSG00000123416 360,362,373,...              
#> 509 4.91887e-28      16964 ENSG00000122254 375,386,413,...              
#> 510 1.34744e-27       8095 ENSG00000006468     446,464,510              
#>                                                           results
#>                                                   <CharacterList>
#> 1                      WM_top1,WM-Layer4_top10,WM-Layer5_top3,...
#> 2                                          WM_top2,WM-Layer6_top6
#> 3                       WM_top3,WM-Layer4_top4,WM-Layer5_top2,...
#> 4                                         WM_top4,WM-Layer6_top10
#> 5                                          WM_top5,WM-Layer6_top3
#> ...                                                           ...
#> 506  Layer3-Layer5_top4,Layer3-Layer6_top8,Layer4-Layer5_top6,...
#> 507 Layer6-Layer1_top10,Layer5-Layer2_top5,Layer6-Layer2_top4,...
#> 508 Layer3-Layer1_top10,Layer4-Layer1_top2,Layer5-Layer1_top3,...
#> 509  Layer5-Layer1_top5,Layer6-Layer1_top6,Layer5-Layer2_top3,...
#> 510              Layer5-Layer3_top6,Layer5-Layer4_top4,noWM_top10