Multiple EWCE results from multiple studies

Source: R/merged_ewce.r

merged_ewce combines enrichment results from multiple studies targetting the same scientific problem

merged_ewce(results, reps = 100)

Arguments

results

a list of EWCE results generated using add_res_to_merging_list.
reps

Number of random gene lists to generate (Default=100 but should be >=10,000 for publication-quality results).

Value

dataframe in which each row gives the statistics (p-value, fold change and number of standard deviations from the mean) associated with the enrichment of the stated cell type in the gene list.

Examples

# Load the single cell data
ctd <- ewceData::ctd()

#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache

# Use 3 bootstrap lists for speed, for publishable analysis use >10000
reps <- 3
# Use 5 up/down regulated genes (thresh) for speed, default is 250
thresh <- 5

# Load the data
tt_alzh_BA36 <- ewceData::tt_alzh_BA36()

#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
tt_alzh_BA44 <- ewceData::tt_alzh_BA44()

#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache

# Run EWCE analysis
tt_results_36 <- EWCE::ewce_expression_data(
    sct_data = ctd,
    tt = tt_alzh_BA36,
    thresh = thresh,
    annotLevel = 1,
    reps = reps,
    ttSpecies = "human",
    sctSpecies = "mouse"
)

#> Returning 10,854 unique genes from the user-supplied bg.
#> Standardising CellTypeDataset
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Checking gene list inputs.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Standardising sct_data.
#> Converting gene list input to standardised human genes.
#> Running without gene size control.
#> 6 hit genes remain after filtering.
#> Computing summed proportions.
#> Testing for enrichment in 7 cell types...
#> Sorting results by p-value.
#> Computing BH-corrected q-values.
#> 1 significant cell type enrichment results @ q<0.05 : 
#>           CellType annotLevel p fold_change sd_from_mean q
#> 1 oligodendrocytes          1 0    1.945157     3.480483 0
#> Returning 10,854 unique genes from the user-supplied bg.
#> Standardising CellTypeDataset
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Checking gene list inputs.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Standardising sct_data.
#> Converting gene list input to standardised human genes.
#> Running without gene size control.
#> 5 hit genes remain after filtering.
#> Computing summed proportions.
#> Testing for enrichment in 7 cell types...
#> Sorting results by p-value.
#> Computing BH-corrected q-values.
#> 2 significant cell type enrichment results @ q<0.05 : 
#>       CellType annotLevel p fold_change sd_from_mean q
#> 1    microglia          1 0    1.422840    0.8918925 0
#> 2 pyramidal SS          1 0    1.146459    0.7165786 0
tt_results_44 <- EWCE::ewce_expression_data(
    sct_data = ctd,
    tt = tt_alzh_BA44,
    thresh = thresh,
    annotLevel = 1,
    reps = reps,
    ttSpecies = "human",
    sctSpecies = "mouse"
)

#> Returning 10,854 unique genes from the user-supplied bg.
#> Standardising CellTypeDataset
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Checking gene list inputs.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Standardising sct_data.
#> Converting gene list input to standardised human genes.
#> Running without gene size control.
#> 6 hit genes remain after filtering.
#> Computing summed proportions.
#> Testing for enrichment in 7 cell types...
#> Sorting results by p-value.
#> Computing BH-corrected q-values.
#> 2 significant cell type enrichment results @ q<0.05 : 
#>            CellType annotLevel p fold_change sd_from_mean q
#> 1  oligodendrocytes          1 0    1.438299     4.677587 0
#> 2 endothelial-mural          1 0    1.923386     3.394031 0
#> Returning 10,854 unique genes from the user-supplied bg.
#> Standardising CellTypeDataset
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Checking gene list inputs.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in gprofiler.
#> Using stored `gprofiler_orgs`.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Standardising sct_data.
#> Converting gene list input to standardised human genes.
#> Running without gene size control.
#> 5 hit genes remain after filtering.
#> Computing summed proportions.
#> Testing for enrichment in 7 cell types...
#> Sorting results by p-value.
#> Computing BH-corrected q-values.
#> 1 significant cell type enrichment results @ q<0.05 : 
#>        CellType annotLevel p fold_change sd_from_mean q
#> 1 pyramidal CA1          1 0    1.359822     3.275729 0

# Fill a list with the results
results <- EWCE::add_res_to_merging_list(tt_results_36)
results <- EWCE::add_res_to_merging_list(tt_results_44, results)

# Perform the merged analysis
# For publication reps should be higher
merged_res <- EWCE::merged_ewce(
    results = results,
    reps = 2
)
print(merged_res)

#>                                   CellType       p        fc sd_from_mean
#> astrocytes_ependymal  astrocytes_ependymal 0.10910 1.1678985   1.16198959
#> endothelial-mural        endothelial-mural 0.10710 1.1688657   1.25405010
#> interneurons                  interneurons 0.67155 0.7853842  -0.69648688
#> microglia                        microglia 0.55440 0.7672514  -0.53308113
#> oligodendrocytes          oligodendrocytes 0.00000 1.6584841   6.20503534
#> pyramidal CA1                pyramidal CA1 0.66670 0.8605966  -0.90599247
#> pyramidal SS                  pyramidal SS 0.77715 0.8408472  -0.67325296
#> astrocytes_ependymal1 astrocytes_ependymal 0.33665 1.0835837   0.44723149
#> endothelial-mural1       endothelial-mural 0.55350 0.7635425  -0.66420002
#> interneurons1                 interneurons 1.00000 0.8301179  -1.47510472
#> microglia1                       microglia 0.22160 1.0609015   0.23482404
#> oligodendrocytes1         oligodendrocytes 0.33665 1.3615614   0.88132354
#> pyramidal CA11               pyramidal CA1 0.00000 1.1758751   2.24622798
#> pyramidal SS1                 pyramidal SS 0.33490 1.0068599   0.03247843
#>                       Direction
#> astrocytes_ependymal         Up
#> endothelial-mural            Up
#> interneurons                 Up
#> microglia                    Up
#> oligodendrocytes             Up
#> pyramidal CA1                Up
#> pyramidal SS                 Up
#> astrocytes_ependymal1      Down
#> endothelial-mural1         Down
#> interneurons1              Down
#> microglia1                 Down
#> oligodendrocytes1          Down
#> pyramidal CA11             Down
#> pyramidal SS1              Down