Plot results of gene frequency Fisher's exact test.

Plots results of Fisher's exact test on gene frequency obtained via gene_frequency_fisher() as a scatterplot.

Usage

fisher_scatterplot(
  fisher_df,
  p_value_col = "Fisher_p_value_fdr",
  annot_threshold = 0.05,
  annot_color = "red",
  gene_sym_col = "GeneName",
  do_not_highlight = NULL,
  keep_not_highlighted = TRUE
)

Arguments

fisher_df

Test results obtained via gene_frequency_fisher()

p_value_col

Name of the column containing the p-value to consider

annot_threshold

Annotate with a different color if a point is below the significance threshold. Single numerical value.

annot_color

The color in which points below the threshold should be annotated

gene_sym_col

The name of the column containing the gene symbol

do_not_highlight

Either NULL, a character vector, an expression or a purrr-style lambda. Tells the function to ignore the highlighting and labeling of these genes even if their p-value is below the threshold. See details.

keep_not_highlighted

If present, how should not highlighted genes be treated? If set to TRUE points are plotted and colored with the chosen color scale. If set to FALSE the points are removed entirely from the plot.

Value

A plot

Details

Specifying genes to avoid highlighting

In some cases, users might want to avoid highlighting certain genes even if their p-value is below the threshold. To do so, use the argument do_not_highlight: character vectors are appropriate for specific genes that are to be excluded, expressions or lambdas allow a finer control. For example we can supply:

with this expression, genes that have a p-value < threshold and start with "MIR" or have an average_TxLen_1 lower than 300 are excluded from the highlighted points. NOTE: keep in mind that expressions are evaluated inside a dplyr::filter context.

Similarly, lambdas are passed to the filtering function but only operate on the column containing the gene symbol.

See also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), integration_alluvial_plot(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob(), top_cis_overtime_heatmap()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
cis <- CIS_grubbs(aggreg, by = "SubjectID")
#> Warning: Warning: missing genes in refgenes table
#> ℹ A total of 5 genes were found in the input data but not in the refgene table. This may be caused by a mismatch in the annotation phase of the matrix. Here is a summary: 
#> # A tibble: 5 × 3
#>   GeneName  GeneStrand chr  
#>   <chr>     <chr>      <chr>
#> 1 PLEKHG4B  -          14   
#> 2 CRELD2    -          15   
#> 3 UBE2D2    +          16   
#> 4 LINC01133 +          19   
#> 5 HTR4      +          6    
#> ℹ NOTE: missing genes will be removed from the final output! Review results carefully
#> ℹ A total of 25 IS will be removed because of missing genes ( 2.33 % of total IS in input)
fisher <- gene_frequency_fisher(cis$cis$PT001, cis$cis$PT002,
    min_is_per_gene = 2
)
fisher_scatterplot(fisher)