Visualization with built-in plots
Default plots from main functions
Default plots can be printed to the screen and/or saved to the disk.
# set options to print & save the plots
options(moo_print_plots = TRUE)
options(moo_save_plots = TRUE)
# when moo_save_plots is TRUE, plots are saved to this directory:
options(moo_plots_dir = "./figures")See ?MOSuite::options for more information.
clean
moo <- create_multiOmicDataSet_from_dataframes(
sample_metadata = as.data.frame(nidap_sample_metadata),
counts_dat = as.data.frame(nidap_raw_counts)
) |>
clean_raw_counts()
#> Saving 5 x 4 in image
#> * cleaning raw counts
#>
#> Not able to identify multiple id's in GeneName
#>
#> Columns that can be used to aggregate gene information GeneName
#>
#> Aggregating the counts for the same ID in different chromosome locations.
#> Column used to Aggregate duplicate IDs: GeneName
#> Number of rows before Collapse: 43280
#>
#> no duplicated IDs in GeneName
#> Saving 5 x 4 in imagefilter
moo <- moo |>
filter_counts(group_colname = "Group")
#> * filtering clean counts
#> Number of features after filtering: 7943
#> Saving 5 x 4 in imagenormalize
moo <- moo |>
normalize_counts(group_colname = "Group")
#> * normalizing filt counts
#> voomLmFit is now recommended over voom
#> Total number of features included: 7943
#> Saving 5 x 4 in image
#> Sample columns: A1, Sample columns: A2, Sample columns: A3, Sample columns: B1, Sample columns: B2, Sample columns: B3, Sample columns: C1, Sample columns: C2, Sample columns: C3batch correct
moo <- moo |>
batch_correct_counts(
covariates_colname = "Group",
batch_colname = "Batch",
label_colname = "Label"
)
#> * batch-correcting norm-voom counts
#> Found2batches
#> Adjusting for2covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding parametric adjustments
#> Adjusting the Data
#> Saving 5 x 4 in image
#> The total number of features in output: 7943
#> Number of samples after batch correction: 10differential expression
moo <- moo |>
diff_counts(
count_type = "filt",
covariates_colnames = c("Group", "Batch"),
contrast_colname = c("Group"),
contrasts = c("B-A", "C-A", "B-C"),
input_in_log_counts = FALSE,
return_mean_and_sd = FALSE,
voom_normalization_method = "quantile",
)
#> * differential counts
#> Setting first column of `counts` as gene annotation.
#> voomLmFit is now recommended over voom
#> Total number of genes included: 7943
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
#> Saving 5 x 4 in image
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Specialized plots
3D PCA
Expression Heatmap
heatmap_plot <- plot_expr_heatmap(
moo,
count_type = "norm",
sub_count_type = "voom",
group_colname = "Group"
)
#> The total number of genes in heatmap: 500
Volcano
Enhanced
Create an enhanced volcano plot for each contrast:
dat_volcano_enhanced <- moo |>
plot_volcano_enhanced()
#> Joining with `by = join_by(GeneName)`
#> Joining with `by = join_by(GeneName)`
#> Genes in initial dataset: 7943
#> Max y: 4.56088783571366
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the EnhancedVolcano package.
#> Please report the issue to the authors.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
#> ℹ Please use the `linewidth` argument instead.
#> ℹ The deprecated feature was likely used in the EnhancedVolcano package.
#> Please report the issue to the authors.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Genes in initial dataset: 7943
#>
#> Max y: 4.70280335204325
#>
#> Genes in initial dataset: 7943
#>
#> Max y: 4.34744066227962
or plot only one contrast at a time by selecting the contrast from
the analyses slot:
dat_volcano_enhanced_B_A <- moo@analyses$diff[["B-A"]] |>
plot_volcano_enhanced(
feature_id_colname = "GeneName",
change_colname = "logFC",
signif_colname = "adjpval"
)
#> Genes in initial dataset: 7943
#> Max y: 4.56088783571366
Summary
Create an enhanced volcano plot for each contrast and compose them in one figure:
dat_volcano_summary <- moo |>
plot_volcano_summary()
#> Joining with `by = join_by(GeneName)`
#> Joining with `by = join_by(GeneName)`
#> Preparing table for contrast: B-A
#> Fold change column: B-A_logFC
#> Significance column: B-A_adjpval
#> Total number of features included in volcano plot: 7943
#> Preparing table for contrast: C-A
#> Fold change column: C-A_logFC
#> Significance column: C-A_adjpval
#> Total number of features included in volcano plot: 7943
#> Preparing table for contrast: B-C
#> Fold change column: B-C_logFC
#> Significance column: B-C_adjpval
#> Total number of features included in volcano plot: 7943
#> Running Enhanced Volcano:
#> Genes in initial dataset: 7943
#> Max y: 4.56088783571366
#> Genes in initial dataset: 7943
#> Max y: 4.70280335204325
#> Genes in initial dataset: 7943
#> Max y: 4.34744066227962
head(dat_volcano_summary)
#> GeneName Contrast FC logFC tstat pval
#> B-A.1 Dntt B-A -42.727551 -5.417095 -15.54572 3.460410e-09
#> B-A.2 Tmsb4x B-A 3.845863 1.943307 12.82926 2.930649e-08
#> B-A.3 Flt3 B-A -7.743692 -2.953022 -11.29797 1.173487e-07
#> B-A.4 Tspan13 B-A -7.035795 -2.814713 -11.06018 1.476477e-07
#> B-A.5 Tapt1 B-A -5.297586 -2.405335 -10.64544 2.226279e-07
#> B-A.6 Itgb7 B-A 8.882141 3.150907 10.62882 2.263833e-07
#> adjpval
#> B-A.1 2.748604e-05
#> B-A.2 1.163907e-04
#> B-A.3 2.931915e-04
#> B-A.4 2.931915e-04
#> B-A.5 2.996937e-04
#> B-A.6 2.996937e-04
Customizing plots
Plots from main functions
You can create the plots generated by the main analysis functions directly so you can customize them to fit your needs.
See the visualization reference for a full list of plotting functions.
Customizing ggplot objects
Plotting functions that use ggplot2 return ggplot objects. You can customize them by adding more ggplot layers, just like any other ggplot.
plot_pca_2d(
moo,
count_type = "batch",
principal_components = c(1, 2),
group_colname = "Batch",
label_colname = "Label"
) +
ggplot2::labs(
title = "Principle components of batch-corrected counts",
caption = "Normalized counts were batch-corrected using svg::ComBat()"
)
Custom colors
MOSuite comes bundled with a default palette:
When creating a multiOmicDataSet object such as with
create_multiOmicDataSet_from_dataframes(), default colors
are automatically picked from mosuite_palette and set in
the analyses slot. You can access the defaults directly:
moo@analyses$colors
#> $Sample
#> A1 A2 A3 B1 B2 B3 C1 C2
#> "#5954d6" "#e1562c" "#b80058" "#00c6f8" "#d163e6" "#00a76c" "#ff9287" "#008cf9"
#> C3
#> "#006e00"
#>
#> $Group
#> A B C
#> "#5954d6" "#e1562c" "#b80058"
#>
#> $Replicate
#> 1 2 3
#> "#00c6f8" "#d163e6" "#00a76c"
#>
#> $Batch
#> 1 2
#> "#ff9287" "#008cf9"
#>
#> $Label
#> A1 A2 A3 B1 B2 B3 C1 C2
#> "#5954d6" "#e1562c" "#b80058" "#00c6f8" "#d163e6" "#00a76c" "#ff9287" "#008cf9"
#> C3
#> "#006e00"The plotting functions access these colors by default, unless
overridden with the color_values argument:
# color palette accessed from moo@analyses$colors[['Group']]
plot_read_depth(
moo,
count_type = "clean",
group_colname = "Group"
)
# color palette overridden by color_values
plot_read_depth(
moo,
count_type = "clean",
group_colname = "Group",
color_values = c(A = "red", B = "green", C = "blue")
)
You can change the default colors in the multiOmicDataSet so that all plotting functions will use your chosen color palette.
moo@analyses$colors[["Batch"]] <- c("1" = "#0E7175", "2" = "#C35BCA")
moo@analyses$colors[["Replicate"]] <- c(
"1" = "#89973D",
"2" = "#E8B92F",
"3" = "#A45E41"
)
moo@analyses$colors[["Group"]] <- c(A = "#E69F00", B = "#56B4E9", C = "#009E73")View the colors in a multiOmicDataSet with
display_colors():
Plotting functions will then use the custom colors set in
moo@analyses$colors:
plot_expr_heatmap(
moo,
count_type = "norm",
sub_count_type = "voom",
group_colname = "Group"
)
#> The total number of genes in heatmap: 500