View source: R/beta_div_test.R
plot_ancombc_pq | R Documentation |
Graphical representation of ANCOMBC2 result.
plot_ancombc_pq(
physeq,
ancombc_res,
filter_passed = TRUE,
filter_diff = TRUE,
min_abs_lfc = 0,
tax_col = "Genus",
tax_label = "Species",
add_marginal_vioplot = TRUE,
add_label = TRUE,
add_hline_cut_lfc = NULL
)
physeq |
(required): a |
ancombc_res |
(required) the result of the ancombc_pq function For the moment only bimodal factors are possible. |
filter_passed |
(logical, default TRUE) Do we filter using the column passed_ss? The passed_ss value is TRUE if the taxon passed the sensitivity analysis, i.e., adding different pseudo-counts to 0s would not change the results. |
filter_diff |
(logical, default TRUE) Do we filter using the column diff? The diff value is TRUE if the taxon is significant (has q less than alpha) |
min_abs_lfc |
(integer, default 0) Minimum absolute value to filter results based on Log Fold Change. For ex. a value of 1 filter out taxa for which the abundance in a given level of the modalty is not at least the double of the abundance in the other level. |
tax_col |
The taxonomic level (must be present in |
tax_label |
The taxonomic level (must be present in |
add_marginal_vioplot |
(logical, default TRUE) Do we add a marginal vioplot representing all the taxa lfc from ancombc_res. |
add_label |
(logical, default TRUE) Do we add a label? |
add_hline_cut_lfc |
(logical, default NULL) Do we add two horizontal lines when min_abs_lfc is set (different from zero)? |
This function is mainly a wrapper of the work of others.
Please make a reference to ANCOMBC::ancombc2()
if you
use this function.
A ggplot2 object. If add_marginal_vioplot is TRUE, this is a
patchworks of plot made using patchwork::plot_layout()
.
Adrien Taudière
if (requireNamespace("mia")) {
data_fungi_mini@tax_table <- phyloseq::tax_table(cbind(
data_fungi_mini@tax_table,
"taxon" = taxa_names(data_fungi_mini)
))
res_time <- ancombc_pq(
data_fungi_mini,
fact = "Time",
levels_fact = c("0", "15"),
tax_level = "taxon",
verbose = TRUE
)
plot_ancombc_pq(data_fungi_mini, res_time,
filter_passed = FALSE,
tax_label = "Genus", tax_col = "Order"
)
plot_ancombc_pq(data_fungi_mini, res_time, tax_col = "Genus")
plot_ancombc_pq(data_fungi_mini, res_time,
filter_passed = FALSE,
filter_diff = FALSE, tax_col = "Family", add_label = FALSE
)
}
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