cor_rocky_mtn: Create Rocky Mountain plots from taxa relative abundance...

In tidyMicro: A Pipeline for Microbiome Analysis and Visualization

Description

Calculate the correlation between the relative abundance of each taxa within a specified table and a continuous variable of interest. Correlation is calculated by cor. By default, Kendall's correlation is used to account for the prevalence of ties that often occur (lots of 0s)

Usage

cor_rocky_mtn(
  micro_set,
  table,
  x,
  y = clr,
  method = c("pearson", "kendall", "spearman"),
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  subtitle = NULL,
  cut_lines = TRUE,
  line_text = TRUE,
  sig_text = TRUE,
  lwd = 1,
  cor_label = 0.5,
  breaks = c(-0.6, -0.5, -0.3, 0.3, 0.5, 0.6)
)

Arguments

micro_set	A tidy_micro data set
table	OTU table of interest
x	Continuous variable of interest
y	The taxa information. The centered log ratio (clr) is recommended.
method	Correlation type; must be supported by cor. By default it is "spearman" to use with clr. If you'd like to use taxa ra, it is recommend you switch to Kendall's correlation to account for the large number of ties common in taxa ra (lots of 0s)
main	Plot title
xlab	Lable for x-axis
ylab	Label for y-axis
subtitle	Plot subtitle
cut_lines	Add lines for p-value cutoffs
line_text	Label p-value cut-offs
sig_text	Label taxa with correlations greater than cor_label in magnitude
lwd	line width for cut_lines
cor_label	Cutoff for correlations to be labeled
breaks	Where to place cut_lines along y-axis

Value

A ggplot you can add geoms to if you'd like

Author(s)

Charlie Carpenter, Dan Frank

Examples

data(bpd_phy); data(bpd_cla); data(bpd_ord); data(bpd_fam); data(bpd_clin)

otu_tabs <- list(Phylum = bpd_phy, Class = bpd_cla,
Order = bpd_ord, Family = bpd_fam)
set <- tidy_micro(otu_tabs = otu_tabs, clinical = bpd_clin) %>%
filter(day == 7) ## Only including the first week

set %>% cor_rocky_mtn(table = "Family", weight, cor_label = 0.3)

tidyMicro documentation built on Jan. 13, 2021, 6:18 a.m.