MetaLonDA (version 1.1.8)

metalondaAll: Metagenomic Longitudinal Differential Abundance Analysis for all Features

Description

Identify significant features and their significant time interval

Usage

metalondaAll(
  Count,
  Time,
  Group,
  ID,
  n.perm = 500,
  fit.method = "nbinomial",
  num.intervals = 100,
  parall = FALSE,
  pvalue.threshold = 0.05,
  adjust.method = "BH",
  time.unit = "days",
  norm.method = "none",
  prefix = "Output",
  ylabel = "Normalized Count",
  col = c("blue", "firebrick")
)

Arguments

Count

Count matrix of all features

Time

Time label of all samples

Group

Group label of all samples

ID

individual ID label for samples

n.perm

number of permutations

fit.method

The fitting method (nbinomial, lowess)

num.intervals

The number of time intervals at which metalonda test differential abundance

parall

logic to indicate whether to use multicore

pvalue.threshold

p-value threshold cutoff

adjust.method

Multiple testing correction methods

time.unit

time unit used in the Time vector (hours, days, weeks, months, etc.)

norm.method

normalization method to be used to normalize count matrix (css, tmm, ra, log10, median_ratio)

prefix

prefix for the output figure

ylabel

text to be shown on the y-axis of all generated figures (default: "Normalized Count")

col

two color to be used for the two groups (eg., c("red", "blue")).

Value

Returns a list of the significant features a long with their significant time intervals

References

Ahmed Metwally ([e-mail])

Examples

# NOT RUN {
data(metalonda_test_data)
n.sample = 5
n.timepoints = 10
n.group = 2
Group = factor(c(rep(0, n.sample*n.timepoints), rep(1,n.sample*n.timepoints)))
Time = rep(rep(1:n.timepoints, times = n.sample), 2)
ID = factor(rep(1:(2*n.sample), each = n.timepoints))
points = seq(1, 10, length.out = 10)
output.nbinomial = metalondaAll(Count = metalonda_test_data, Time = Time, Group = Group,
ID = ID, n.perm = 10, fit.method =  "nbinomial", num.intervals = 100, 
parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", 
time.unit = "hours", norm.method = "none", prefix = "Test",  time.unit = "hours", 
ylabel = "Normalized Count", col = c("black", "green"))
# }