MetaLonDA (version 1.1.8)

metalonda: Metagenomic Longitudinal Differential Abundance Analysis for one feature

Description

Find significant time intervals of the one feature

Usage

metalonda(
  Count,
  Time,
  Group,
  ID,
  n.perm = 500,
  fit.method = "nbinomial",
  points,
  text = 0,
  parall = FALSE,
  pvalue.threshold = 0.05,
  adjust.method = "BH",
  time.unit = "days",
  ylabel = "Normalized Count",
  col = c("blue", "firebrick"),
  prefix = "Test"
)

Arguments

Count

matrix has the number of reads that mapped to each feature in each sample.

Time

vector of the time label of each sample.

Group

vector of the group label of each sample.

ID

vector of the subject ID label of each sample.

n.perm

number of permutations.

fit.method

fitting method (nbinomial, lowess).

points

points at which the prediction should happen.

text

Feature's name.

parall

boolean to indicate whether to use multicore.

pvalue.threshold

p-value threshold cutoff for identifing significant time intervals.

adjust.method

multiple testing correction method.

time.unit

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

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")).

prefix

prefix to be used to create directory for the analysis results

Value

returns a list of the significant time intervals for the tested feature.

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)
# }
# NOT RUN {
output.nbinomial = metalonda(Count = metalonda_test_data[1,], Time = Time, Group = Group,
ID = ID, fit.method =  "nbinomial", n.perm = 10, points = points,
text = rownames(metalonda_test_data)[1], parall = FALSE, pvalue.threshold = 0.05, 
adjust.method = "BH", time.unit = "hours", ylabel = "Normalized Count", col = c("black", "green"))
# }