diff_analysis: Differential expression analysis

In xiangpin/MicrobitaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

Differential expression analysis

Description

Differential expression analysis

Usage

diff_analysis(obj, ...)

## S3 method for class 'data.frame'
diff_analysis(
  obj,
  sampleda,
  classgroup,
  subclass = NULL,
  taxda = NULL,
  alltax = TRUE,
  include.rownames = FALSE,
  standard_method = NULL,
  mlfun = "lda",
  ratio = 0.7,
  firstcomfun = "kruskal.test",
  padjust = "fdr",
  filtermod = "pvalue",
  firstalpha = 0.05,
  strictmod = TRUE,
  fcfun = "generalizedFC",
  secondcomfun = "wilcox.test",
  clmin = 5,
  clwilc = TRUE,
  secondalpha = 0.05,
  subclmin = 3,
  subclwilc = TRUE,
  ldascore = 2,
  normalization = 1e+06,
  bootnums = 30,
  ci = 0.95,
  type = "species",
  ...
)

## S3 method for class 'phyloseq'
diff_analysis(obj, ...)

Arguments

obj	object,a phyloseq class contained otu_table, sample_data, taxda, or data.frame, nrow sample * ncol features.
...	additional parameters.
sampleda	data.frame, nrow sample * ncol factor, the sample names of sampleda and data should be the same.
classgroup	character, the factor name in sampleda.
subclass	character, the factor name in sampleda, default is NULL, meaning no subclass compare.
taxda	data.frame, the classification of the feature in data. default is NULL.
alltax	logical, whether to set all classification (taxonomy) as features when taxda is not NULL, default is TRUE.
include.rownames	logical, whether to consider the OTU of obj as (all taxonomy) features, when taxda is not NULL, default is FALSE.
standard_method	character, the method of standardization, see also decostand, default is NULL, it represents that the relative abundance of taxonomy will be used. If count was set, it represents the count reads of taxonomy will be used.
mlfun	character, the method for calculating the effect size of features, choose "lda" or "rf", default is "lda".
ratio	numeric, range from 0 to 1, the proportion of samples for calculating the effect size of features, default is 0.7.
firstcomfun	character, the method for first test, "oneway.test" for normal distributions, suggested choosing "kruskal.test" for uneven distributions, default is "kruskal.test", or you can use lm, glm, or glm.nb (for negative binomial distribution), or 'kruskal_test', 'oneway_test' of 'coin'.
padjust	character, the correction method, default is "fdr".
filtermod	character, the method to filter, default is "pvalue".
firstalpha	numeric, the alpha value for the first test, default is 0.05.
strictmod	logical, whether to performed in one-against-one, default is TRUE (strict).
fcfun	character, default is "generalizedFC", it can't be set another at the present time.
secondcomfun	character, the method for one-against-one, default is "wilcox.test" for uneven distributions, or 'wilcox_test' of 'coin', or you can also use 'lm', 'glm', 'glm.nb'(for negative binomial distribution in 'MASS').
clmin	integer, the minimum number of samples per classgroup for performing test, default is 5.
clwilc	logical, whether to perform test of per classgroup, default is TRUE.
secondalpha	numeric, the alpha value for the second test, default is 0.05.
subclmin	integer, the minimum number of samples per suclass for performing test, default is 3.
subclwilc	logical, whether to perform test of per subclass, default is TRUE, meaning more strict.
ldascore	numeric, the threshold on the absolute value of the logarithmic LDA score, default is 2.
normalization	integer, set the normalization value, set a big number if to get more meaningful values for the LDA score, or you can set NULL for no normalization, default is 1000000.
bootnums	integer, set the number of bootstrap iteration for lda or rf, default is 30.
ci	numeric, the confidence interval of effect size (LDA or MDA), default is 0.95.
type	character, the type of datasets, default is "species", if the dataset is not about species, such as dataset of kegg function, you should set it to "others".

Value

diff_analysis class.

Author(s)

Shuangbin Xu

Examples

## Not run: 
data(kostic2012crc)
kostic2012crc %<>% as.phyloseq()
head(phyloseq::sample_data(kostic2012crc),3)
kostic2012crc <- phyloseq::rarefy_even_depth(kostic2012crc,rngseed=1024)
table(phyloseq::sample_data(kostic2012crc)$DIAGNOSIS)
set.seed(1024)
diffres <- diff_analysis(kostic2012crc, classgroup="DIAGNOSIS",
                        mlfun="lda", filtermod="fdr",
                        firstcomfun = "kruskal.test",
                        firstalpha=0.05, strictmod=TRUE,
                        secondcomfun = "wilcox.test",
                        subclmin=3, subclwilc=TRUE,
                        secondalpha=0.01, ldascore=3)

## End(Not run)

xiangpin/MicrobitaProcess documentation built on Nov. 6, 2024, 1:15 a.m.