mp_diff_analysis-methods: Differential expression analysis for MPSE or tbl_mpse object

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

Differential expression analysis for MPSE or tbl_mpse object

Description

Differential expression analysis for MPSE or tbl_mpse object

Usage

mp_diff_analysis(
  .data,
  .abundance,
  .group,
  .sec.group = NULL,
  action = "add",
  tip.level = "OTU",
  force = FALSE,
  relative = TRUE,
  taxa.class = "all",
  first.test.method = "kruskal.test",
  first.test.alpha = 0.05,
  p.adjust = "fdr",
  filter.p = "fdr",
  strict = TRUE,
  fc.method = "generalizedFC",
  second.test.method = "wilcox.test",
  second.test.alpha = 0.05,
  cl.min = 5,
  cl.test = TRUE,
  subcl.min = 3,
  subcl.test = TRUE,
  ml.method = "lda",
  normalization = 1e+06,
  ldascore = 2,
  bootnums = 30,
  sample.prop.boot = 0.7,
  ci = 0.95,
  seed = 123,
  type = "species",
  ...
)

## S4 method for signature 'MPSE'
mp_diff_analysis(
  .data,
  .abundance,
  .group,
  .sec.group = NULL,
  action = "add",
  tip.level = "OTU",
  force = FALSE,
  relative = TRUE,
  taxa.class = "all",
  first.test.method = "kruskal.test",
  first.test.alpha = 0.05,
  p.adjust = "fdr",
  filter.p = "fdr",
  strict = TRUE,
  fc.method = "generalizedFC",
  second.test.method = "wilcox.test",
  second.test.alpha = 0.05,
  cl.min = 5,
  cl.test = TRUE,
  subcl.min = 3,
  subcl.test = TRUE,
  ml.method = "lda",
  normalization = 1e+06,
  ldascore = 2,
  bootnums = 30,
  sample.prop.boot = 0.7,
  ci = 0.95,
  seed = 123,
  type = "species",
  ...
)

## S4 method for signature 'tbl_mpse'
mp_diff_analysis(
  .data,
  .abundance,
  .group,
  .sec.group = NULL,
  action = "add",
  tip.level = "OTU",
  force = FALSE,
  relative = TRUE,
  taxa.class = "all",
  first.test.method = "kruskal.test",
  first.test.alpha = 0.05,
  p.adjust = "fdr",
  filter.p = "fdr",
  strict = TRUE,
  fc.method = "generalizedFC",
  second.test.method = "wilcox.test",
  second.test.alpha = 0.05,
  cl.min = 5,
  cl.test = TRUE,
  subcl.min = 3,
  subcl.test = TRUE,
  ml.method = "lda",
  normalization = 1e+06,
  ldascore = 2,
  bootnums = 30,
  sample.prop.boot = 0.7,
  ci = 0.95,
  seed = 123,
  type = "species",
  ...
)

## S4 method for signature 'grouped_df_mpse'
mp_diff_analysis(
  .data,
  .abundance,
  .group,
  .sec.group = NULL,
  action = "add",
  tip.level = "OTU",
  force = FALSE,
  relative = TRUE,
  taxa.class = "all",
  first.test.method = "kruskal.test",
  first.test.alpha = 0.05,
  p.adjust = "fdr",
  filter.p = "fdr",
  strict = TRUE,
  fc.method = "generalizedFC",
  second.test.method = "wilcox.test",
  second.test.alpha = 0.05,
  cl.min = 5,
  cl.test = TRUE,
  subcl.min = 3,
  subcl.test = TRUE,
  ml.method = "lda",
  normalization = 1e+06,
  ldascore = 2,
  bootnums = 30,
  sample.prop.boot = 0.7,
  ci = 0.95,
  seed = 123,
  type = "species",
  ...
)

Arguments

.data	MPSE or tbl_mpse object
.abundance	the name of abundance to be calculated
.group	the group name of the samples to be calculated.
.sec.group	the second group name of the samples to be calculated.
action	character, "add" joins the new information to the taxatree (if it exists) or rowData and return MPSE object,"only" return a non-redundant tibble with the result of different analysis. "get" return 'diffAnalysisClass' object.
tip.level	character the taxa level to be as tip level
force	logical whether to calculate the relative abundance forcibly when the abundance is not be rarefied, default is FALSE.
relative	logical whether calculate the relative abundance.
taxa.class	character if taxa class is not 'all', only the specified taxa class will be identified, default is 'all'.
first.test.method	the method for first test, option is "kruskal.test", "oneway.test", "lm", "glm", or "glm.nb", "kruskal_test", "oneway_test" of "coin" package. default is "kruskal.test".
first.test.alpha	numeric the alpha value for the first test, default is 0.05.
p.adjust	character the correction method, default is "fdr", see also p.adjust function default is fdr.
filter.p	character the method to filter pvalue, default is fdr, meanings the features that fdr <= .first.test.alpha will be kept, if it is set to pvalue, meanings the features that pvalue <= .first.test.alpha will be kept.
strict	logical whether to performed in one-against-one when .sec.group is provided, default is TRUE (strict).
fc.method	character the method to check which group has more abundance for the significantly different features, default is "generalizedFC", options are generalizedFC, compare_median, compare_mean.
second.test.method	the method for one-against-one (the second test), default is "wilcox.test" other option is one of 'wilcox_test' of 'coin'; 'glm'; 'glm.nb' of 'MASS'.
second.test.alpha	numeric the alpha value for the second test, default is 0.05.
cl.min	integer the minimum number of samples per group for performing test, default is 5.
cl.test	logical whether to perform test (second test) between the groups (the number of sample of the .group should be also larger that cl.min), default is TRUE.
subcl.min	integer the minimum number of samples in each second groups for performing test, default is 3.
subcl.test	logical whether to perform test for between the second groups (the .sec.group should be provided and the number sample of each .sec.group should be larger than subcl.min, and strict is TRUE), default is TRUE.
ml.method	the method for calculating the effect size of features, option is 'lda' or 'rf'. default is 'lda'.
normalization	integer 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.
ldascore	numeric the threshold on the absolute value of the logarithmic LDA score, default is 2.
bootnums	integer, set the number of bootstrap iteration for lda or rf, default is 30.
sample.prop.boot	numeric range from 0 to 1, the proportion of samples for calculating the effect size of features, default is 0.7.
ci	numeric, the confidence interval of effect size (LDA or MDA), default is 0.95.
seed	a random seed to make the analysis reproducible, default is 123.
type	character type="species" meaning the abundance matrix is from the species abundance, other option is "others", default is "species".
...	additional parameters

Value

update object according to the action argument.

Author(s)

Shuangbin Xu

Examples

data(mouse.time.mpse)
mouse.time.mpse %<>%
  mp_rrarefy() 
mouse.time.mpse
mouse.time.mpse %<>%
  mp_diff_analysis(.abundance=RareAbundance, 
                   .group=time, 
                   first.test.alpha=0.01,
                   action="add") 
library(ggplot2)
p <- mouse.time.mpse %>% mp_plot_diff_res()
p <- p + 
     scale_fill_manual(
       aesthetics = "fill_new", # The fill aes was renamed to "fill_new" for the abundance dotplot layer
       values = c("skyblue", "orange")
     )  + 
     scale_fill_manual(
       values=c("skyblue", "orange") # The LDA barplot layer
     )
### and the fill aes for hight light layer of tree was renamed to 'fill_new_new'
p <- p + 
     scale_fill_manual(
       aesthetics = "fill_new_new",
       values = c("#E41A1C", "#377EB8", "#4DAF4A", 
                  "#984EA3", "#FF7F00", "#FFFF33", 
                  "#A65628", "#F781BF", "#999999")
     )
p
## Not run: 
  ### visualizing the differential taxa with cladogram
  f <- mouse.time.mpse %>% 
       mp_plot_diff_cladogram(
         label.size = 2.5, 
         hilight.alpha = .3, 
         bg.tree.size = .5, 
         bg.point.size = 2, 
         bg.point.stroke = .25
       ) + 
       scale_fill_diff_cladogram(
         values = c('skyblue', 'orange')
       ) +
       scale_size_continuous(range = c(1, 4))
  f

## End(Not run)

xiangpin/MicrobitaProcess documentation built on April 12, 2024, 9:03 p.m.