reporter_score {ReporterScore}

One step to get the reporter score of your KO abundance table.

Description

One step to get the reporter score of your KO abundance table.

Usage

reporter_score(
  kodf,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method1 = "none",
  mode = c("directed", "mixed")[1],
  verbose = TRUE,
  feature = "ko",
  type = c("pathway", "module")[1],
  p.adjust.method2 = "BH",
  modulelist = NULL,
  threads = 1,
  perm = 4999,
  min_exist_KO = 3,
  max_exist_KO = 600
)

Arguments

kodf

KO_abundance table, rowname is ko id (e.g. K00001),colnames is samples.

group

The comparison groups (at least two categories) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of kodf. And you can use factor levels to change order.

metadata

sample information data.frame contains group

method

the type of test. Default is 'wilcox.test'. Allowed values include:

• t.test (parametric) and wilcox.test (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed.

• anova (parametric) and kruskal.test (non-parametric). Perform one-way ANOVA test comparing multiple groups.

• 'pearson', 'kendall', or 'spearman' (correlation), see cor.

pattern

a named vector matching the group, e.g. c('G1'=1,'G2'=3,'G3'=2), use the correlation analysis with specific pattern to calculate p-value.

p.adjust.method1

p.adjust.method for 'ko.test', see p.adjust

mode

'mixed' or 'directed' (default, only for two groups differential analysis or multi-groups correlation analysis.), see details in pvalue2zs.

verbose

logical

feature

one of 'ko', 'gene', 'compound'

type

'pathway' or 'module' for default KOlist for microbiome, 'CC', 'MF', 'BP', 'ALL' for default GOlist for homo sapiens. And org in listed in 'https://www.genome.jp/kegg/catalog/org_list.html' such as 'hsa' (if your kodf is come from a specific organism, you should specify type here).

p.adjust.method2

p.adjust.method for the correction of ReporterScore, see p.adjust

modulelist

NULL or customized modulelist dataframe, must contain 'id','K_num','KOs','Description' columns. Take the 'KOlist' as example, use custom_modulelist.

threads

default 1

perm

permutation number, default: 4999.

min_exist_KO

min exist KO number in a pathway (default, 3, when a pathway contains KOs less than 3, there will be no RS)

max_exist_KO

max exist KO number in a pathway (default, 600, when a pathway contains KOs more than 600, there will be no RS)

Value

reporter_score object:

kodf

your input KO_abundance table

ko_stat

ko statistics result contains p.value and z_score

reporter_s

the reporter score in each pathway

modulelist

default KOlist or customized modulelist dataframe

group

The comparison groups in your data

metadata

sample information dataframe contains group

for the 'reporter_s' in result, whose columns represent:

ID

pathway id

Description

pathway description

K_num

total number of KOs/genes in the pathway

Exist_K_num

number of KOs/genes in your inputdata that exist in the pathway

Significant_K_num

number of kos/genes in your inputdata that are significant in the pathway

Z_score

Z_{pathway}=\frac{1}{\sqrt{k}}\sum Z_{koi}

BG_Mean

Background mean, \mu _k

BG_Sd

Background standard deviation, \sigma _k

ReporterScore

ReporterScore of the pathway, ReporterScore=(Z_{pathway}-\mu _k)/\sigma _k

p.value

p.value of the ReporterScore

p.adjust

adjusted p.value by p.adjust.method2

See Also

Other GRSA: get_reporter_score(), ko.test(), pvalue2zs()

Examples

message("The following example require some time to run:")

data("KO_abundance_test")
reporter_score_res <- reporter_score(KO_abundance, "Group", metadata,
  mode = "directed", perm = 499
)
head(reporter_score_res$reporter_s)
reporter_score_res2 <- reporter_score(KO_abundance, "Group2", metadata,
  mode = "mixed",
  method = "kruskal.test", p.adjust.method1 = "none", perm = 499
)
reporter_score_res3 <- reporter_score(KO_abundance, "Group2", metadata,
  mode = "directed",
  method = "pearson", pattern = c("G1" = 1, "G2" = 3, "G3" = 2), perm = 499
)