reporter_score function

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

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 are feature ids (e.g. K00001 if feature="ko"; PEX11A if feature="gene"; C00024 if feature="compound"), colnames are 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)

Returns

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

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
)

See Also

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