Package 'ReporterScore' reference manual

Title:	Generalized Reporter Score-Based Enrichment Analysis for Omics Data
Description:	Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.
Authors:	Chen Peng [aut, cre]
Maintainer:	Chen Peng <[email protected]>
License:	GPL-3
Version:	0.1.8
Built:	2024-09-26 06:21:28 UTC
Source:	CRAN

Test the proper clusters k for c_means

Description

Test the proper clusters k for c_means

C-means cluster

Usage

cm_test_k(otu_group, filter_var, fast = TRUE)

c_means(otu_group, k_num, filter_var)
cm_test_k(otu_group, filter_var, fast = TRUE)

c_means(otu_group, k_num, filter_var)

Arguments

`otu_group`	standardize data
`filter_var`	filter the highest var
`fast`	whether do the gap_stat?
`k_num`	cluster number

Value

ggplot

Examples


if (requireNamespace("e1071") && requireNamespace("factoextra")) {
  data(otutab, package = "pcutils")
  pcutils::hebing(otutab, metadata$Group) -> otu_group
  cm_test_k(otu_group, filter_var = 0.7)
  cm_res <- c_means(otu_group, k_num = 3, filter_var = 0.7)
  plot(cm_res, 0.8)
}

if (requireNamespace("e1071") && requireNamespace("factoextra")) {
  data(otutab, package = "pcutils")
  pcutils::hebing(otutab, metadata$Group) -> otu_group
  cm_test_k(otu_group, filter_var = 0.7)
  cm_res <- c_means(otu_group, k_num = 3, filter_var = 0.7)
  plot(cm_res, 0.8)
}

Combine the results of 'step by step GRSA'

Description

Combine the results of 'step by step GRSA'

Usage

combine_rs_res(kodf, group, metadata, ko_stat, reporter_s, modulelist = NULL)
combine_rs_res(kodf, group, metadata, ko_stat, reporter_s, modulelist = NULL)

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
`ko_stat`	result of `pvalue2zs`
`reporter_s`	result of `get_reporter_score`
`modulelist`	NULL or customized modulelist dataframe, must contain 'id','K_num','KOs','Description' columns. Take the 'KOlist' as example, use `custom_modulelist`.

Value

reporter_score object

Examples


data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")
reporter_s1 <- get_reporter_score(ko_stat, perm = 499)
reporter_res <- combine_rs_res(KO_abundance, "Group", metadata, ko_stat, reporter_s1)

data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")
reporter_s1 <- get_reporter_score(ko_stat, perm = 499)
reporter_res <- combine_rs_res(KO_abundance, "Group", metadata, ko_stat, reporter_s1)

The CPDlist used for enrichment.

Description

an list contains two data.frame named pathway and module.

Format

four columns in each data.frame.

id: "map0010" or "M00001"
K_num: contians how many Compounds in this pathway or module
KOs: Compounds name
Description: the description of this pathway or module

Build a custom modulelist

Description

Build a custom modulelist

Transform a modulelist to a list

Usage

custom_modulelist(pathway2ko, pathway2desc = NULL, verbose = TRUE)

transform_modulelist(mymodulelist, mode = 1)
custom_modulelist(pathway2ko, pathway2desc = NULL, verbose = TRUE)

transform_modulelist(mymodulelist, mode = 1)

Arguments

`pathway2ko`	user input annotation of Pathway to KO mapping, a data.frame of 2 column with pathway and ko.
`pathway2desc`	user input of Pathway TO Description mapping, a data.frame of 2 column with pathway and description.
`verbose`	verbose
`mymodulelist`	mymodulelist
`mode`	1~2

Value

a custom modulelist

modulelist

Examples

mydat <- data.frame(pathway = paste0("PATHWAY", rep(seq_len(2), each = 5)), ko = paste0("K", 1:10))
mymodulelist <- custom_modulelist(mydat)
print(mymodulelist)
transform_modulelist(mymodulelist)
mydat <- data.frame(pathway = paste0("PATHWAY", rep(seq_len(2), each = 5)), ko = paste0("K", 1:10))
mymodulelist <- custom_modulelist(mydat)
print(mymodulelist)
transform_modulelist(mymodulelist)

Custom modulelist from a specific organism

Description

Custom modulelist from a specific organism

Usage

custom_modulelist_from_org(
  org = "hsa",
  feature = "ko",
  gene = "symbol",
  verbose = TRUE
)
custom_modulelist_from_org(
  org = "hsa",
  feature = "ko",
  gene = "symbol",
  verbose = TRUE
)

Arguments

`org`	kegg organism, listed in https://www.genome.jp/kegg/catalog/org_list.html, default, "hsa"
`feature`	one of "ko", "gene", "compound"
`gene`	one of "symbol","id"
`verbose`	logical

Value

modulelist

Examples

hsa_pathway <- custom_modulelist_from_org(org = "hsa", feature = "gene")
hsa_pathway <- custom_modulelist_from_org(org = "hsa", feature = "gene")

Export report score result tables

Description

Export report score result tables

Usage

export_report_table(reporter_res, dir_name, overwrite = FALSE)
export_report_table(reporter_res, dir_name, overwrite = FALSE)

Arguments

`reporter_res`	a reporter_score object or rs_by_cm object
`dir_name`	the directory to save the report tables
`overwrite`	overwrite the existed files or not, default is FALSE.

Value

No return value

Transfer gene symbol table to KO table

Description

You can use 'clusterProfiler::bitr()' to transfer your table from other gene_id to gene_symbol.

Usage

gene2ko(genedf, org = "hsa")
gene2ko(genedf, org = "hsa")

Arguments

`genedf`	,rowname is gene symbol (e.g. PFKM), colnames is samples
`org`	kegg organism, listed in 'https://www.genome.jp/kegg/catalog/org_list.html', default, 'hsa'

Value

kodf

Examples

data("genedf")
KOdf <- gene2ko(genedf, org = "hsa")
data("genedf")
KOdf <- gene2ko(genedf, org = "hsa")

get features in a modulelist

Description

get features in a modulelist

Usage

get_features(map_id = "map00010", ko_stat = NULL, modulelist = NULL)
get_features(map_id = "map00010", ko_stat = NULL, modulelist = NULL)

Arguments

`map_id`	map_id in modulelist
`ko_stat`	NULL or ko_stat result from `pvalue2zs`
`modulelist`	NULL or customized modulelist dataframe, must contain 'id','K_num','KOs','Description' columns. Take the 'KOlist' as example, use `custom_modulelist`.

Value

KOids, or data.frame with these KOids.

Examples

get_features(map_id = "map00010")
get_features(map_id = "map00010")

Calculate reporter score

Description

Calculate reporter score

Usage

get_reporter_score(
  ko_stat,
  type = c("pathway", "module")[1],
  feature = "ko",
  threads = 1,
  modulelist = NULL,
  perm = 4999,
  verbose = TRUE,
  p.adjust.method2 = "BH",
  min_exist_KO = 3,
  max_exist_KO = 600
)
get_reporter_score(
  ko_stat,
  type = c("pathway", "module")[1],
  feature = "ko",
  threads = 1,
  modulelist = NULL,
  perm = 4999,
  verbose = TRUE,
  p.adjust.method2 = "BH",
  min_exist_KO = 3,
  max_exist_KO = 600
)

Arguments

`ko_stat`	ko_stat result from `pvalue2zs`
`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).
`feature`	one of 'ko', 'gene', 'compound'
`threads`	default 1
`modulelist`	NULL or customized modulelist dataframe, must contain 'id','K_num','KOs','Description' columns. Take the 'KOlist' as example, use `custom_modulelist`.
`perm`	permutation number, default: 4999.
`verbose`	logical
`p.adjust.method2`	p.adjust.method for the correction of ReporterScore, see `p.adjust`
`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_res data.frame

Examples


data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")
reporter_s1 <- get_reporter_score(ko_stat, perm = 499)

data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")
reporter_s1 <- get_reporter_score(ko_stat, perm = 499)

The GOlist used for enrichment.

Description

an list contains three data.frame named BP, CC, MF.

Format

four columns in each data.frame.

id: "map0010" or "M00001"
K_num: contians how many Genes in this GO term
KOs: Genes name
Description: the description of this GO term

The KOs abundance table and group table.

Description

The KOs abundance table and group table.

Perform enrichment analysis

Description

This function performs KO enrichment analysis using the 'clusterProfiler' package.

Usage

KO_enrich(
  ko_stat,
  padj_threshold = 0.05,
  logFC_threshold = NULL,
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)

as.enrich_res(gsea_res)
KO_enrich(
  ko_stat,
  padj_threshold = 0.05,
  logFC_threshold = NULL,
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)

as.enrich_res(gsea_res)

Arguments

`ko_stat`	ko_stat dataframe from `ko.test`.
`padj_threshold`	p.adjust threshold to determine whether a feature significant or not. p.adjust < padj_threshold, default: 0.05
`logFC_threshold`	logFC threshold to determine whether a feature significant or not. abs(logFC)>logFC_threshold, default: NULL
`add_mini`	add_mini when calculate the logFC. e.g (10+0.1)/(0+0.1), default 0.05*min(avg_abundance)
`p.adjust.method`	The method used for p-value adjustment (default: "BH").
`type`	"pathway" or "module" for default KOlist_file.
`feature`	one of "ko", "gene", "compound"
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`verbose`	logical
`gsea_res`	gsea_res from KO_gsea

Value

A data frame containing the enrichment results.

enrich_res object

Perform fisher's exact enrichment analysis

Description

Perform fisher's exact enrichment analysis

Usage

KO_fisher(
  ko_stat,
  padj_threshold = 0.05,
  logFC_threshold = NULL,
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)
KO_fisher(
  ko_stat,
  padj_threshold = 0.05,
  logFC_threshold = NULL,
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)

Arguments

`ko_stat`	ko_stat dataframe from `ko.test`.
`padj_threshold`	p.adjust threshold to determine whether a feature significant or not. p.adjust < padj_threshold, default: 0.05
`logFC_threshold`	logFC threshold to determine whether a feature significant or not. abs(logFC)>logFC_threshold, default: NULL
`add_mini`	add_mini when calculate the logFC. e.g (10+0.1)/(0+0.1), default 0.05*min(avg_abundance)
`p.adjust.method`	The method used for p-value adjustment (default: "BH").
`type`	"pathway" or "module" for default KOlist_file.
`feature`	one of "ko", "gene", "compound"
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`verbose`	logical

Value

data.frame

Examples

## use `fisher.test` from the `stats` package.
data("reporter_score_res")
fisher_res <- KO_fisher(reporter_score_res)
## use `fisher.test` from the `stats` package.
data("reporter_score_res")
fisher_res <- KO_fisher(reporter_score_res)

Perform gene set analysis

Description

Perform gene set analysis

Usage

KO_gsa(
  reporter_res,
  method = "Two class unpaired",
  p.adjust.method = "BH",
  verbose = TRUE,
  perm = 1000,
  ...
)
KO_gsa(
  reporter_res,
  method = "Two class unpaired",
  p.adjust.method = "BH",
  verbose = TRUE,
  perm = 1000,
  ...
)

Arguments

`reporter_res`	reporter_res
`method`	Problem type: "quantitative" for a continuous parameter; "Two class unpaired" ; "Survival" for censored survival outcome; "Multiclass" : more than 2 groups, coded 1,2,3...; "Two class paired" for paired outcomes, coded -1,1 (first pair), -2,2 (second pair), etc
`p.adjust.method`	"BH"
`verbose`	TRUE
`perm`	1000
`...`	additional parameters to `GSA`

Value

enrich_res object

Examples


## use `GSA` from the `GSA` package.
if (requireNamespace("GSA")) {
  data("reporter_score_res")
  gsa_res <- KO_gsa(reporter_score_res, p.adjust.method = "none", perm = 200)
  plot(gsa_res)
}

## use `GSA` from the `GSA` package.
if (requireNamespace("GSA")) {
  data("reporter_score_res")
  gsa_res <- KO_gsa(reporter_score_res, p.adjust.method = "none", perm = 200)
  plot(gsa_res)
}

Perform gene set enrichment analysis

Description

Perform gene set enrichment analysis

Usage

KO_gsea(
  ko_stat,
  weight = "logFC",
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)
KO_gsea(
  ko_stat,
  weight = "logFC",
  add_mini = NULL,
  p.adjust.method = "BH",
  type = c("pathway", "module")[1],
  feature = "ko",
  modulelist = NULL,
  verbose = TRUE
)

Arguments

`ko_stat`	ko_stat dataframe from `ko.test`.
`weight`	the metric used for ranking, default: logFC
`add_mini`	add_mini when calculate the logFC. e.g (10+0.1)/(0+0.1), default 0.05*min(avg_abundance)
`p.adjust.method`	The method used for p-value adjustment (default: "BH").
`type`	"pathway" or "module" for default KOlist_file.
`feature`	one of "ko", "gene", "compound"
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`verbose`	logical

Value

DOSE object

Examples

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

## use `GSEA` from the `clusterProfiler` package.
if (requireNamespace("clusterProfiler")) {
  data("reporter_score_res")
  gsea_res <- KO_gsea(reporter_score_res, p.adjust.method = "none")
  enrichplot::gseaplot(gsea_res, geneSetID = data.frame(gsea_res)$ID[1])
  gsea_res_df <- as.enrich_res(gsea_res)
  plot(gsea_res_df)
}

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

## use `GSEA` from the `clusterProfiler` package.
if (requireNamespace("clusterProfiler")) {
  data("reporter_score_res")
  gsea_res <- KO_gsea(reporter_score_res, p.adjust.method = "none")
  enrichplot::gseaplot(gsea_res, geneSetID = data.frame(gsea_res)$ID[1])
  gsea_res_df <- as.enrich_res(gsea_res)
  plot(gsea_res_df)
}

Perform Gene Set Variation Analysis

Description

Perform Gene Set Variation Analysis

Usage

KO_gsva(
  reporter_res,
  verbose = TRUE,
  method = "wilcox.test",
  p.adjust.method = "BH",
  ...
)
KO_gsva(
  reporter_res,
  verbose = TRUE,
  method = "wilcox.test",
  p.adjust.method = "BH",
  ...
)

Arguments

`reporter_res`	reporter_res
`verbose`	verbose
`method`	see `ko.test`
`p.adjust.method`	p.adjust.method
`...`	additional parameters to `gsva`

Value

enrich_res

Examples


## use `gsva` from the `GSVA` package.
if (requireNamespace("GSVA")) {
  data("reporter_score_res")
  gsva_res <- KO_gsva(reporter_score_res, p.adjust.method = "none")
}

## use `gsva` from the `GSVA` package.
if (requireNamespace("GSVA")) {
  data("reporter_score_res")
  gsva_res <- KO_gsva(reporter_score_res, p.adjust.method = "none")
}

Perform Pathway Analysis with Down-weighting of Overlapping Genes (PADOG)

Description

Perform Pathway Analysis with Down-weighting of Overlapping Genes (PADOG)

Usage

KO_padog(
  reporter_res,
  verbose = TRUE,
  perm = 1000,
  p.adjust.method = "BH",
  ...
)
KO_padog(
  reporter_res,
  verbose = TRUE,
  perm = 1000,
  p.adjust.method = "BH",
  ...
)

Arguments

`reporter_res`	The input reporter result.
`verbose`	If TRUE, print verbose messages. Default is TRUE.
`perm`	The number of permutations. Default is 1000.
`p.adjust.method`	Method for p-value adjustment. Default is "BH".
`...`	Additional parameters to be passed to `padog` function.

Value

A data frame containing PADOG results for KO enrichment.

A data frame with columns "ID," "Description," "K_num," "Exist_K_num," "p.value," and "p.adjust."

Examples


## use `PADOG` from the `PADOG` package.
if (requireNamespace("PADOG")) {
  data("reporter_score_res")
  padog_res <- KO_padog(reporter_score_res,
    verbose = TRUE,
    perm = 200, p.adjust.method = "none"
  )
}

## use `PADOG` from the `PADOG` package.
if (requireNamespace("PADOG")) {
  data("reporter_score_res")
  padog_res <- KO_padog(reporter_score_res,
    verbose = TRUE,
    perm = 200, p.adjust.method = "none"
  )
}

Perform Significance Analysis of Function and Expression

Description

Perform Significance Analysis of Function and Expression

Usage

KO_safe(
  reporter_res,
  verbose = TRUE,
  perm = 1000,
  C.matrix = NULL,
  p.adjust.method = "BH",
  ...
)
KO_safe(
  reporter_res,
  verbose = TRUE,
  perm = 1000,
  C.matrix = NULL,
  p.adjust.method = "BH",
  ...
)

Arguments

`reporter_res`	The input reporter result.
`verbose`	If TRUE, print verbose messages. Default is TRUE.
`perm`	The number of permutations. Default is 1000.
`C.matrix`	The contrast matrix. Default is NULL, and it will be generated from the module list.
`p.adjust.method`	Method for p-value adjustment. Default is "BH".
`...`	Additional parameters to be passed to `safe` function.

Value

A data frame containing SAFE results for KO enrichment.

Examples


## use `safe` from the `safe` package.
if (requireNamespace("safe")) {
  data("reporter_score_res")
  safe_res <- KO_safe(reporter_score_res,
    verbose = TRUE,
    perm = 200, p.adjust.method = "none"
  )
}

## use `safe` from the `safe` package.
if (requireNamespace("safe")) {
  data("reporter_score_res")
  safe_res <- KO_safe(reporter_score_res,
    verbose = TRUE,
    perm = 200, p.adjust.method = "none"
  )
}

Perform Simultaneous Enrichment Analysis

Description

Perform Simultaneous Enrichment Analysis

Usage

KO_sea(reporter_res, verbose = TRUE, ...)
KO_sea(reporter_res, verbose = TRUE, ...)

Arguments

`reporter_res`	The input reporter result.
`verbose`	If TRUE, print verbose messages. Default is TRUE.
`...`	Additional parameters to be passed to `SEA` function.

Value

enrich_res

Examples


## use `SEA` from the `rSEA` package.
if (requireNamespace("rSEA")) {
  data("reporter_score_res")
  sea_res <- KO_sea(reporter_score_res, verbose = TRUE)
}

## use `SEA` from the `rSEA` package.
if (requireNamespace("rSEA")) {
  data("reporter_score_res")
  sea_res <- KO_sea(reporter_score_res, verbose = TRUE)
}

Differential analysis or Correlation analysis for KO-abundance table

Description

Differential analysis or Correlation analysis for KO-abundance table

Usage

ko.test(
  kodf,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method1 = "none",
  threads = 1,
  verbose = TRUE
)
ko.test(
  kodf,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method1 = "none",
  threads = 1,
  verbose = TRUE
)

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`
`threads`	default 1
`verbose`	logical

Value

ko_pvalue data.frame

Examples


data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)

data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)

The KOlist used for enrichment.

Description

an list contains two data.frame named pathway and module.

Format

four columns in each data.frame.

id: "map0010" or "M00001"
K_num: contians how many KOs in this pathway or module
KOs: KOs name
Description: the description of this pathway or module

Load the CARDinfo (from CARD database)

Description

Load the CARDinfo (from CARD database)

Usage

load_CARDinfo(verbose = TRUE)
load_CARDinfo(verbose = TRUE)

Arguments

verbose

logical

Value

CARDinfo

Load the GOlist (from 'GO' database)

Description

Load the GOlist (from 'GO' database)

Load the GOinfo (from GO)

Usage

load_GOlist(verbose = TRUE)

load_GOinfo(verbose = TRUE)
load_GOlist(verbose = TRUE)

load_GOinfo(verbose = TRUE)

Arguments

verbose

logical

Value

GOlist

GOinfo

Load the specific table (from 'KEGG')

Description

Load the specific table (from 'KEGG')

Load the KOlist (from 'KEGG')

Load the CPDlist (from 'KEGG')

Load the KO description (from 'KEGG')

Load the KO_htable (from 'KEGG')

Load the Pathway_htable (from 'KEGG')

Load the Module_htable (from 'KEGG')

Load the Compound_htable (from 'KEGG')

Load the pathway information for an organism (from 'KEGG')

Usage

load_htable(type, verbose = TRUE)

load_KOlist(verbose = TRUE)

load_CPDlist(verbose = TRUE)

load_KO_desc(verbose = TRUE)

load_KO_htable(verbose = TRUE)

load_Pathway_htable(verbose = TRUE)

load_Module_htable(verbose = TRUE)

load_Compound_htable(verbose = TRUE)

load_org_pathway(org = "hsa", verbose = TRUE)
load_htable(type, verbose = TRUE)

load_KOlist(verbose = TRUE)

load_CPDlist(verbose = TRUE)

load_KO_desc(verbose = TRUE)

load_KO_htable(verbose = TRUE)

load_Pathway_htable(verbose = TRUE)

load_Module_htable(verbose = TRUE)

load_Compound_htable(verbose = TRUE)

load_org_pathway(org = "hsa", verbose = TRUE)

Arguments

`type`	"ko", "module", "pathway", "compound" ...
`verbose`	logical
`org`	kegg organism, listed in https://www.genome.jp/kegg/catalog/org_list.html, default, "hsa"

Value

KO_htable

KOlist

CPDlist

KO description

KO_htable

Pathway_htable

Module_htable

Compound_htable

KOlist

Examples

Pathway_htable <- load_htable("pathway")
head(Pathway_htable)
Pathway_htable <- load_htable("pathway")
head(Pathway_htable)

Modify the pathway description before plotting

Description

Modify the pathway description before plotting

Usage

modify_description(
  reporter_res,
  pattern = " - Homo sapiens (human)",
  replacement = ""
)
modify_description(
  reporter_res,
  pattern = " - Homo sapiens (human)",
  replacement = ""
)

Arguments

`reporter_res`	reporter_res
`pattern`	str, like " - Homo sapiens (human)"
`replacement`	str, like ""

Value

reporter_res

Examples

data("reporter_score_res")
modify_description(reporter_score_res, pattern = " - Homo sapiens (human)")
data("reporter_score_res")
modify_description(reporter_score_res, pattern = " - Homo sapiens (human)")

Plot features boxplot

Description

Plot features boxplot

Usage

plot_features_box(
  kodf,
  group = NULL,
  metadata = NULL,
  map_id = "map00780",
  select_ko = NULL,
  only_sig = FALSE,
  box_param = NULL,
  modulelist = NULL,
  KO_description = FALSE,
  str_width = 50
)
plot_features_box(
  kodf,
  group = NULL,
  metadata = NULL,
  map_id = "map00780",
  select_ko = NULL,
  only_sig = FALSE,
  box_param = NULL,
  modulelist = NULL,
  KO_description = FALSE,
  str_width = 50
)

Arguments

`kodf`	KO_abundance table, rowname is ko id (e.g. K00001),colnames is samples. or result of 'get_reporter_score'
`group`	The compare group (two category) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of kodf.
`metadata`	metadata
`map_id`	the pathway or module id
`select_ko`	select which ko
`only_sig`	only show the significant features
`box_param`	parameters pass to `group_box`
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`KO_description`	show KO description rather than KO id.
`str_width`	str_width to wrap

Value

ggplot

Examples


data("reporter_score_res")
plot_features_box(reporter_score_res,
  select_ko = c("K00059", "K00208", "K00647", "K00652", "K00833", "K01012"),
  box_param = list(p_value1 = FALSE, trend_line = TRUE)
)
plot_features_box(reporter_score_res,
  select_ko = "K00059", KO_description = TRUE,
  box_param = list(p_value1 = FALSE, trend_line = TRUE)
)

data("reporter_score_res")
plot_features_box(reporter_score_res,
  select_ko = c("K00059", "K00208", "K00647", "K00652", "K00833", "K01012"),
  box_param = list(p_value1 = FALSE, trend_line = TRUE)
)
plot_features_box(reporter_score_res,
  select_ko = "K00059", KO_description = TRUE,
  box_param = list(p_value1 = FALSE, trend_line = TRUE)
)

plot the Z-score of features distribution

Description

plot the Z-score of features distribution

Usage

plot_features_distribution(
  reporter_res,
  map_id,
  text_size = 4,
  text_position = NULL,
  rug_length = 0.04
)
plot_features_distribution(
  reporter_res,
  map_id,
  text_size = 4,
  text_position = NULL,
  rug_length = 0.04
)

Arguments

`reporter_res`	result of 'reporter_score'
`map_id`	the pathway or module id
`text_size`	text_size=4
`text_position`	text_position, e.g. c(x=3,y=0.4)
`rug_length`	rug_length=0.04

Value

ggplot

Examples


data("reporter_score_res")
plot_features_distribution(reporter_score_res, map_id = c("map05230", "map03010"))

data("reporter_score_res")
plot_features_distribution(reporter_score_res, map_id = c("map05230", "map03010"))

Plot features heatmap

Description

Plot features heatmap

Usage

plot_features_heatmap(
  kodf,
  group = NULL,
  metadata = NULL,
  map_id = "map00780",
  select_ko = NULL,
  only_sig = FALSE,
  columns = NULL,
  modulelist = NULL,
  KO_description = FALSE,
  str_width = 50,
  heatmap_param = list()
)
plot_features_heatmap(
  kodf,
  group = NULL,
  metadata = NULL,
  map_id = "map00780",
  select_ko = NULL,
  only_sig = FALSE,
  columns = NULL,
  modulelist = NULL,
  KO_description = FALSE,
  str_width = 50,
  heatmap_param = list()
)

Arguments

`kodf`	KO_abundance table, rowname is ko id (e.g. K00001),colnames is samples. or result of 'get_reporter_score'
`group`	The compare group (two category) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of kodf.
`metadata`	metadata
`map_id`	the pathway or module id
`select_ko`	select which ko
`only_sig`	only show the significant KOs
`columns`	change columns
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`KO_description`	show KO description rather than KO id.
`str_width`	str_width to wrap
`heatmap_param`	parameters pass to `pheatmap`

Value

ggplot

Examples


if (requireNamespace("pheatmap")) {
  data("reporter_score_res")
  plot_features_heatmap(reporter_score_res, map_id = "map00780")
}

if (requireNamespace("pheatmap")) {
  data("reporter_score_res")
  plot_features_heatmap(reporter_score_res, map_id = "map00780")
}

Plot features trend in one pathway or module

Description

Plot features trend in one pathway or module

Usage

plot_features_in_pathway(
  ko_stat,
  map_id = "map00780",
  modulelist = NULL,
  select_ko = NULL,
  box_color = reporter_color,
  show_number = TRUE,
  scale = FALSE,
  feature_type = "KOs",
  line_color = c(Depleted = "seagreen", Enriched = "orange", None = "grey", Significant =
    "red2")
)
plot_features_in_pathway(
  ko_stat,
  map_id = "map00780",
  modulelist = NULL,
  select_ko = NULL,
  box_color = reporter_color,
  show_number = TRUE,
  scale = FALSE,
  feature_type = "KOs",
  line_color = c(Depleted = "seagreen", Enriched = "orange", None = "grey", Significant =
    "red2")
)

Arguments

`ko_stat`	ko_stat result from `pvalue2zs` or result of 'get_reporter_score'
`map_id`	the pathway or module id
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`select_ko`	select which ko
`box_color`	box and point color, default: c("#e31a1c","#1f78b4")
`show_number`	show the numbers.
`scale`	scale the data by row.
`feature_type`	show in the title ,default: KOs
`line_color`	line color, default: c("Depleted"="seagreen","Enriched"="orange","None"="grey")

Value

ggplot

Examples


data("reporter_score_res")
plot_features_in_pathway(ko_stat = reporter_score_res, map_id = "map00860")

data("reporter_score_res")
plot_features_in_pathway(ko_stat = reporter_score_res, map_id = "map00860")

Plot features network

Description

Plot features network

Usage

plot_features_network(
  ko_stat,
  map_id = "map00780",
  near_pathway = FALSE,
  modulelist = NULL,
  kos_color = c(Depleted = "seagreen", Enriched = "orange", None = "grey", Significant =
    "red2", Pathway = "#80b1d3"),
  pathway_label = TRUE,
  kos_label = TRUE,
  pathway_description = FALSE,
  kos_description = FALSE,
  str_width = 50,
  mark_module = FALSE,
  mark_color = NULL,
  return_net = FALSE,
  ...
)
plot_features_network(
  ko_stat,
  map_id = "map00780",
  near_pathway = FALSE,
  modulelist = NULL,
  kos_color = c(Depleted = "seagreen", Enriched = "orange", None = "grey", Significant =
    "red2", Pathway = "#80b1d3"),
  pathway_label = TRUE,
  kos_label = TRUE,
  pathway_description = FALSE,
  kos_description = FALSE,
  str_width = 50,
  mark_module = FALSE,
  mark_color = NULL,
  return_net = FALSE,
  ...
)

Arguments

`ko_stat`	ko_stat result from `pvalue2zs` or result of 'get_reporter_score'
`map_id`	the pathway or module id
`near_pathway`	show the near_pathway if any features exist.
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`kos_color`	default, c("Depleted"="seagreen","Enriched"="orange","None"="grey","Significant"="red2")
`pathway_label`	show pathway_label?
`kos_label`	show kos_label?
`pathway_description`	show the pathway description?
`kos_description`	show the kos description?
`str_width`	str width
`mark_module`	mark the modules?
`mark_color`	mark colors, default, c("Depleted"="seagreen","Enriched"="orange","None"="grey","Significant"="red2")
`return_net`	return the network
`...`	additional arguments for `c_net_plot`

Value

network plot

Examples

if (requireNamespace("MetaNet")) {
  data("reporter_score_res")
  plot_features_network(reporter_score_res, map_id = "map05230")
  plot_features_network(reporter_score_res, map_id = "map00780", near_pathway = TRUE)
}
if (requireNamespace("MetaNet")) {
  data("reporter_score_res")
  plot_features_network(reporter_score_res, map_id = "map05230")
  plot_features_network(reporter_score_res, map_id = "map00780", near_pathway = TRUE)
}

Plot htable levels

Description

Plot htable levels

Usage

plot_htable(type = "ko", select = NULL, htable = NULL)
plot_htable(type = "ko", select = NULL, htable = NULL)

Arguments

`type`	"ko", "module", "pathway", "compound"
`select`	select ids
`htable`	custom a htable

Value

ggplot

Examples


data("KO_abundance_test")
plot_htable(select = rownames(KO_abundance))

data("KO_abundance_test")
plot_htable(select = rownames(KO_abundance))

plot_KEGG_map

Description

plot_KEGG_map

Usage

plot_KEGG_map(
  ko_stat,
  map_id = "map00780",
  modulelist = NULL,
  type = "pathway",
  feature = "ko",
  color_var = "Z_score",
  save_dir,
  color = c("seagreen", "grey", "orange")
)
plot_KEGG_map(
  ko_stat,
  map_id = "map00780",
  modulelist = NULL,
  type = "pathway",
  feature = "ko",
  color_var = "Z_score",
  save_dir,
  color = c("seagreen", "grey", "orange")
)

Arguments

`ko_stat`	ko_stat result from `pvalue2zs` or result of 'get_reporter_score'
`map_id`	the pathway or module id
`modulelist`	NULL or customized modulelist dataframe, must contain "id","K_num","KOs","Description" columns. Take the 'KOlist' as example, use `custom_modulelist`.
`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).
`feature`	one of "ko", "gene", "compound"
`color_var`	use which variable to color
`save_dir`	where to save the png files
`color`	color

Value

png files

References

https://zhuanlan.zhihu.com/p/357687076

Examples

message("The following example will download some files, run yourself:")

if (requireNamespace("pathview")) {
  output_dir <- tempdir()
  data("reporter_score_res")
  plot_KEGG_map(reporter_score_res$ko_stat,
    map_id = "map00780", type = "pathway",
    feature = "ko", color_var = "Z_score", save_dir = output_dir
  )
}

message("The following example will download some files, run yourself:")

if (requireNamespace("pathview")) {
  output_dir <- tempdir()
  data("reporter_score_res")
  plot_KEGG_map(reporter_score_res$ko_stat,
    map_id = "map00780", type = "pathway",
    feature = "ko", color_var = "Z_score", save_dir = output_dir
  )
}

Plot the reporter_res

Description

Plot the reporter_res

Usage

plot_report(
  reporter_res,
  rs_threshold = 1.64,
  mode = 1,
  y_text_size = 13,
  str_width = 100,
  show_ID = FALSE,
  Pathway_description = TRUE,
  facet_level = FALSE,
  facet_anno = NULL,
  facet_str_width = 15
)
plot_report(
  reporter_res,
  rs_threshold = 1.64,
  mode = 1,
  y_text_size = 13,
  str_width = 100,
  show_ID = FALSE,
  Pathway_description = TRUE,
  facet_level = FALSE,
  facet_anno = NULL,
  facet_str_width = 15
)

Arguments

`reporter_res`	result of 'get_reporter_score' or 'reporter_score'
`rs_threshold`	plot threshold vector, default:1.64
`mode`	1~2 plot style.
`y_text_size`	y_text_size
`str_width`	str_width to wrap
`show_ID`	show pathway id
`Pathway_description`	show KO description rather than KO id.
`facet_level`	facet plot if the type is "pathway" or "module"
`facet_anno`	annotation table for facet, two columns, first is level summary, second is pathway id.
`facet_str_width`	str width for facet label

Value

ggplot

Examples

data("reporter_score_res")
plot_report(reporter_score_res, rs_threshold = c(2.5, -2.5), y_text_size = 10, str_width = 40)
data("reporter_score_res")
plot_report(reporter_score_res, rs_threshold = c(2.5, -2.5), y_text_size = 10, str_width = 40)

Plot the reporter_res as circle_packing

Description

Plot the reporter_res as circle_packing

Usage

plot_report_circle_packing(
  reporter_res,
  rs_threshold = 1.64,
  mode = 2,
  facet_anno = NULL,
  show_ID = FALSE,
  Pathway_description = TRUE,
  str_width = 10,
  show_level_name = "all",
  show_tip_label = TRUE
)
plot_report_circle_packing(
  reporter_res,
  rs_threshold = 1.64,
  mode = 2,
  facet_anno = NULL,
  show_ID = FALSE,
  Pathway_description = TRUE,
  str_width = 10,
  show_level_name = "all",
  show_tip_label = TRUE
)

Arguments

`reporter_res`	result of 'get_reporter_score'
`rs_threshold`	plot threshold vector, default:1.64
`mode`	1~2 plot style.
`facet_anno`	annotation table for facet, more two columns, last is pathway name, last second is pathway id.
`show_ID`	show pathway id
`Pathway_description`	show KO description rather than KO id.
`str_width`	str_width to wrap
`show_level_name`	show the level name?
`show_tip_label`	show the tip label?

Value

ggplot

Examples


data("reporter_score_res")
if (requireNamespace("igraph") && requireNamespace("ggraph")) {
  plot_report_circle_packing(reporter_score_res, rs_threshold = c(2, -2), str_width = 40)
}

data("reporter_score_res")
if (requireNamespace("igraph") && requireNamespace("ggraph")) {
  plot_report_circle_packing(reporter_score_res, rs_threshold = c(2, -2), str_width = 40)
}

Plot the significance of pathway

Description

Plot the significance of pathway

Usage

plot_significance(reporter_res, map_id)
plot_significance(reporter_res, map_id)

Arguments

`reporter_res`	result of 'get_reporter_score' or 'reporter_score'
`map_id`	the pathway or module id

Value

ggplot

Examples


data("reporter_score_res")
plot_significance(reporter_score_res, map_id = c("map05230", "map03010"))

data("reporter_score_res")
plot_significance(reporter_score_res, map_id = c("map05230", "map03010"))

Transfer p-value of KOs to Z-score

Description

Transfer p-value of KOs to Z-score

Usage

pvalue2zs(
  ko_pvalue,
  mode = c("directed", "mixed")[1],
  p.adjust.method1 = "none"
)
pvalue2zs(
  ko_pvalue,
  mode = c("directed", "mixed")[1],
  p.adjust.method1 = "none"
)

Arguments

`ko_pvalue`	data.frame from `ko.test`, 'KO_id' and 'p.value' columns are required.
`mode`	'mixed' or 'directed' (default, only for two groups differential analysis or multi-groups correlation analysis.), see details in `pvalue2zs`.
`p.adjust.method1`	p.adjust.method for 'ko.test', see `p.adjust`

Details

'mixed' mode is the original reporter-score method from Patil, K. R. et al. PNAS 2005. In this mode, the reporter score is undirected, and the larger the reporter score, the more significant the enrichment, but it cannot indicate the up-and-down regulation information of the pathway! (Liu, L. et al. iMeta 2023.)

steps:

1. Use the Wilcoxon rank sum test to obtain the P value of the significance of each KO difference between the two groups (ie $P_{koi}$ , i represents a certain KO);

2. Using an inverse normal distribution, convert the P value of each KO into a Z value ( $Z_{koi}$ ), the formula:

$Z_{koi}=\theta ^{-1}(1-P_{koi})$

3. 'Upgrade' KO to pathway: $Z_{koi}$ , calculate the Z value of the pathway, the formula:

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

where k means A total of k KOs were annotated to the corresponding pathway;

4. Evaluate the degree of significance: permutation (permutation) 1000 times, get the random distribution of $Z_{pathway}$ , the formula:

$Z_{adjustedpathway}=(Z_{pathway}-\mu _k)/\sigma _k$

$\mu _k$ is The mean of the random distribution, $\sigma _k$ is the standard deviation of the random distribution.

Instead, 'directed' mode is a derived version of 'mixed', referenced from https://github.com/wangpeng407/ReporterScore.

This approach is based on the same assumption of many differential analysis methods: the expression of most genes has no significant change.

steps:

1. Use the Wilcoxon rank sum test to obtain the P value of the significance of each KO difference between the two groups (ie $P_{koi}$ , i represents a certain KO), and then divide the P value by 2, that is, the range of (0,1] becomes (0,0.5], $P_{koi}=P_{koi}/2$ ;

2. Using an inverse normal distribution, convert the P value of each KO into a Z value ( $Z_{koi}$ ), the formula:

$Z_{koi}=\theta ^{-1}(1-P_{koi})$

since the above P value is less than 0.5, all Z values will be greater than 0;

3. Considering whether each KO is up-regulated or down-regulated, calculate $diff\_KO$ ,

$Z_{koi}=-Z_{koi}\ \ \ \ (diff\_KO<0)$ ,

so $Z_{koi}$ is greater than 0 Up-regulation, $Z_{koi}$ less than 0 is down-regulation;

4. 'Upgrade' KO to pathway: $Z_{koi}$ , calculate the Z value of the pathway, the formula:

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

where k means A total of k KOs were annotated to the corresponding pathway;

5. Evaluate the degree of significance: permutation (permutation) 1000 times, get the random distribution of $Z_{pathway}$ , the formula:

$Z_{adjustedpathway}=(Z_{pathway}-\mu _k)/\sigma _k$

$\mu _k$ is The mean of the random distribution, $\sigma _k$ is the standard deviation of the random distribution.

The finally obtained $Z_{adjustedpathway}$ is the Reporter score value enriched for each pathway. In this mode, the Reporter score is directed, and a larger positive value represents a significant up-regulation enrichment, and a smaller negative values represent significant down-regulation enrichment.

However, the disadvantage of this mode is that when a pathway contains about the same number of significantly up-regulates KOs and significantly down-regulates KOs, the final absolute value of Reporter score may approach 0, becoming a pathway that has not been significantly enriched.

Value

ko_stat data.frame

References

1. Patil, K. R. & Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci U S A 102, 2685–2689 (2005). 2. Liu, L., Zhu, R. & Wu, D. Misuse of reporter score in microbial enrichment analysis. iMeta n/a, e95. 3. https://github.com/wangpeng407/ReporterScore

Examples


data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")

data("KO_abundance_test")
ko_pvalue <- ko.test(KO_abundance, "Group", metadata)
ko_stat <- pvalue2zs(ko_pvalue, mode = "directed")

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
)
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)

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

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
)

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
)

'reporter_score()' result from KO_abundance_test

Description

'reporter_score()' result from KO_abundance_test

Format

a list contain 7 elements.

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 compare group (two category) in your data
metadata: sample information dataframe contains group

Reporter score analysis after C-means clustering

Description

Reporter score analysis after C-means clustering

Extract one cluster from rs_by_cm object

Plot c_means result

Usage

RSA_by_cm(
  kodf,
  group,
  metadata = NULL,
  k_num = NULL,
  filter_var = 0.7,
  verbose = TRUE,
  method = "pearson",
  ...
)

extract_cluster(rsa_cm_res, cluster = 1)

plot_c_means(
  rsa_cm_res,
  filter_membership,
  mode = 1,
  show.clust.cent = TRUE,
  show_num = TRUE,
  ...
)
RSA_by_cm(
  kodf,
  group,
  metadata = NULL,
  k_num = NULL,
  filter_var = 0.7,
  verbose = TRUE,
  method = "pearson",
  ...
)

extract_cluster(rsa_cm_res, cluster = 1)

plot_c_means(
  rsa_cm_res,
  filter_membership,
  mode = 1,
  show.clust.cent = TRUE,
  show_num = TRUE,
  ...
)

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
`k_num`	if NULL, perform the cm_test_k, else an integer
`filter_var`	see c_means
`verbose`	verbose
`method`	method from `reporter_score`
`...`	additional
`rsa_cm_res`	a cm_res object
`cluster`	integer
`filter_membership`	filter membership 0~1.
`mode`	1~2
`show.clust.cent`	show cluster center?
`show_num`	show number of each cluster?

Value

rs_by_cm

reporter_score object

ggplot

Examples

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

if (requireNamespace("e1071") && requireNamespace("factoextra")) {
  data("KO_abundance_test")
  rsa_cm_res <- RSA_by_cm(KO_abundance, "Group2", metadata,
    k_num = 3,
    filter_var = 0.7, method = "pearson", perm = 199
  )
  extract_cluster(rsa_cm_res, cluster = 1)
}

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

if (requireNamespace("e1071") && requireNamespace("factoextra")) {
  data("KO_abundance_test")
  rsa_cm_res <- RSA_by_cm(KO_abundance, "Group2", metadata,
    k_num = 3,
    filter_var = 0.7, method = "pearson", perm = 199
  )
  extract_cluster(rsa_cm_res, cluster = 1)
}

Upgrade the KO level

Description

Upgrade the KO level

Usage

up_level_KO(
  KO_abundance,
  level = "pathway",
  show_name = FALSE,
  modulelist = NULL,
  verbose = TRUE
)
up_level_KO(
  KO_abundance,
  level = "pathway",
  show_name = FALSE,
  modulelist = NULL,
  verbose = TRUE
)

Arguments

`KO_abundance`	KO_abundance
`level`	one of 'pathway', 'module', 'level1', 'level2', 'level3', 'module1', 'module2', 'module3'.
`show_name`	logical
`modulelist`	NULL or customized modulelist dataframe, must contain 'id','K_num','KOs','Description' columns. Take the 'KOlist' as example, use `custom_modulelist`.
`verbose`	logical

Value

data.frame

Examples

data("KO_abundance_test")
KO_level1 <- up_level_KO(KO_abundance, level = "level1", show_name = TRUE)
data("KO_abundance_test")
KO_level1 <- up_level_KO(KO_abundance, level = "level1", show_name = TRUE)

update CARDinfo from (from 'CARD' database)

Description

update CARDinfo from (from 'CARD' database)

Usage

update_CARDinfo(download_dir = NULL, card_data = NULL)
update_CARDinfo(download_dir = NULL, card_data = NULL)

Arguments

`download_dir`	download_dir
`card_data`	card_data from https://card.mcmaster.ca/download/0/broadstreet-v3.2.8.tar.bz2

Value

No value

Update the GO2gene files (from 'GO' database)

Description

Download links: http://geneontology.org/docs/download-ontology/ https://asa12138.github.io/FileList/GOlist.rda

Usage

update_GOlist(download_dir = NULL, GO_file = NULL)

update_GOinfo(download_dir = NULL, obo_file = NULL)
update_GOlist(download_dir = NULL, GO_file = NULL)

update_GOinfo(download_dir = NULL, obo_file = NULL)

Arguments

`download_dir`	download_dir
`GO_file`	GO_file
`obo_file`	obo_file from http://current.geneontology.org/ontology/go.obo

Value

No value

Update files from 'KEGG'

Description

Download links:

https://rest.kegg.jp/list/pathway https://rest.kegg.jp/link/pathway/ko https://rest.kegg.jp/link/pathway/compound https://rest.kegg.jp/list/module https://rest.kegg.jp/link/module/ko https://rest.kegg.jp/link/module/compound

Usage

update_KEGG(download_dir)

update_KO_file(download_dir, RDSfile = NULL)

update_htable(type, keg_file = NULL, download = FALSE, download_dir = NULL)

update_org_pathway(
  org = "hsa",
  RDS_file = NULL,
  download = TRUE,
  download_dir = NULL
)
update_KEGG(download_dir)

update_KO_file(download_dir, RDSfile = NULL)

update_htable(type, keg_file = NULL, download = FALSE, download_dir = NULL)

update_org_pathway(
  org = "hsa",
  RDS_file = NULL,
  download = TRUE,
  download_dir = NULL
)

Arguments

`download_dir`	where to save the .keg file?
`RDSfile`	saved KO_files.RDS file
`type`	"ko", "module", "pathway", "compound" ...
`keg_file`	path of a .keg file, such as ko00001.keg from https://www.genome.jp/kegg-bin/download_htext?htext=ko00001&format=htext.
`download`	save the .keg file?
`org`	kegg organism, listed in https://www.genome.jp/kegg/catalog/org_list.html, default, "hsa"
`RDS_file`	path of a org.RDS file if you saved before.

Value

No value

`enrich_res`	enrich_res object
`mode`	plot style: 1~2
`padj_threshold`	p.adjust threshold
`show_ID`	show pathway id
`Pathway_description`	show KO description rather than KO id.
`facet_level`	facet plot if the type is "pathway" or "module"
`facet_anno`	annotation table for facet, two columns, first is level summary, second is pathway id.
`str_width`	default: 50
`facet_str_width`	str width for facet label
`...`	add
`x`	enrich_res object

`x`	a cm_res object
`filter_membership`	filter membership
`mode`	1~2
`show.clust.cent`	show cluster center?
`show_num`	show number of each cluster?
`...`	additional

Package 'ReporterScore'

Help Index

Test the proper clusters k for c_means

Description

Usage

Arguments

Value

See Also

Examples

Combine the results of 'step by step GRSA'

Description

Usage

Arguments

Value

See Also

Examples

Compound htable from 'KEGG'

Description

See Also

The CPDlist used for enrichment.

Description

Format

See Also

Build a custom modulelist

Description

Usage

Arguments

Value

See Also

Examples

Custom modulelist from a specific organism

Description

Usage

Arguments

Value

See Also

Examples

Export report score result tables

Description

Usage

Arguments

Value

Transfer gene symbol table to KO table

Description

Usage

Arguments

Value

Examples

human gene table

Description

See Also

get features in a modulelist

Description

Usage

Arguments

Value

See Also

Examples

Calculate reporter score

Description

Usage

Arguments

Value

See Also

Examples

The GOlist used for enrichment.

Description

Format

See Also

pathway information for "hsa"

Description

See Also

The KOs abundance table and group table.

Description

See Also

Perform enrichment analysis

Description

Usage

Arguments

Value