Type:	Package
Title:	Generalized Reporter Score-Based Enrichment Analysis for Omics Data
Version:	0.1.9
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.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.2.3
Imports:	magrittr, dplyr, stats, ggplot2 (≥ 3.2.0), pcutils (≥ 0.2.5), utils, scales, ggnewscale, ggrepel, reshape2, stringr, foreach
Suggests:	knitr, rmarkdown, plyr, e1071, factoextra, snow, doSNOW, pheatmap, readr, R.utils, KEGGREST, clusterProfiler, enrichplot, pathview, GSA, vegan, MetaNet, igraph, ggraph, PADOG, safe, rSEA, GSVA
Depends:	R (≥ 4.2.0)
VignetteBuilder:	knitr
BugReports:	https://github.com/Asa12138/ReporterScore/issues
URL:	https://github.com/Asa12138/ReporterScore
NeedsCompilation:	no
Packaged:	2024-11-28 13:55:48 UTC; asa
Author:	Chen Peng [aut, cre]
Maintainer:	Chen Peng <pengchen2001@zju.edu.cn>
Repository:	CRAN
Date/Publication:	2024-11-28 14:10:06 UTC

ReporterScore: 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.

Author(s)

Maintainer: Chen Peng pengchen2001@zju.edu.cn (ORCID)

See Also

Useful links:

• https://github.com/Asa12138/ReporterScore

• Report bugs at https://github.com/Asa12138/ReporterScore/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Examples

seq_len(5) %>% sum()

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

See Also

Other data: Compound_htable, GOlist, KO_htable, KOlist, Module_htable, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

Compound htable from 'KEGG'

Description

Compound htable from 'KEGG'

See Also

Other data: CPDlist, GOlist, KO_htable, KOlist, Module_htable, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

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

See Also

Other data: CPDlist, Compound_htable, KO_htable, KOlist, Module_htable, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

The KOs abundance table and group table.

Description

The KOs abundance table and group table.

The KOs abundance table and group table.

See Also

Other test_data: genedf, reporter_score_res

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)

Arguments

Value

A data frame containing the enrichment results.

enrich_res object

See Also

Other common_enrich: KO_fisher(), KO_gsa(), KO_gsea(), KO_gsva(), KO_padog(), KO_safe(), KO_sea(), plot_enrich_res()

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
)

Arguments

Value

data.frame

See Also

Other common_enrich: KO_enrich(), KO_gsa(), KO_gsea(), KO_gsva(), KO_padog(), KO_safe(), KO_sea(), plot_enrich_res()

Examples

## 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,
  ...
)

Arguments

Value

enrich_res object

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsea(), KO_gsva(), KO_padog(), KO_safe(), KO_sea(), plot_enrich_res()

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)
}

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
)

Arguments

Value

DOSE object

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsva(), KO_padog(), KO_safe(), KO_sea(), plot_enrich_res()

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)
}

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",
  ...
)

Arguments

Value

enrich_res

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsea(), KO_padog(), KO_safe(), KO_sea(), plot_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")
}

KO htable from 'KEGG'

Description

KO htable from 'KEGG'

See Also

Other data: CPDlist, Compound_htable, GOlist, KOlist, Module_htable, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

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",
  ...
)

Arguments

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."

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsea(), KO_gsva(), KO_safe(), KO_sea(), plot_enrich_res()

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"
  )
}

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",
  ...
)

Arguments

Value

A data frame containing SAFE results for KO enrichment.

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsea(), KO_gsva(), KO_padog(), KO_sea(), plot_enrich_res()

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"
  )
}

Perform Simultaneous Enrichment Analysis

Description

Perform Simultaneous Enrichment Analysis

Usage

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

Arguments

Value

enrich_res

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsea(), KO_gsva(), KO_padog(), KO_safe(), plot_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)
}

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

See Also

Other data: CPDlist, Compound_htable, GOlist, KO_htable, Module_htable, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

Module htable from 'KEGG'

Description

Module htable from 'KEGG'

See Also

Other data: CPDlist, Compound_htable, GOlist, KO_htable, KOlist, Pathway_htable, hsa_kegg_pathway, mmu_kegg_pathway

Pathway htable from 'KEGG'

Description

Pathway htable from 'KEGG'

See Also

Other data: CPDlist, Compound_htable, GOlist, KO_htable, KOlist, Module_htable, hsa_kegg_pathway, mmu_kegg_pathway

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,
  ...
)

Arguments

Value

rs_by_cm

reporter_score object

ggplot

See Also

Other C_means: cm_test_k()

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)
}

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)

Arguments

Value

ggplot

ggplot

See Also

Other C_means: RSA_by_cm()

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)
}

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)

Arguments

Value

reporter_score object

See Also

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

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)

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)

Arguments

Value

a custom modulelist

modulelist

See Also

Other modulelist: custom_modulelist_from_org(), get_features()

Other modulelist: custom_modulelist_from_org(), get_features()

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)

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
)

Arguments

Value

modulelist

See Also

Other modulelist: custom_modulelist(), get_features()

Examples

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)

Arguments

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")

Arguments

Value

kodf

Examples

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

human gene table

Description

human gene table

See Also

Other test_data: KO_abundance, reporter_score_res

get features in a modulelist

Description

get features in a modulelist

Usage

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

Arguments

Value

KOids, or data.frame with these KOids.

See Also

Other modulelist: custom_modulelist_from_org(), custom_modulelist()

Examples

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
)

Arguments

Value

reporter_res data.frame

See Also

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

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)

pathway information for "hsa"

Description

pathway information for "hsa"

See Also

Other data: CPDlist, Compound_htable, GOlist, KO_htable, KOlist, Module_htable, Pathway_htable, mmu_kegg_pathway

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
)

Arguments

Value

ko_pvalue data.frame

See Also

Other GRSA: combine_rs_res(), get_reporter_score(), pvalue2zs(), reporter_score()

Examples

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

Load the CARDinfo (from CARD database)

Description

Load the CARDinfo (from CARD database)

Usage

load_CARDinfo(verbose = TRUE)

Arguments

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)

Arguments

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)

Arguments

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 information for "mmu"

Description

pathway information for "mmu"

See Also

Other data: CPDlist, Compound_htable, GOlist, KO_htable, KOlist, Module_htable, Pathway_htable, hsa_kegg_pathway

Modify the pathway description before plotting

Description

Modify the pathway description before plotting

Usage

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

Arguments

Value

reporter_res

Examples

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

Plot c_means result

Description

Plot c_means result

Usage

## S3 method for class 'cm_res'
plot(
  x,
  filter_membership,
  mode = 1,
  show.clust.cent = TRUE,
  show_num = TRUE,
  ...
)

Arguments

Value

ggplot

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")
)

Arguments

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
  )
}

Plot enrich_res

Description

Plot enrich_res

Plot enrich_res

Usage

plot_enrich_res(
  enrich_res,
  mode = 1,
  padj_threshold = 0.05,
  show_ID = FALSE,
  Pathway_description = TRUE,
  facet_level = FALSE,
  facet_anno = NULL,
  str_width = 50,
  facet_str_width = 15,
  ...
)

## S3 method for class 'enrich_res'
plot(
  x,
  mode = 1,
  padj_threshold = 0.05,
  show_ID = FALSE,
  Pathway_description = TRUE,
  facet_level = FALSE,
  facet_anno = NULL,
  str_width = 50,
  facet_str_width = 15,
  ...
)

Arguments

Value

ggplot

ggplot

See Also

Other common_enrich: KO_enrich(), KO_fisher(), KO_gsa(), KO_gsea(), KO_gsva(), KO_padog(), KO_safe(), KO_sea()

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
)

Arguments

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)
)

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
)

Arguments

Value

ggplot

Examples

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()
)

Arguments

Value

ggplot

Examples

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")
)

Arguments

Value

ggplot

Examples

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,
  ...
)

Arguments

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)
}

Plot htable levels

Description

Plot htable levels

Usage

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

Arguments

Value

ggplot

Examples

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

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_line = TRUE,
  reorder = FALSE
)

Arguments

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)

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
)

Arguments

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)
}

Plot the significance of pathway

Description

Plot the significance of pathway

Usage

plot_significance(reporter_res, map_id)

Arguments

Value

ggplot

Examples

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

Print reporter_score

Description

Print reporter_score

Usage

## S3 method for class 'reporter_score'
print(x, ...)

Arguments

Value

No value

Print rs_by_cm

Description

Print rs_by_cm

Usage

## S3 method for class 'rs_by_cm'
print(x, ...)

Arguments

Value

No value

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"
)

Arguments

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

See Also

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

Examples

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
)

Arguments

Value

reporter_score object:

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

See Also

Other GRSA: combine_rs_res(), 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
)

'reporter_score()' result from KO_abundance_test

Description

'reporter_score()' result from KO_abundance_test

'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

See Also

Other test_data: KO_abundance, genedf

Upgrade the KO level

Description

Upgrade the KO level

Usage

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

Arguments

Value

data.frame

Examples

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)

Arguments

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)

Arguments

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
)

Arguments

Value

No value