Type:	Package
Title:	Professional Comprehensive Omics Data Analysis
Version:	0.1.3
Description:	Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.2.3
Depends:	R (≥ 4.2.0)
LazyData:	true
Imports:	pcutils (≥ 0.2.5), dplyr, ggplot2 (≥ 3.2.0), vegan, magrittr, grDevices, RColorBrewer, ggrepel, reshape2, stats, tibble, utils, ggpubr, ggnewscale, ade4, scales
Suggests:	picante, httr, NST, permute, aplot, pheatmap, MASS, Rtsne, mixOmics, geosphere, phyloseq, phyloseqGraphTest, plotly, umap, Hmisc, minpack.lm, bbmle, snow, foreach, doSNOW, patchwork, tidytree, ggtree, ggtreeExtra, vctrs, zoo, ape, DESeq2, limma, ALDEx2, Mfuzz, edgeR, methods, randomForest, knitr, rmarkdown, MetaNet, showtext, jsonlite, prettydoc, readxl, clipr, zetadiv, ggforce
VignetteBuilder:	knitr
BugReports:	https://github.com/Asa12138/pctax/issues
URL:	https://github.com/Asa12138/pctax
ByteCompile:	true
biocViews:	Microbiome, Software, Visualization
NeedsCompilation:	no
Packaged:	2024-12-02 09:39:58 UTC; asa
Author:	Chen Peng [aut, cre]
Maintainer:	Chen Peng <pengchen2001@zju.edu.cn>
Repository:	CRAN
Date/Publication:	2024-12-02 10:00:02 UTC

pctax: Professional Comprehensive Omics Data Analysis

Description

Provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.

Author(s)

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

See Also

Useful links:

• https://github.com/Asa12138/pctax

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

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Assignment pipe

Description

See magrittr::%<>% for details.

Usage

lhs %<>% rhs

Arguments

Value

The result of calling rhs(lhs).

ALDEX

Description

ALDEX

Usage

ALDEX(otutab, group_df)

Arguments

Value

diff

References

https://cloud.tencent.com/developer/article/1621879

Examples

if (requireNamespace("ALDEx2")) {
  data(otutab, package = "pcutils")
  ALDEX(otutab, metadata["Group"]) -> res
  res %>%
    dplyr::top_n(9, -glm.eBH) %>%
    .[, "tax"] -> sig
  data.frame(t(otutab[sig, ])) %>% pcutils::group_box(., "Group", metadata)
}

Calculate RCbray-curtis

Description

Calculate RCbray-curtis

Usage

RCbray1(
  otutab,
  reps = 9,
  threads = 1,
  classic_metric = TRUE,
  split_ties = TRUE
)

Arguments

Details

Parallelized version of the Raup-Crick algorithm for "abundance" data (Stegen et al. 2013).

Value

a dist

Examples

if (requireNamespace("picante")) {
  data(otutab, package = "pcutils")
  df2tree(taxonomy) -> phylo
  b_NTI1(phylo, otutab) -> bnti_res
  RCbray1(otutab, reps = 9) -> rc_res

  data.frame(
    type = factor(c("Homo_S", "Heter_S", "Homo_D", "D_limit", "Undominated"),
      levels = c("Homo_S", "Heter_S", "Homo_D", "D_limit", "Undominated")
    ),
    number = c(
      sum(bnti_res < (-2)), sum(bnti_res > 2),
      sum((abs(bnti_res) < 2) & (abs(rc_res) < 0.95)),
      sum((abs(bnti_res) < 2) & (rc_res < (-0.95))),
      sum((abs(bnti_res) < 2) & (rc_res > 0.95))
    )
  ) -> com_pro
  pcutils::gghuan(com_pro, reorder = FALSE)
}

Plot RDA res

Description

Plot RDA res

Usage

RDA_plot(
  phy.rda,
  Group,
  metadata = NULL,
  Group2 = NULL,
  env_rate = 1,
  mode = 1,
  tri = FALSE,
  Topn = 10,
  rate = 1,
  margin = FALSE,
  box = TRUE,
  pal = NULL,
  sample_label = TRUE,
  stat_ellipse = TRUE,
  coord_fix = FALSE,
  bi_text_size = 3,
  env_text_param = NULL,
  ...
)

Arguments

Value

ggplot

See Also

myRDA

Calculate a_diversity of otutab

Description

Calculate a_diversity of otutab

Usage

a_diversity(otutab, ...)

## S3 method for class 'data.frame'
a_diversity(
  otutab,
  method = c("richness", "shannon"),
  tree = NULL,
  digits = 4,
  ...
)

## S3 method for class 'pc_otu'
a_diversity(otutab, method = "all", tbl = "otutab", ...)

## S3 method for class 'numeric'
a_diversity(otutab, ...)

Arguments

Value

a a_res object

Examples

data(otutab, package = "pcutils")
a_diversity(otutab) -> a_res
plot(a_res, "Group", metadata)

add strips for a tree plot

Description

add strips for a tree plot

Usage

add_strip(trp, some_tax, flat_n = 5, strip_params = NULL)

Arguments

Value

tree plot

Examples

data(otutab, package = "pcutils")
# run yourself
if (interactive()) {
  ann_tree(taxonomy, otutab) -> tree
  easy_tree(tree) -> p
  some_tax <- table(taxonomy$Phylum) %>%
    sort(decreasing = TRUE) %>%
    head(5) %>%
    names()
  add_strip(p, some_tax)
}

Add taxonomy for a pc_otu object

Description

Add taxonomy for a pc_otu object

Usage

add_tax(pc, taxonomy)

Arguments

Value

pc_otu

Examples

data(otutab, package = "pcutils")
pc_tax1 <- pc_otu(otutab, metadata)
pc_tax1 <- add_tax(pc_tax1, taxonomy)

all element cycle information.

Description

all element cycle information.

Format

a list contains four tables.

ec_node

chemicals

ec_link

reactions

ec_gene

genes

ec_path

reactions labels

all species latin names and chinese names

Description

all species latin names and chinese names.

Format

a dataframe.

latin_name

latin name

chinese_name

chinese name

Annotate a tree

Description

Annotate a tree

Easy way to plot a phylogenetic tree

Usage

ann_tree(f_tax, otutab = NULL, level = ncol(f_tax))

easy_tree(
  tree,
  highlight = "Phylum",
  colorfill = "color",
  topN = NULL,
  pal = NULL,
  name_prefix = FALSE,
  basic_params = NULL,
  add_abundance = TRUE,
  color_name = "abundance",
  add_tiplab = TRUE,
  fontsize = NULL
)

Arguments

Value

a treedata

a ggplot

Examples

if (interactive()) {
  data(otutab, package = "pcutils")
  ann_tree(taxonomy, otutab) -> tree
  # run yourself
  easy_tree(tree, add_abundance = FALSE) -> p
  p
}

Calculate Abundance-occupancy_relationship

Description

Calculate Abundance-occupancy_relationship

Plot a AOR

Usage

aor(otutab, ...)

## S3 method for class 'data.frame'
aor(
  otutab,
  top_r = 0.7,
  ocup_n = ceiling(0.8 * ncol(otutab)),
  special_n = ceiling(0.1 * ncol(otutab)),
  ...
)

## S3 method for class 'AOR'
plot(x, ...)

Arguments

Value

AOR

ggplot

References

Barberán, A., Bates, S. T., Casamayor, E. & Fierer, N. (2012) Using network analysis to explore co-occurrence patterns in soil microbial communities.

Examples

data(otutab, package = "pcutils")
aor(otutab) -> AOR
plot(AOR)

Transfer dist to b_dist

Description

Transfer dist to b_dist

Plot dist

Plot b_dist

Usage

as.b_dist(dist, group_df = NULL)

## S3 method for class 'dist'
plot(x, group_df = NULL, ...)

## S3 method for class 'b_dist'
plot(x, mode = 1, c_group = "inter", ...)

Arguments

Value

a b_dist with annotation by group

a pheatmap

a ggplot or pheatmap

Examples

data(otutab, package = "pcutils")
mat_dist(otutab) %>% as.b_dist(., group_df = metadata["Group"]) -> aa
plot(aa)
plot(aa, mode = 2)

Transfer b_dist to dist

Description

Transfer b_dist to dist

Usage

## S3 method for class 'b_dist'
as.dist(m, diag = FALSE, upper = FALSE)

Arguments

Value

dist

Calculate beta_NTI

Description

Calculate beta_NTI

Usage

b_NTI1(
  phylo,
  otutab,
  beta.reps = 9,
  weighted = TRUE,
  threads = 1,
  verbose = TRUE
)

Arguments

Value

a dist: b_NTI

Beta_diversity Ordination: dimensionality reduction

Description

Species abundance data can be preprocessed with Hellinger transformation or chord transformation data before PCA analysis. Because the Hellinger distance or chord distance with-without data is equal to \sqrt2\sqrt{1-Ochiai\ similarity}, therefore, the sorting diagram (type 1 scale) of PCA analysis after Hellinger transformation or chord transformation with-without data is internal sample The distance between the squares is the Ochiai distance. \sqrt2\sqrt{1-Ochiai\ similarity} is a distance measure, which is also suitable for the analysis of species data. The processed data is then used for pca without norm.

Usage

b_analyse(otutab, ...)

## S3 method for class 'data.frame'
b_analyse(
  otutab,
  norm = TRUE,
  method = c("pca"),
  group = NULL,
  dist = "bray",
  ndim = 2,
  scale = FALSE,
  ...
)

Arguments

Value

b_res object

References

https://www.jianshu.com/p/9694c0b6302d https://zhuanlan.zhihu.com/p/25501130

Examples

data(otutab, package = "pcutils")
b_analyse(otutab, method = "pca") -> b_res
plot(b_res, "Group", metadata)

3D plot for b_res

Description

3D plot for b_res

Usage

b_res_3d(b_res, Group, metadata = NULL, ...)

Arguments

Value

plotly list

Examples

if (requireNamespace("plotly")) {
  data(otutab, package = "pcutils")
  b_analyse(otutab, method = "pca", ndim = 3) -> b_res
  b_res_3d(b_res, "Group", metadata)
}

ggdotchart for diff analysis

Description

ggdotchart for diff analysis

Usage

bbtt(res, pvalue = "glm.eBH", topN = 20)

Arguments

Value

ggplot

Before df2tree check

Description

Before df2tree check

Usage

before_tree(f_tax)

Arguments

Value

table

Examples

wrong_taxdf <- data.frame(
  kingdom = c(rep(c("A", "B"), each = 4), "C", NA),
  "phylum" = c("A", "a", "b", "c", "c", "c", "d", NA, NA, "e")
)
before_tree(wrong_taxdf)

Check taxonkit

Description

Check taxonkit

Usage

check_taxonkit(print = TRUE)

Arguments

Value

taxonkit path

See Also

Other Rtaxonkit: download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Convert taxon names between Chinese and Latin

Description

Convert taxon names between Chinese and Latin

Usage

convert_taxon_name(input_names, mode = "latin_to_chinese", fuzzy = FALSE)

Arguments

Value

character vector of converted names

Examples

convert_taxon_name(c("Escherichia coli", "Clostridioides difficile"))

Correlation network, species-interaction network for omics

Description

Correlation network, species-interaction network for omics

Usage

cor_net()

Value

No value

From a dataframe to construct a phylo

Description

NOTE: this function will do before_tree first.

Usage

df2tree(data, edge_df = FALSE)

Arguments

Value

phylo object

Examples

data(otutab, package = "pcutils")
df2tree(taxonomy) -> tax_tree
print(tax_tree)
# check all nodes matched!
if (requireNamespace("picante")) {
  picante::match.phylo.comm(tax_tree, t(otutab)) -> nn
  nrow(nn$comm) == nrow(t(otutab))
}

From a dataframe to construct a phylo (save nwk)

Description

NOTE: this function will transfer all space to ⁠_⁠

Usage

df2tree1(taxa)

Arguments

Value

phylo object

Examples

data(otutab, package = "pcutils")
df2tree(taxonomy) -> tax_tree
print(tax_tree)

Difference analysis

Description

Difference analysis

Usage

diff_da(
  otutab,
  group_df,
  ctrl = NULL,
  method = "deseq2",
  log = TRUE,
  add_mini = NULL,
  ...
)

Arguments

Value

a dataframe

Examples

if (requireNamespace("limma")) {
  data(otutab, package = "pcutils")
  diff_da(otutab, metadata["Group"], method = "limma") -> res
  volcano_p(res)
  volcano_p(res, mode = 2)
}

Download taxonkit dataset

Description

Download taxonkit dataset

Usage

download_taxonkit_dataset(make_sure = FALSE, taxdump_tar_gz = NULL)

Arguments

Value

No value

See Also

Other Rtaxonkit: check_taxonkit(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Envfit test for RDA result

Description

Envfit test for RDA result

Usage

envfitt(phy.rda, env, ...)

Arguments

Value

g_test object

See Also

envfit

Examples

data(otutab, package = "pcutils")
env <- metadata[, 6:10]
# RDA
myRDA(otutab, env) -> phy.rda
envfitt(phy.rda, env) -> envfit_res
plot(envfit_res)

Lm for sample similarity and geographical distance

Description

Lm for sample similarity and geographical distance

Usage

geo_sim(otutab, geo, method = "bray", spe_nwk = NULL, ...)

Arguments

Value

a ggplot

References

Graco-Roza, C. et al. (2022) Distance decay 2.0 - A global synthesis of taxonomic and functional turnover in ecological communities. Glob Ecol Biogeogr 31, 1399–1421.

Examples

if (requireNamespace("geosphere")) {
  library(ggplot2)
  data(otutab, package = "pcutils")
  metadata[, c("lat", "long")] -> geo
  geo_sim(otutab, geo) -> geo_res
}

get all species Latin and Chinese name from the CCTCC database

Description

get all species Latin and Chinese name from the CCTCC database

Usage

get_all_sp_la_zh_name(
  download_dir = "~/Documents/",
  each_verbose = FALSE,
  max_requests = 50,
  max_id = 30609,
  failure_ids = NULL
)

Arguments

Value

No value

Get mean and type

Description

Get mean and type

Usage

get_diff_type(otutab, group_df)

Arguments

Value

No value

Group inter-intra density

Description

Group inter-intra density

Usage

gp_dis_density(otutab, group)

Arguments

Value

ggplot

Examples

data(otutab, package = "pcutils")
gp_dis_density(otutab, metadata["Group"])

Performs graph-based permutation tests

Description

Performs graph-based permutation tests

Usage

grap_p_test(otutab, metadata, group = "Group", nperm = 999, ...)

Arguments

Value

ggplot

Examples

if (requireNamespace("phyloseqGraphTest") && requireNamespace("phyloseq")) {
  data(otutab, package = "pcutils")
  grap_p_test(otutab, metadata, "Group")
}

Install taxonkit

Description

Install taxonkit

Usage

install_taxonkit(make_sure = FALSE, taxonkit_tar_gz = NULL)

Arguments

Value

No value

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

KW test

Description

KW test

Usage

kwtest(otutab, group_df, method = "kruskal.test")

Arguments

Value

res

Examples

data(otutab, package = "pcutils")
kwtest(otutab, metadata["Group"]) -> res
bbtt(res, pvalue = "p.format")

Load N-cycle data

Description

Load N-cycle data

Usage

load_N_data()

Value

list

References

Tu, Q., Lin, L., Cheng, L., Deng, Y. & He, Z. (2019) NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes. Bioinformatics 35, 1040–1048. Kuypers, M. M. M., Marchant, H. K. & Kartal, B. (2018) The microbial nitrogen-cycling network. Nat Rev Microbiol 16, 263–276.

Calculate distance for otutab

Description

Calculate distance for otutab

Usage

mat_dist(otutab, method = "bray", spe_nwk = NULL)

Arguments

Value

dist

Examples

data(otutab, package = "pcutils")
mat_dist(otutab)

Microbiome sbatch

Description

Microbiome sbatch

Usage

micro_sbatch(
  work_dir = "/share/home/jianglab/pengchen/work/asthma/",
  step = "fastp",
  all_sample_num = 40,
  array = 1,
  partition = "cpu",
  cpus_per_task = 1,
  mem_per_cpu = "2G"
)

Arguments

Value

No value

Difference analysis

Description

Difference analysis

Usage

multi_bar(
  otutab,
  group_df,
  mode = 1,
  text_df = NULL,
  text_x = NULL,
  text_angle = -90,
  errorbar = "bottom"
)

Arguments

Value

a data.frame

Examples

data(otutab, package = "pcutils")
multi_bar(otutab[1:10, ], metadata["Group"])

RDA

Description

RDA

Usage

myRDA(
  otutab,
  env,
  norm = TRUE,
  scale = FALSE,
  choose_var = FALSE,
  direction = "forward",
  nperm = 499,
  verbose = TRUE,
  method = "rda",
  dist = "bray"
)

myCCA(
  otutab,
  env,
  norm = TRUE,
  scale = FALSE,
  choose_var = FALSE,
  nperm = 499,
  verbose = TRUE
)

myCAP(
  otutab,
  env,
  norm = TRUE,
  scale = FALSE,
  choose_var = FALSE,
  nperm = 499,
  verbose = TRUE,
  dist = "bray"
)

Arguments

Value

rda/cca

See Also

vegdist;unifrac

Examples

data(otutab, package = "pcutils")
env <- metadata[, 6:10]
# RDA
myRDA(otutab, env) -> phy.rda
RDA_plot(phy.rda, "Group", metadata)

Transfer taxon name or taxid to the lineage dataframe

Description

Transfer taxon name or taxid to the lineage dataframe

Usage

name_or_id2df(
  name_or_id,
  mode = "name",
  add_prefix = TRUE,
  fill_miss_rank = TRUE,
  data_dir = NULL
)

Arguments

Value

dataframe

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Examples

## Not run: 
name_or_id2df(c("Homo sapiens", "Akkermansia muciniphila ATCC BAA-835"))

## End(Not run)

Sloan Neutral Model

Description

Sloan Neutral Model

Plot ncm_res

Usage

ncm(otutab, model = "nls")

## S3 method for class 'ncm_res'
plot(
  x,
  mycols = c(Above = "#069870", Below = "#e29e02", In = "#1e353a"),
  text_position = NULL,
  pie_text_params = list(size = 2.5),
  ...
)

Arguments

Value

ncm_res

ggplot

References

Sloan, W. TRUE. et al. (2006) Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environmental Microbiology 8, 732–740.

Examples

if (requireNamespace("Hmisc") && requireNamespace("minpack.lm")) {
  data(otutab, package = "pcutils")
  ncm(otutab) -> ncm_res
  plot(ncm_res)
}

Calculate NST for each group

Description

Calculate NST for each group

Usage

nst(otutab, group_df, threads = 1, file = NULL, rep = 20, save = FALSE)

Arguments

Value

a b_dist object, dis is MSTij

References

Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. (2019) A general framework for quantitatively assessing ecological stochasticity. Proceedings of the National Academy of Sciences 116, 16892–16898.

Examples

if (requireNamespace("NST")) {
  library(ggplot2)
  data(otutab, package = "pcutils")
  nst(otutab, metadata["Group"]) -> nst_res
  plot(nst_res, c_group = "intra") + geom_hline(yintercept = 0.5, lty = 2) + ylab("NST")
}

Calculate b_NTI and RC_bray for each group

Description

Calculate b_NTI and RC_bray for each group

Plot NTI_RC object

Usage

nti_rc(
  otutab,
  phylo,
  group_df,
  threads = 1,
  file = NULL,
  rep = 20,
  save = FALSE
)

## S3 method for class 'NTI_RC'
plot(x, ...)

Arguments

Value

a b_dist object, dis is MSTij

ggplot

References

Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. (2019) A general framework for quantitatively assessing ecological stochasticity. Proceedings of the National Academy of Sciences 116, 16892–16898.

Examples

if (requireNamespace("NST") && requireNamespace("pctax")) {
  data(otutab, package = "pcutils")
  pctax::df2tree(taxonomy) -> phylo
  nti_rc(otutab, phylo, metadata["Group"]) -> nti_res
  plot(nti_res)
}

Create a pc_otu class object

Description

Create a pc_otu class object

Usage

pc_otu(otutab = data.frame(), metadata = data.frame(), taxonomy = NULL, ...)

Arguments

Value

pc_otu

Examples

data(otutab, package = "pcutils")
pc_tax1 <- pc_otu(otutab, metadata)
print(pc_tax1)

test data (pc_otu class) for pc_tax package.

Description

an otutab, metadata and a taxonomy table.

Format

a pc_otu contains an otutab, metadata and a taxonomy table.

tbls

contians otutable rawdata

metas

contians metadata

otus

contians taxomomy table

Judge pc_otu is valid or not

Description

Judge pc_otu is valid or not

Usage

pc_valid(pc)

Arguments

Value

logical

Permanova between a otutab and a variable

Description

Permanova between a otutab and a variable

Usage

permanova(
  otutab,
  envs,
  norm = TRUE,
  each = TRUE,
  method = "adonis",
  dist = "bray",
  nperm = 999,
  ...
)

Arguments

Value

a g_test object with these columns

References

https://blog.csdn.net/qq_42458954/article/details/110390488

Examples

data(otutab, package = "pcutils")
permanova(otutab, metadata[, c(2:10)]) -> adonis_res
print(adonis_res)
plot(adonis_res)

Plot a_res object

Description

Plot a_res object

Usage

## S3 method for class 'a_res'
plot(x, group, metadata, ...)

Arguments

Value

patchwork object,you can change theme with &

See Also

a_diversity

Plot a b_res

Description

Plot a b_res

Usage

## S3 method for class 'b_res'
plot(
  x,
  Group,
  metadata = NULL,
  Group2 = NULL,
  mode = 1,
  bi = FALSE,
  Topn = 10,
  rate = 1,
  margin = FALSE,
  margin_label = TRUE,
  permanova_res = NULL,
  text_param = list(),
  box_margin = TRUE,
  box_param = list(),
  pal = NULL,
  sample_label = TRUE,
  stat_ellipse = TRUE,
  coord_fix = FALSE,
  bi_text_size = 3,
  ...
)

Arguments

Value

a ggplot

See Also

b_analyse

Plot g_test

Description

Plot g_test

Usage

## S3 method for class 'g_test'
plot(x, ...)

Arguments

Value

ggplot

See Also

permanova

Plot pro_res

Description

Plot pro_res

Usage

## S3 method for class 'pro_res'
plot(x, group, metadata = NULL, pal = NULL, ...)

Arguments

Value

a ggplot

Plot time_cm

Description

Plot time_cm

Usage

## S3 method for class 'time_cm'
plot(x, mem_thr = 0.6, ...)

Arguments

Value

ggplot

Plot the N-cycling pathway and genes

Description

Plot the N-cycling pathway and genes

Usage

plot_N_cycle(
  my_N_genes = NULL,
  just_diff = FALSE,
  path_col = NULL,
  type_col = c(up = "red", down = "blue", none = NA),
  fill_alpha = 0.5,
  arrow_size = 0.1,
  line_width = 1,
  title = "Nitrogen cycling",
  legend.position = c(0.85, 0.15)
)

Arguments

Value

ggplot

Examples

N_data <- load_N_data()
my_N_genes <- data.frame(
  `Gene_families` = sample(N_data$N_genes$Gene_families, 10, replace = FALSE),
  change = rnorm(10), check.names = FALSE
)
my_N_genes <- dplyr::mutate(my_N_genes,
  type = ifelse(change > 0, "up", ifelse(change < 0, "down", "none"))
)
plot_N_cycle(my_N_genes, just_diff = FALSE, fill_alpha = 0.2)
# ggsave(filename = "test.pdf", width = 14, height = 10)

Plot element cycle

Description

Plot element cycle

Usage

plot_element_cycle(
  cycle = "Nitrogen cycle",
  anno_df = NULL,
  only_anno = FALSE,
  cell_fill = NA,
  cell_color = "orange",
  use_chinese = FALSE,
  chemical_size = 7,
  chemical_bold = TRUE,
  chemical_color = "black",
  chemical_label = TRUE,
  reaction_width = 1,
  reaction_arrow_size = 4,
  reaction_arrow_closed = TRUE,
  gene_or_ko = "gene",
  gene_size = 3,
  gene_x_offset = 0.3,
  gene_y_offset = 0.15,
  gene_label = TRUE,
  gene_color = NULL,
  gene_bold = TRUE,
  gene_italic = TRUE,
  gene_label_fill = "white"
)

Arguments

Value

ggplot

Examples

if (requireNamespace("ggforce")) plot_element_cycle()

Plot two trees in one plot

Description

Plot two trees in one plot

Usage

plot_two_tree(
  tree1,
  tree2,
  edge_df = NULL,
  tree2_x = 10,
  filter_link = FALSE,
  tree1_param = list(),
  tree2_param = list(),
  line_param = list(),
  tree1_tip = FALSE,
  tip1_param = list(),
  tree2_tip = FALSE,
  tip2_param = list(),
  tree1_highlight = NULL,
  highlight1_param = list(),
  highlight1_scale = NULL,
  tree2_highlight = NULL,
  highlight2_param = list(),
  highlight2_scale = ggplot2::scale_fill_hue(na.value = NA)
)

Arguments

Value

ggplot object

Examples

if (requireNamespace("ggtree")) {
  data(otutab, package = "pcutils")
  df2tree(taxonomy[1:50, ]) -> tax_tree
  df2tree(taxonomy[51:100, ]) -> tax_tree2
  link <- data.frame(from = sample(tax_tree$tip.label, 20), to = sample(tax_tree2$tip.label, 20))
  plot_two_tree(tax_tree, tax_tree2, link)
}

Prepare the result from fastp (.json file)

Description

Prepare the result from fastp (.json file)

Usage

pre_fastp(jsonfiles, prefix = c("Raw", "Clean"))

Arguments

Value

data.frame

Complete a taxonomy table

Description

Complete a taxonomy table

Usage

pre_tax_table(
  tax_table,
  tax_levels = c("k", "p", "c", "o", "f", "g", "s", "st"),
  na_tax = "Unclassified|uncultured|Ambiguous|Unknown|unknown|metagenome|Unassig",
  ignore.case = TRUE,
  na_repalce = "Unknown"
)

Arguments

Value

a good taxonomy table

References

MicrobiotaProcess

Examples

taxmat <- matrix(sample("onelevel", 7 * 2, replace = TRUE), nrow = 2, ncol = 7) %>% as.data.frame()
colnames(taxmat) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
pre_tax_table(taxmat)

Print

Description

Print

Usage

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

Arguments

Value

No value

Procrustes Rotation of Two Configurations and PROTEST

Description

Procrustes Rotation of Two Configurations and PROTEST

Usage

procrustes_analyse(b_res1, b_res2, nperm = 999, ...)

Arguments

Value

pro_res

Examples

data(otutab, package = "pcutils")
b_analyse(otutab, method = "pca") -> b_res1
b_analyse(otutab * abs(rnorm(10)), method = "pca") -> b_res2
pro_res <- procrustes_analyse(b_res1, b_res2)
plot(pro_res, "Group", metadata)

Rare the sample

Description

Rare the sample

Plot a rare curve

Usage

rare_curve_sample(otutab, rep = 30, count_cutoff = 1)

## S3 method for class 'rare_res'
plot(x, ...)

Arguments

Value

ggplot

ggplot

Examples

data(otutab, package = "pcutils")
a <- rare_curve_sample(otutab)
plot(a)

Rare the species

Description

Rare the species

Usage

rare_curve_species(
  otutab,
  step = 2000,
  method = "richness",
  mode = 2,
  reps = 3,
  threads = 1,
  verbose = TRUE
)

Arguments

Value

ggplot

Examples

data(otutab, package = "pcutils")
a <- rare_curve_species(otutab, mode = 1)
plot(a)

Rarefy a otutab

Description

Rarefy a otutab

Usage

rarefaction(otutab, sample = NULL)

Arguments

Value

a rarefied otutab

Examples

data(otutab, package = "pcutils")
rarefaction(otutab)

RandomForest

Description

RandomForest

Usage

suijisenlin(otutab, group_df, topN = 10)

Arguments

Value

diff

Examples

if (requireNamespace("randomForest")) {
  data(otutab, package = "pcutils")
  suijisenlin(otutab, metadata["Group"]) -> rf_res
}

Summary pc_otu

Description

Summary pc_otu

Usage

## S3 method for class 'pc_otu'
summary(object, ...)

Arguments

Value

No value

Examples

data("pc_tax1")
summary(pc_tax1)

Calculate the lowest common ancestor (LCA) of a set of taxa

Description

Calculate the lowest common ancestor (LCA) of a set of taxa

Usage

tax_lca(df)

Arguments

Value

character

Examples

df <- data.frame(
  A = c("a", "a", "a", "a"),
  B = c("x", "x", "y", "y"),
  C = c("1", "1", "2", "3"),
  stringsAsFactors = FALSE
)
tax_lca(df)

Filter TaxIDs based on Taxonomic Ranks

Description

This function uses the "taxonkit filter" command to filter TaxIDs based on taxonomic ranks.

Usage

taxonkit_filter(
  file_path,
  black_list = NULL,
  discard_noranks = FALSE,
  discard_root = FALSE,
  equal_to = NULL,
  higher_than = NULL,
  lower_than = NULL,
  rank_file = NULL,
  root_taxid = NULL,
  save_predictable_norank = FALSE,
  taxid_field = NULL,
  text = FALSE,
  data_dir = NULL
)

Arguments

Value

A character vector containing the output of the "taxonkit filter" command.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Examples

## Not run: 
taxids2 <- system.file("extdata/taxids2.txt", package = "pctax")
taxonkit_filter(taxids2, lower_than = "genus")

## End(Not run)

Compute Lowest Common Ancestor (LCA) of TaxIDs

Description

This function uses the "taxonkit lca" command to compute the Lowest Common Ancestor (LCA) of TaxIDs.

Usage

taxonkit_lca(
  file_path,
  buffer_size = "1M",
  separator = " ",
  skip_deleted = FALSE,
  skip_unfound = FALSE,
  taxids_field = NULL,
  text = FALSE,
  data_dir = NULL
)

Arguments

Value

A character vector containing the computed LCAs.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Examples

## Not run: 
taxonkit_lca("239934, 239935, 349741", text = TRUE, separator = ", ")

## End(Not run)

Retrieve Taxonomic Lineage using taxonkit

Description

Retrieve Taxonomic Lineage using taxonkit

Usage

taxonkit_lineage(
  file_path,
  delimiter = ";",
  no_lineage = FALSE,
  show_lineage_ranks = FALSE,
  show_lineage_taxids = FALSE,
  show_name = FALSE,
  show_rank = FALSE,
  show_status_code = FALSE,
  taxid_field = 1,
  text = FALSE,
  data_dir = NULL
)

Arguments

Value

A character vector containing the taxonomic lineage information.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_list(), taxonkit_name2taxid(), taxonkit_reformat()

Examples

## Not run: 
taxonkit_lineage("9606\n63221", show_name = TRUE, show_rank = TRUE, text = TRUE)

## End(Not run)

Taxonkit list

Description

This function uses Taxonkit to perform the "list" operation, which retrieves information about taxa based on their TaxIDs.

Usage

taxonkit_list(
  ids,
  indent = "  ",
  json = FALSE,
  show_name = FALSE,
  show_rank = FALSE,
  data_dir = NULL
)

Arguments

Value

The output of the Taxonkit list operation.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_name2taxid(), taxonkit_reformat()

Examples

## Not run: 
taxonkit_list(ids = c(9605), indent = "-", show_name = TRUE, show_rank = TRUE)

## End(Not run)

Convert Taxonomic Names to TaxIDs

Description

This function uses the "taxonkit taxonkit_name2taxid" command to convert taxonomic names to corresponding taxonomic IDs (TaxIDs).

Usage

taxonkit_name2taxid(
  file_path,
  name_field = NULL,
  sci_name = FALSE,
  show_rank = FALSE,
  text = FALSE,
  data_dir = NULL
)

Arguments

Value

A character vector containing the output of the "taxonkit_name2taxid" command.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_reformat()

Examples

## Not run: 
names <- system.file("extdata/name.txt", package = "pctax")
taxonkit_name2taxid(names, name_field = 1, sci_name = FALSE, show_rank = FALSE)
"Homo sapiens" %>% taxonkit_name2taxid(text = TRUE)

## End(Not run)

Reformat Taxonomic Lineage using taxonkit

Description

Reformat Taxonomic Lineage using taxonkit

Usage

taxonkit_reformat(
  file_path,
  delimiter = NULL,
  add_prefix = FALSE,
  prefix_kingdom = "K__",
  prefix_phylum = "p__",
  prefix_class = "c__",
  prefix_order = "o__",
  prefix_family = "f__",
  prefix_genus = "g__",
  prefix_species = "s__",
  prefix_subspecies = "t__",
  prefix_strain = "T__",
  fill_miss_rank = FALSE,
  format_string = "",
  miss_rank_repl_prefix = "unclassified ",
  miss_rank_repl = "",
  miss_taxid_repl = "",
  output_ambiguous_result = FALSE,
  lineage_field = 2,
  taxid_field = NULL,
  pseudo_strain = FALSE,
  trim = FALSE,
  text = FALSE,
  data_dir = NULL
)

Arguments

Value

A character vector containing the reformatted taxonomic lineages.

See Also

Other Rtaxonkit: check_taxonkit(), download_taxonkit_dataset(), install_taxonkit(), name_or_id2df(), taxonkit_filter(), taxonkit_lca(), taxonkit_lineage(), taxonkit_list(), taxonkit_name2taxid()

Examples

## Not run: 
# Use taxid
taxids2 <- system.file("extdata/taxids2.txt", package = "pctax")
reformatted_lineages <- taxonkit_reformat(taxids2,
  add_prefix = TRUE, taxid_field = 1, fill_miss_rank = TRUE
)
reformatted_lineages
taxonomy <- strsplit2(reformatted_lineages, "\t")
taxonomy <- strsplit2(taxonomy$V2, ";")

# Use lineage result
taxonkit_lineage("9606\n63221", show_name = TRUE, show_rank = TRUE, text = TRUE) %>%
  taxonkit_reformat(text = TRUE)

## End(Not run)

Time series analysis

Description

Time series analysis

Usage

time_by_cm(otu_time, n_cluster = 6, min.std = 0)

Arguments

Value

time_cm

Examples

if (interactive()) {
  data(otutab, package = "pcutils")
  otu_time <- pcutils::hebing(otutab, metadata$Group)
  time_by_cm(otu_time, n_cluster = 4) -> time_cm_res
  plot(time_cm_res)
}

Volcano plot for difference analysis

Description

Volcano plot for difference analysis

Usage

volcano_p(
  res,
  logfc = 1,
  adjp = 0.05,
  text = TRUE,
  repel = TRUE,
  mode = 1,
  number = FALSE
)

Arguments

Value

ggplot

See Also

diff_da

Calculate Zeta Diversity

Description

This function calculates Zeta diversity for each group in the provided otutab.

This function plots the Zeta diversity results obtained from the z_diversity function.

Usage

z_diversity(otutab, group_df = NULL, zetadiv_params = list())

## S3 method for class 'zeta_res'
plot(x, lm_model = c("exp", "pl")[1], ribbon = FALSE, text = TRUE, ...)

Arguments

Value

zeta_res

A ggplot object.

Examples

if (requireNamespace("zetadiv")) {
  data(otutab, package = "pcutils")
  zeta_result <- z_diversity(otutab, metadata["Group"], zetadiv_params = list(sam = 10))
  plot(zeta_result, lm_model = "exp", text = TRUE)
}

Calculate Zeta Diversity with Distance

Description

This function calculates Zeta diversity for each group in the provided otutab.

Usage

z_diversity_decay(otutab, xy_df, group_df = NULL, zetadiv_params = list())

## S3 method for class 'zeta_decay'
plot(x, ribbon = TRUE, ...)

Arguments

Value

zeta_decay

A ggplot object.

Examples

if (requireNamespace("zetadiv")) {
  data(otutab, package = "pcutils")
  zeta_decay_result <- z_diversity_decay(otutab, metadata[, c("lat", "long")],
    metadata["Group"],
    zetadiv_params = list(sam = 10)
  )
  plot(zeta_decay_result)
}