,
Leo Lahti [aut] | Title: | microbiomeutilities: Utilities for Microbiome Analytics |
|---|---|
| Description: | This is a supporting tool for extending the functionality of both phyloseq and microbiome R packages. |
| Authors: | Sudarshan Shetty [aut, cre] ,
Leo Lahti [aut] |
| Maintainer: | Sudarshan Shetty <[email protected]> |
| License: | BSD_2_clause + file LICENSE |
| Version: | 1.00.17 |
| Built: | 2024-11-06 19:14:46 UTC |
| Source: | https://github.com/microsud/microbiomeutilities |
Utility to add refseq slot for dada2 based phyloseq object. Here, the rownames which are unique sequences, are stored in refseq slot of phyloseq. Sequence ids are converted to ids using tag option.
add_refseq(x, tag = "ASV")add_refseq(x, tag = "ASV")
x |
|
tag |
Provide name for Ids, Default="ASV". |
phyloseq-class object
# ps <- add_refseq(p0,tag="ASV") # ps# ps <- add_refseq(p0,tag="ASV") # ps
Summarize phyloseq: combine other than the most abundant taxa.
aggregate_top_taxa2(x, top, level)aggregate_top_taxa2(x, top, level)
x |
|
top |
Keep the top-n taxa, and merge the rest under the category 'Other'. Instead of top-n numeric this can also be a character vector listing the groups to combine. |
level |
Summarization level (from |
Backup from microbiome R pkg. This function is replaced by aggregate_rare function.
phyloseq-class object
Contact: Leo Lahti [email protected]
See citation('microbiome')
data(dietswap) s <- aggregate_top_taxa2(dietswap, top = 3, "Phylum")data(dietswap) s <- aggregate_top_taxa2(dietswap, top = 3, "Phylum")
Identify dominant taxa in each sample and give overview.
dominant_taxa(x, level = NULL, group = NULL)dominant_taxa(x, level = NULL, group = NULL)
x |
|
level |
Taxonomic level uses microbiome::aggregate_taxa |
group |
Provide overview by groups. Default=NULL |
Identifies the dominant taxa in each sample and gives an overview of frequency and percent sample that are dominated by each taxon. Can be group wise or overall.
A list of two data frames/tibbles
library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 x.d <- dominant_taxa(p0, level = "Genus", group = "DiseaseState") head(x.d$dominant_overview)library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 x.d <- dominant_taxa(p0, level = "Genus", group = "DiseaseState") head(x.d$dominant_overview)
Finding the samples dominated by user provided taxa in a phyloseq object. This is useful especially if user suspects a taxa to be contaminant and wishes to identify which samples are dominated by the contaminant taxa.
find_samples_taxa(x, taxa = NULL, relative = FALSE)find_samples_taxa(x, taxa = NULL, relative = FALSE)
x |
|
taxa |
this should match the rownames of otu_table(x) |
relative |
Logical. If TRUE will transform input to relative abundance. Default=FALSE |
A character with sample ids.
library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- aggregate_taxa(p0, "Genus") bac_dom <- find_samples_taxa(p0.f, taxa = "g__Bacteroides") # get samples dominated by g__Bacteroides ps.sub <- prune_samples(sample_names(p0.f) %in% bac_dom, p0.f)library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- aggregate_taxa(p0, "Genus") bac_dom <- find_samples_taxa(p0.f, taxa = "g__Bacteroides") # get samples dominated by g__Bacteroides ps.sub <- prune_samples(sample_names(p0.f) %in% bac_dom, p0.f)
phyloseq-class Object advancedFormat the phyloseq object to add the best taxonomy in phyloseq object (tax_table and otu_table).
format_to_besthit(x, prefix = NULL)format_to_besthit(x, prefix = NULL)
x |
|
prefix |
Prefered prefix e.g. OTU-d__denovo161:Roseburia or ASV-d__denovo161:Roseburia |
Most commonly it is observed that the taxonomy file has classification until a given taxonomic level.
row.names for both tax_table and otu_table have best hit, until maximun genus level (species classification with short amplicons is a myth)is made available. This code is a
slight modification of the code from ampvis phyloseq-class.
Here, we directly take the phyloseq object as input and make the necessary formatting.
phyloseq-class object
## Not run: # Example data library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0, prefix = "OTU-") ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0, prefix = "OTU-") ## End(Not run)
Taxa abundance summary by group. Useful for description of microbiome.
get_group_abundances(x, level, group, transform = "compositional")get_group_abundances(x, level, group, transform = "compositional")
x |
|
level |
Taxonomic level uses microbiome::aggregate_taxa, if NULL with return OTU/ASVs level stats. |
group |
Provide overview by groups. Default=NULL and will return values for entire dataset, akin to taxa_summary. |
transform |
Either "compositional" or "counts". Default= compositional |
A data frames/ grouped tibble
## Not run: library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 get_group_abundances(p0, level = "Phylum", group = "DiseaseState") ## End(Not run)## Not run: library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 get_group_abundances(p0, level = "Phylum", group = "DiseaseState") ## End(Not run)
Test microbiome data in phyloseq format.
get_microbiome_data(disease, study)get_microbiome_data(disease, study)
disease |
Disease of interest as shown in list_microbiome_data() |
study |
Name of the study as shown in list_microbiome_data() |
You can download few example datasets in phyloseq format from Duvallet et al 2017 https://www.nature.com/articles/s41467-017-01973-8.pdf?origin=ppub. The source file for these data is the microbiomedatarepo https://github.com/microsud/microbiomedatarepo.
phyloseq-class object.
## Not run: # Example data library(microbiome) library(microbiomeUtilities) list_microbiome_data() ps1 <- get_microbiome_data(disease = "CDI", "Schubert2014_CDI") print_ps(ps1) ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeUtilities) list_microbiome_data() ps1 <- get_microbiome_data(disease = "CDI", "Schubert2014_CDI") print_ps(ps1) ## End(Not run)
Utility to convert phyloseq slots to tibbles.
get_tibble(x, slot = "otu_table", column_id = "column_id")get_tibble(x, slot = "otu_table", column_id = "column_id")
x |
|
slot |
Must be one of c("otu_table", "sam_data", "tax_table"). Default= "otu_table" |
column_id |
Provide name for the column which will hold the rownames of slot. |
A tibble
library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 otu_tibble <- get_tibble(p0,slot="otu_table",column_id="OTUID") head(otu_tibble)library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 otu_tibble <- get_tibble(p0,slot="otu_table",column_id="OTUID") head(otu_tibble)
Data from a Stansfield J, Dozmorov M. "16s rRNA sequencing data from the Human Microbiome Project 2". Randomly choosen 13 particpants with multiple timepoints for rectum samples.
data("hmp2")data("hmp2")
An object of class "phyloseq".
Stansfield J, Dozmorov M (2019). HMP2Data: 16s rRNA sequencing data from the Human Microbiome Project 2. R package version 1.1.0, https://bioconductor.org/packages/HMP2Data/
## Not run: library(microbiomeutilities) data("hmp2") pseq <- hmp2 print(hmp2) ## End(Not run)## Not run: library(microbiomeutilities) data("hmp2") pseq <- hmp2 print(hmp2) ## End(Not run)
Utility to join otu_table and tax_table to tibble.
join_otu_tax(x, column_id = "OTUID")join_otu_tax(x, column_id = "OTUID")
x |
|
column_id |
Provide name for the column which will hold the rownames of slot. |
A tibble
library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 otu_tax <- join_otu_tax(p0,column_id = "OTUID") head(otu_tax)library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 otu_tax <- join_otu_tax(p0,column_id = "OTUID") head(otu_tax)
Data are used from Duvallet et al 2017 https://www.nature.com/articles/s41467-017-01973-8.pdf?origin=ppub.
list_microbiome_data(printtab = TRUE)list_microbiome_data(printtab = TRUE)
printtab |
Print in console or not, defaut is TRUE and will print output. |
Data for practice, also an example for importing mothur files from Baxtrer et al 2016. The source file for these data is the microbiomedatarepo https://github.com/microsud/microbiomedatarepo.
## Not run: library(microbiomeutilities) df <- list_microbiome_data(printtab = FALSE) ## End(Not run)## Not run: library(microbiomeutilities) df <- list_microbiome_data(printtab = FALSE) ## End(Not run)
Creates a combination of variables for use with ggpubr::stat_compare_means.
make_pairs(x)make_pairs(x)
x |
list of vector to compare |
# library(microbiomeutilities) # data("zackular2014") # pseq <- zackular2014 # comps <- make_pairs(meta(pseq)$DiseaseState)# library(microbiomeutilities) # data("zackular2014") # pseq <- zackular2014 # comps <- make_pairs(meta(pseq)$DiseaseState)
phyloseq objectsThese functions work on otu_table, tax_table, sample_data
or on data.frame and matrix.
peak_abundance returns, user specified number of rows and columns
for otu_table.
#' peak_taxonomy returns, user specified number of rows and columns
for tax_table.
peak_sample returns, user specified number of rows and columns
for sample_data.
peak_base returns, user specified number of rows and columns
for data.frame and matrix.
peak_abundance(x, nrows = 1:5, ncols = 1:5) peak_taxonomy(x, nrows = 1:5, ncols = 1:5) peak_sample(x, nrows = 1:5, ncols = 1:5) peak_base(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_abundance(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_taxonomy(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_sample(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'ANY' peak_base(x, nrows = 1:5, ncols = 1:5)peak_abundance(x, nrows = 1:5, ncols = 1:5) peak_taxonomy(x, nrows = 1:5, ncols = 1:5) peak_sample(x, nrows = 1:5, ncols = 1:5) peak_base(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_abundance(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_taxonomy(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'phyloseq' peak_sample(x, nrows = 1:5, ncols = 1:5) ## S4 method for signature 'ANY' peak_base(x, nrows = 1:5, ncols = 1:5)
x |
a
|
nrows |
number of rows, to be specified as numeric e.g. 1, or sequence of numeric specified as 1:5. to return first to fifth row. |
ncols |
number of cols, to be specified as numeric e.g. 1, or sequence of numeric specified as 1:5 to return first to fifth col. |
Print user specified rows and columns
data("zackular2014") peak_abundance(zackular2014, nrows=1:3, ncols = 1:3) peak_taxonomy(zackular2014, nrows=1:3, ncols = 1:3) peak_sample(zackular2014, nrows=1:3, ncols = 1:3) dat.frm <- meta(zackular2014) # specify specific columns peak_base(dat.frm, nrows=1:3, ncols = c(1, 3, 4)) matrix_ab <- abundances(zackular2014) peak_base(matrix_ab, nrows=1:3, ncols = 1:3)data("zackular2014") peak_abundance(zackular2014, nrows=1:3, ncols = 1:3) peak_taxonomy(zackular2014, nrows=1:3, ncols = 1:3) peak_sample(zackular2014, nrows=1:3, ncols = 1:3) dat.frm <- meta(zackular2014) # specify specific columns peak_base(dat.frm, nrows=1:3, ncols = c(1, 3, 4)) matrix_ab <- abundances(zackular2014) peak_base(matrix_ab, nrows=1:3, ncols = 1:3)
phyloseq-class
Summarize the percent taxa classification for phyloseq-class.
percent_classified(x)percent_classified(x)
x |
|
Table with information on percent OTUs classified.
Contact: Sudarshan A. Shetty [email protected]
## Not run: library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 percent_classified(pseq) ## End(Not run)## Not run: library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 percent_classified(pseq) ## End(Not run)
phyloseq-class object to long data formatAn alternative to psmelt function from phyloseq-class object.
phy_to_ldf(x, transform.counts)phy_to_ldf(x, transform.counts)
x |
|
transform.counts |
Data transform to be used in plotting
(but not in sample/taxon ordering). The options are 'Z-OTU', 'Z-Sample',
'log10' and 'compositional'. See the |
A data frame in long format with appropriate transfomation if requested
## Not run: # Example data library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 pseq_df <- phy_to_ldf(pseq, transform.counts = NULL) ## End(Not run)## Not run: # Example data library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 pseq_df <- phy_to_ldf(pseq, transform.counts = NULL) ## End(Not run)
Calculated difference in microbiota composition for each individual between two timepoints.
plasticity(x, dist.method = "bray", participant.col)plasticity(x, dist.method = "bray", participant.col)
x |
|
dist.method |
Any of the methods supported by phyloseq::distance or correlation method cor() |
participant.col |
Column name with participant IDs |
Using a beta diversity metrics or correlation matrix to identify variability in microbiota of an individual. The code is slight modification from Grembi et. al. see ref below. This is useful for instance if one wants to quantiify changes in microbiota before and after a treatment, dietary modulation, antibiotic treatment, etc. The choice of index is important. For example, Bray-Curtis dissimilarity, the higher values mean higher plasticity/variability. On the contrary, higer spearman correlation values mean lower plasticity.
plot
Grembi, J.A., Nguyen, L.H., Haggerty, T.D. et al. Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention. Sci Rep 10, 1405 (2020).https://www.nature.com/articles/s41598-020-58000-y
## Not run: library(microbiome) library(microbiomeutilities) library(dplyr) library(ggpubr) data(peerj32) pseq <- peerj32$phyloseq pseq.rel <- microbiome::transform(pseq, "compositional") pl <- plasticity(pseq.rel, participant.col = "subject") ## End(Not run)## Not run: library(microbiome) library(microbiomeutilities) library(dplyr) library(ggpubr) data(peerj32) pseq <- peerj32$phyloseq pseq.rel <- microbiome::transform(pseq, "compositional") pl <- plasticity(pseq.rel, participant.col = "subject") ## End(Not run)
Plots Mean Abundance-Prevalence for taxa. Mean abundance, mean prevalence, and upper and lower confidence interval for each taxa is calculated by random subsampling.
plot_abund_prev( x, lower.conf = 0.025, upper.conf = 0.975, bs.iter = 99, color = "steelblue", dot.opacity = 0.5, dot.size = 2, label.core = FALSE, label.size = NULL, label.opacity = NULL, label.color = "grey70", mean.abund.thres = NULL, mean.prev.thres = NULL, log.scale = TRUE, nudge.label = NULL, ... )plot_abund_prev( x, lower.conf = 0.025, upper.conf = 0.975, bs.iter = 99, color = "steelblue", dot.opacity = 0.5, dot.size = 2, label.core = FALSE, label.size = NULL, label.opacity = NULL, label.color = "grey70", mean.abund.thres = NULL, mean.prev.thres = NULL, log.scale = TRUE, nudge.label = NULL, ... )
x |
|
lower.conf |
Lower confidence interval =0.025 |
upper.conf |
Upper confidence interval =0.975 |
bs.iter |
Number of bootstrap iterations =99 |
color |
taxa level to color. Preferably at phylum or just a single color |
dot.opacity |
Numeric for ggplot alpha. Default is 0.5 |
dot.size |
Numeric size of point |
label.core |
Logical default is FALSE |
label.size |
If label_core is TRUE specify text size. Default is NULL |
label.opacity |
Numeric for ggplot alpha. Defualt is NULL |
label.color |
Color for labels. Default="grey70" |
mean.abund.thres |
If label_core is TRUE specify mean abundance threshold. Default is NULL |
mean.prev.thres |
If label_core is TRUE specify mean prevalence threshold. Default is NULL |
log.scale |
Plot log10 scale. Default is TRUE abundance criteria. Default is NULL |
nudge.label |
Argument to pass to ggrepel::geom_text_repel Default is NULL |
... |
Arguments to pass to sample() function. |
Check if there are spurious OTUs/ASVs.
A ggplot plot object.
## Not run: # Example data library(microbiomeutilities) asv_ps <- zackular2014 asv_ps <- microbiome::transform(asv_ps, "compositional") asv_ps <- core(asv_ps, detection = 0.0001, prevalence = 0.5) asv_ps <- format_to_besthit(asv_ps) set.seed(2349) p_v <- plot_abund_prev(asv_ps, size = 20, replace = TRUE) + geom_vline(xintercept = 0.75, lty = "dashed", alpha = 0.7) + geom_hline(yintercept = 0.01, lty = "dashed", alpha = 0.7) + scale_color_brewer(palette = "Paired") p_v ## End(Not run)## Not run: # Example data library(microbiomeutilities) asv_ps <- zackular2014 asv_ps <- microbiome::transform(asv_ps, "compositional") asv_ps <- core(asv_ps, detection = 0.0001, prevalence = 0.5) asv_ps <- format_to_besthit(asv_ps) set.seed(2349) p_v <- plot_abund_prev(asv_ps, size = 20, replace = TRUE) + geom_vline(xintercept = 0.75, lty = "dashed", alpha = 0.7) + geom_hline(yintercept = 0.01, lty = "dashed", alpha = 0.7) + scale_color_brewer(palette = "Paired") p_v ## End(Not run)
microbiome packageUtility plot function for diversity measures calcualted by microbiome package.
plot_alpha_diversities( x, type, index.val = "all", plot.type, variableA, palette )plot_alpha_diversities( x, type, index.val = "all", plot.type, variableA, palette )
x |
|
type |
Either alpha (Diversity Index) or dominance (Dominance Index) or evenness (Evenness Index) |
index.val |
see global function in |
plot.type |
Three optons c("stripchart", "boxplot", "violin") |
variableA |
Variable of interested to be checked. This will also be used to color the plot |
palette |
Any of the |
Uses the microbiome package global function to calculate diversities and then returns
a plot.
ggplot object. This can be further modified using ggpubr
library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p <- plot_alpha_diversities(p0, type = "dominance", index.val = "all", plot.type = "stripchart", variableA = "DiseaseState", palette = "jco" ) print(p)library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p <- plot_alpha_diversities(p0, type = "dominance", index.val = "all", plot.type = "stripchart", variableA = "DiseaseState", palette = "jco" ) print(p)
Calculates alpha diversity idenx at varying sampling units (sequencing depth).
plot_alpha_rcurve( x, index = "observed", subsamples = c(100, 1000, 2000, 3000, 4000, 5000), lower.conf = 0.025, upper.conf = 0.975, group = NULL, linetype.main = 1, line.opacity.main = 0.5, linetype.type = 2, line.opacity.type = 0.25, type = "CI", label.min = TRUE, label.size = 3, label.color = "grey70" )plot_alpha_rcurve( x, index = "observed", subsamples = c(100, 1000, 2000, 3000, 4000, 5000), lower.conf = 0.025, upper.conf = 0.975, group = NULL, linetype.main = 1, line.opacity.main = 0.5, linetype.type = 2, line.opacity.type = 0.25, type = "CI", label.min = TRUE, label.size = 3, label.color = "grey70" )
x |
|
index |
Default:: "observed", |
subsamples |
Default: c(100,1000, 2000, 3000, 4000, 5000) |
lower.conf |
Default: 0.025. If type=CI |
upper.conf |
Default: 0.975. |
group |
Default: NULL |
linetype.main |
For ggplot line type for line by group. Default: 1 |
line.opacity.main |
For ggplot alpha to determine opacity for line by group. Default: 0.5 |
linetype.type |
For ggplot line type for line CI or SD. Default: 2 |
line.opacity.type |
For ggplot line type to determine opacity for line CI or SD. Default: 0.25 |
type |
Either CI (confidence interval) or SD (Standard deviation) Default: CI |
label.min |
TRUE or FALSE. Default: TRUE |
label.size |
Label min size |
label.color |
Label min color |
## Not run: library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 # e.g. to make range of # subsamples <- seq(0, 5000, by=100)[-1] p <- plot_alpha_rcurve(p0, index="observed", lower.conf = 0.025, upper.conf = 0.975, group="DiseaseState") + scale_color_brewer(palette = "Paired") + scale_fill_brewer(palette = "Paired") print(p ) ## End(Not run)## Not run: library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 # e.g. to make range of # subsamples <- seq(0, 5000, by=100)[-1] p <- plot_alpha_rcurve(p0, index="observed", lower.conf = 0.025, upper.conf = 0.975, group="DiseaseState") + scale_color_brewer(palette = "Paired") + scale_fill_brewer(palette = "Paired") print(p ) ## End(Not run)
Create an area plot for longitudinal samples
with ggplot2 package.
plot_area( x, xvar = NULL, level = NULL, facet.by = NULL, fill.colors = brewer.pal(6, "Paired"), abund.thres = 0.001, prev.thres = 0.5, ncol = 5, nrow = 5 )plot_area( x, xvar = NULL, level = NULL, facet.by = NULL, fill.colors = brewer.pal(6, "Paired"), abund.thres = 0.001, prev.thres = 0.5, ncol = 5, nrow = 5 )
x |
|
xvar |
Column name to plot on x-axis. |
level |
Taxonomic level. OTU/ASV level not supported. |
facet.by |
Column with variable that has multiple measurements. |
fill.colors |
brewer.pal(6,"Paired"). Specify colors. |
abund.thres |
= 0.001 check |
prev.thres |
= 0.1 check |
ncol |
wrap, specify number of columns. |
nrow |
wrap, specify number of rows. |
ggplot object.
## Not run: library(microbiomeutilities) data("hmp2") ps <- hmp2 ps.rel <- microbiome::transform(ps, "compositional") p <- plot_area(ps.rel, xvar="visit_number", level = "Phylum", facet.by = "subject_id", fill.colors=brewer.pal(6,"Paired")) ## End(Not run)## Not run: library(microbiomeutilities) data("hmp2") ps <- hmp2 ps.rel <- microbiome::transform(ps, "compositional") p <- plot_area(ps.rel, xvar="visit_number", level = "Phylum", facet.by = "subject_id", fill.colors=brewer.pal(6,"Paired")) ## End(Not run)
Diversity plot with stats
plot_diversity_stats( x, index, group = NULL, group.colors = c("brown3", "steelblue"), dot.opacity = 0.25, box.opacity = 0.25, violin.opacity = 0.5, group.order = NULL, stats = TRUE, label.format = "p.format", ... )plot_diversity_stats( x, index, group = NULL, group.colors = c("brown3", "steelblue"), dot.opacity = 0.25, box.opacity = 0.25, violin.opacity = 0.5, group.order = NULL, stats = TRUE, label.format = "p.format", ... )
x |
|
index |
diversity index. Calculated using microbiome::alpha |
group |
Grouping variable to compare |
group.colors |
Colors for plotting groups |
dot.opacity |
for ggplot alpha to determine opacity for points |
box.opacity |
for ggplot alpha to determine opacity for box |
violin.opacity |
for ggplot alpha to determine opacity for violin |
group.order |
Default is NULL. a list specifing order of x-axis. |
stats |
Logical TRUE or FALSE. Calls ggpubr::stat_compare_means. |
label.format |
For ggpubr::stat_compare_means "p.signif" E.g. c("H","CRC","nonCRC") |
... |
params for ggpubr::stat_compare_means |
## Not run: library(microbiomeutilities) library(ggpubr) data("zackular2014") p0 <- zackular2014 mycols <- c("brown3", "steelblue", "grey50") p.m <- plot_diversity_stats(p0, group = "DiseaseState", index = "diversity_shannon", group.order = c("H", "CRC", "nonCRC"), group.colors = mycols ) print(p.m) ## End(Not run)## Not run: library(microbiomeutilities) library(ggpubr) data("zackular2014") p0 <- zackular2014 mycols <- c("brown3", "steelblue", "grey50") p.m <- plot_diversity_stats(p0, group = "DiseaseState", index = "diversity_shannon", group.order = c("H", "CRC", "nonCRC"), group.colors = mycols ) print(p.m) ## End(Not run)
User specifed OTUs are plotted.
plot_listed_taxa( x, select.taxa, group, group.colors, dot.opacity = 0.25, box.opacity = 0.25, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, panel.arrange = "grid", ncol = NULL, nrow = NULL )plot_listed_taxa( x, select.taxa, group, group.colors, dot.opacity = 0.25, box.opacity = 0.25, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, panel.arrange = "grid", ncol = NULL, nrow = NULL )
x |
|
select.taxa |
a character list of taxa to be plotted. eg. select.taxa <- c("OTU-370251", "OTU-311173", "OTU-341024"). |
group |
Grouping variable to compare |
group.colors |
Colors for plotting groups |
dot.opacity |
For ggplot alpha to determine opacity for points |
box.opacity |
For ggplot alpha to determine opacity for box |
group.order |
Default is NULL. a list specifing order of x-axis. |
add.violin |
Loical. If half violoin to the added. Default=TRUE |
violin.opacity |
If add.violin=TRUE, opacity for violin. |
panel.arrange |
panels "grid" or "wrap" ggplot's facet_XXX |
ncol |
if wrap, specify number of columns. |
nrow |
if wrap, specify number of rows. |
Useful for instances where user is interested only in some OTUs. For example OTUs reported to be significantly diferent. This can also be used at higher taxonomic levels, output from phyloseq::tax_glom or microbiome::aggregate_taxa.
ggplot object. This can be further modified using ggpubr.
## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0) select.taxa <- c("OTU-d__denovo31:Dorea", "OTU-d__denovo24:Blautia") mycols <- c("brown3", "steelblue", "grey50") p <- plot_listed_taxa(p0.f, select.taxa, group = "DiseaseState", add.violin = TRUE, group.colors = mycols ) print(p) ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0) select.taxa <- c("OTU-d__denovo31:Dorea", "OTU-d__denovo24:Blautia") mycols <- c("brown3", "steelblue", "grey50") p <- plot_listed_taxa(p0.f, select.taxa, group = "DiseaseState", add.violin = TRUE, group.colors = mycols ) print(p) ## End(Not run)
This function extends the plot_ordination function of phyloseqto highlight the top taxa loadings
on the species ordination.
plot_ordination_utils( x, ordiObject, color.opt = NULL, plot.arrow = TRUE, scale.arrow = NULL, top.taxa = 5 )plot_ordination_utils( x, ordiObject, color.opt = NULL, plot.arrow = TRUE, scale.arrow = NULL, top.taxa = 5 )
x |
|
ordiObject |
Output of ordinate from package phyloseq. Only NMDS/CCA and Bray supported. |
color.opt |
Variable of interest from metadata. |
plot.arrow |
If arrow should be plotted for species either TRUE or FALSE. |
scale.arrow |
If arrow is plotted a constant to multiply axis values for clearing visualisations. |
top.taxa |
Top varying taxa to plot, default is 5. |
This function is useful for visualizing specifc taxa that could be important in explaining variations in ordinations.
plot
## Not run: library(microbiomeutilities) library(RColorBrewer) data("zackular2014") ps1 <- zackular2014 ps2 <- tax_glom(ps1, "Genus") ps2f <- format_to_besthit(ps2) orddi <- ordinate(ps2f, method = "CCA", distance = "bray") p <- plot_ordination_utils(ps2f, orddi, color = "DiseaseState", plot.arrow = TRUE, scale.arrow = 1.3, top.taxa = 5 ) print(p) ## End(Not run)## Not run: library(microbiomeutilities) library(RColorBrewer) data("zackular2014") ps1 <- zackular2014 ps2 <- tax_glom(ps1, "Genus") ps2f <- format_to_besthit(ps2) orddi <- ordinate(ps2f, method = "CCA", distance = "bray") p <- plot_ordination_utils(ps2f, orddi, color = "DiseaseState", plot.arrow = TRUE, scale.arrow = 1.3, top.taxa = 5 ) print(p) ## End(Not run)
This function will plot the ordaination along with highligthing the core microbes on the species ordination.
plot_ordiplot_core( x, ordiObject, prevalences, detections, min.prevalence, color.opt, shape, Samples = c(TRUE, FALSE) )plot_ordiplot_core( x, ordiObject, prevalences, detections, min.prevalence, color.opt, shape, Samples = c(TRUE, FALSE) )
x |
|
ordiObject |
Output of ordinate from package phyloseq. Only NMDS and Bray supported. |
prevalences |
Prevalences as supported by microbiome package. |
detections |
Detections as supported by microbiome package. |
min.prevalence |
Minimum prevalence value to plot. |
color.opt |
Variable of interest from metadata. |
shape |
Variable of interest from metadata. |
Samples |
c("TRUE" or "FALSE") |
This function is useful for visualizing core taxa in a 2D ordination plot.
plot
## Not run: library(microbiomeutilities) library(RColorBrewer) data("zackular2014") p0 <- zackular2014 ps1 <- format_to_besthit(p0) ps1 <- subset_samples(ps1, DiseaseState == "H") ps1 <- prune_taxa(taxa_sums(ps1) > 0, ps1) prev.thres <- seq(.05, 1, .05) det.thres <- 10^seq(log10(1e-4), log10(.2), length = 10) pseq.rel <- microbiome::transform(ps1, "compositional") ord.bray <- ordinate(pseq.rel, "NMDS", "bray") p <- plot_ordiplot_core(pseq.rel, ord.bray, prev.thres, det.thres, min.prevalence = 0.9, color.opt = "DiseaseState", shape = NULL, Sample = TRUE ) p ## End(Not run)## Not run: library(microbiomeutilities) library(RColorBrewer) data("zackular2014") p0 <- zackular2014 ps1 <- format_to_besthit(p0) ps1 <- subset_samples(ps1, DiseaseState == "H") ps1 <- prune_taxa(taxa_sums(ps1) > 0, ps1) prev.thres <- seq(.05, 1, .05) det.thres <- 10^seq(log10(1e-4), log10(.2), length = 10) pseq.rel <- microbiome::transform(ps1, "compositional") ord.bray <- ordinate(pseq.rel, "NMDS", "bray") p <- plot_ordiplot_core(pseq.rel, ord.bray, prev.thres, det.thres, min.prevalence = 0.9, color.opt = "DiseaseState", shape = NULL, Sample = TRUE ) p ## End(Not run)
User specified taxa are plotted.
plot_paired_abundances( x, select.taxa = NULL, group = NULL, group.colors = NULL, dot.opacity = 0.25, dot.size = 2, add.box = FALSE, box.opacity = 0.25, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, ncol = NULL, nrow = NULL, line = NULL, line.down = "#7209b7", line.stable = "#8d99ae", line.up = "#14213d", line.na.value = "grey50", line.guide = "legend", line.opacity = 0.25, line.size = 1, jitter.width = 0 )plot_paired_abundances( x, select.taxa = NULL, group = NULL, group.colors = NULL, dot.opacity = 0.25, dot.size = 2, add.box = FALSE, box.opacity = 0.25, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, ncol = NULL, nrow = NULL, line = NULL, line.down = "#7209b7", line.stable = "#8d99ae", line.up = "#14213d", line.na.value = "grey50", line.guide = "legend", line.opacity = 0.25, line.size = 1, jitter.width = 0 )
x |
|
select.taxa |
a character list of taxa to be plotted. eg. select.taxa <- c("OTU-370251", "OTU-311173", "OTU-341024"). |
group |
Grouping variable to compare. x axis, eg. before-after, t1-t2. |
group.colors |
Colors for plotting groups. |
dot.opacity |
For ggplot alpha to determine opacity for points. |
dot.size |
For ggplot point size. |
add.box |
Logical. If boxplot to the added. Default=TRUE |
box.opacity |
For ggplot alpha to determine opacity for box. |
group.order |
Default is NULL. a list specifying order of x-axis. |
add.violin |
Logical. If half violin to the added. Default=TRUE |
violin.opacity |
If add.violin=TRUE, opacity for violin. |
ncol |
If 2 or more taxa to plot, specify number of columns. |
nrow |
If 2 or more taxa to plot, specify number of rows. |
line |
Variable to use for lines. E.g. "subject" before-after |
line.down |
Line Color for change when negative. Decreased abundance. |
line.stable |
Line Color for no change. |
line.up |
Line Color for change when positive. Increased abundance. |
line.na.value |
"grey50" for no/missing observations. |
line.guide |
"none" to plot guide for line. |
line.opacity |
Line opacity. |
line.size |
Size of line to plot. |
jitter.width |
Value to avoid over plotting by moving points. |
Useful for instances where user is interested only in some taxa and thier change after an intervention. This can also be used at higher taxonomic levels, output from phyloseq::tax_glom or microbiome::aggregate_taxa.
ggplot object. This can be further modified using ggpubr.
## Not run: library(microbiome) library(microbiomeutilities) library(gghalves) library(tidyr) data(peerj32) # Source: https://peerj.com/articles/32/ pseq <- peerj32$phyloseq # Ren pseq.rel <- microbiome::transform(pseq, "compositional") select.taxa <- c("Akkermansia", "Dialister") group.colors <- c("brown3", "steelblue", "grey70") p <- plot_paired_abundances(pseq.rel, select.taxa = select.taxa, group = "time", group.colors = group.colors, dot.opacity = 0.25, dot.size = 2, group.order = NULL, line = "subject" ) p ## End(Not run)## Not run: library(microbiome) library(microbiomeutilities) library(gghalves) library(tidyr) data(peerj32) # Source: https://peerj.com/articles/32/ pseq <- peerj32$phyloseq # Ren pseq.rel <- microbiome::transform(pseq, "compositional") select.taxa <- c("Akkermansia", "Dialister") group.colors <- c("brown3", "steelblue", "grey70") p <- plot_paired_abundances(pseq.rel, select.taxa = select.taxa, group = "time", group.colors = group.colors, dot.opacity = 0.25, dot.size = 2, group.order = NULL, line = "subject" ) p ## End(Not run)
Plots distribution of reads.
plot_read_distribution(x, groups, plot.type = c("density", "histogram"))plot_read_distribution(x, groups, plot.type = c("density", "histogram"))
x |
|
groups |
Metadata variable to check for groups based sequencing effort. |
plot.type |
Either density or histogram plot |
A ggplot plot object.
Contact: Sudarshan Shetty [email protected]
library(microbiome) data(zackular2014) ps0 <- zackular2014 p <- plot_read_distribution(ps0, groups = "DiseaseState", plot.type = "density") print(p)library(microbiome) data(zackular2014) ps0 <- zackular2014 p <- plot_read_distribution(ps0, groups = "DiseaseState", plot.type = "density") print(p)
User specifed OTUs are plotted.
plot_select_taxa( x, select.taxa, variableA, palette, plot.type, group.order = NULL )plot_select_taxa( x, select.taxa, variableA, palette, plot.type, group.order = NULL )
x |
|
select.taxa |
a character list of taxa to be plotted. eg. select.taxa <- c("OTU-370251", "OTU-311173", "OTU-341024"). |
variableA |
Variable of interested to be checked. This will also be used to color the plot. |
palette |
Any of the RColorBrewer plettes. |
plot.type |
Three optons c("stripchart", "boxplot", "violin") |
group.order |
Default is NULL. a list specifing order of x-axis. E.g. c("H","CRC","nonCRC") |
Useful for instances where user is interested only in some OTUs. For example OTUs reported to be significantly diferent.
ggplot object. This can be further modified using ggpubr.
## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0) select.taxa <- c("OTU-d__denovo31:Dorea", "OTU-d__denovo24:Blautia") p <- plot_select_taxa(p0.f, select.taxa, "DiseaseState", "Paired", plot.type = "stripchart") print(p) ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.f <- format_to_besthit(p0) select.taxa <- c("OTU-d__denovo31:Dorea", "OTU-d__denovo24:Blautia") p <- plot_select_taxa(p0.f, select.taxa, "DiseaseState", "Paired", plot.type = "stripchart") print(p) ## End(Not run)
Spaghetti Plots
plot_spaghetti( x, plot.var = "by_taxa", select.taxa = NULL, xvar = NULL, group = NULL, line.bg.color = "#8d99ae", bg.opacity = 0.5, focus.color = "brown3", ncol = NULL, nrow = NULL, focus.line.size = 0.5, line.size = 1 )plot_spaghetti( x, plot.var = "by_taxa", select.taxa = NULL, xvar = NULL, group = NULL, line.bg.color = "#8d99ae", bg.opacity = 0.5, focus.color = "brown3", ncol = NULL, nrow = NULL, focus.line.size = 0.5, line.size = 1 )
x |
|
plot.var |
One of "by_sample":Many sample one taxa or "by_taxa":Many taxa one sample |
select.taxa |
a character list of taxa to be plotted. eg. select.taxa <- c("OTU-370251", "OTU-311173", "OTU-341024"). |
xvar |
X-axis variable. This can be visit_number for participants in subject_id. |
group |
Column describing sample that have multiple measurements. For e.g. Subject_Ids which has all subject_ids listed. |
line.bg.color |
Line color for background. Default ="#8d99ae", |
bg.opacity |
Line opacity for background. |
focus.color |
Line color for focus being plotted. |
ncol |
if wrap, specify number of columns. |
nrow |
if wrap, specify number of rows. |
focus.line.size |
Line size for focus being plotted. |
line.size |
Line size for background. |
ggplot object. This can be further modified using ggpubr.
## Not run: # Example data library(microbiomeutilities) data("hmp2") pseq <- hmp2 # Ren pseq.rel <- microbiome::transform(pseq, "compositional") pseq.relF <- format_to_besthit(pseq.rel) # Choose one participant phdf.s <- subset_samples(pseq.relF, subject_id == "Participant_1") # Choose top 12 taxa to visualize ntax <- top_taxa(phdf.s, 12) phdf.s <- prune_taxa(ntax, phdf.s) plot_spaghetti(phdf.s, plot.var = "by_taxa", select.taxa = ntax, xvar = "visit_number", line.bg.color = "#8d99ae", focus.color = "#555b6e", ncol = 3, nrow = 4, line.size = 0.2 ) print(p) ## End(Not run)## Not run: # Example data library(microbiomeutilities) data("hmp2") pseq <- hmp2 # Ren pseq.rel <- microbiome::transform(pseq, "compositional") pseq.relF <- format_to_besthit(pseq.rel) # Choose one participant phdf.s <- subset_samples(pseq.relF, subject_id == "Participant_1") # Choose top 12 taxa to visualize ntax <- top_taxa(phdf.s, 12) phdf.s <- prune_taxa(ntax, phdf.s) plot_spaghetti(phdf.s, plot.var = "by_taxa", select.taxa = ntax, xvar = "visit_number", line.bg.color = "#8d99ae", focus.color = "#555b6e", ncol = 3, nrow = 4, line.size = 0.2 ) print(p) ## End(Not run)
Plot taxon abundance for samples.
plot_taxa_boxplot( x, taxonomic.level, top.otu, keep.other = FALSE, group, title, group.colors = NULL, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, box.opacity = 0.25, dot.opacity = 0.25, dot.size = 2 )plot_taxa_boxplot( x, taxonomic.level, top.otu, keep.other = FALSE, group, title, group.colors = NULL, group.order = NULL, add.violin = TRUE, violin.opacity = 0.25, box.opacity = 0.25, dot.opacity = 0.25, dot.size = 2 )
x |
|
taxonomic.level |
Merge the OTUs (for phyloseq object) into a higher taxonomic level. This has to be one from colnames(tax_table(x)). |
top.otu |
Top number of taxa to plot. |
keep.other |
TRUE or FALSE. Default is FALSE. This will not plot taxa group as Other |
group |
Specify main variable of interest. This should be one of the variables in sample_variables(x). |
title |
title for the plot |
group.colors |
Colors for plotting groups |
group.order |
Default is NULL. a list specifing order of x-axis. E.g. c("H","CRC","nonCRC") |
add.violin |
Loical. If half violoin to the added. Default=TRUE |
violin.opacity |
If add.violin=TRUE, opacity for violin. |
box.opacity |
For ggplot alpha to determine opacity for box |
dot.opacity |
For ggplot alpha to determine opacity for points |
dot.size |
For ggplot alpha to determine size for points |
A ggplot plot object.
## Not run: # Example data library(microbiomeutilities) library(RColorBrewer) data("zackular2014") ps0 <- zackular2014 mycols <- c("brown3", "steelblue", "grey50") pn <- plot_taxa_boxplot(ps0, taxonomic.level = "Phylum", top.otu = 6, group = "DiseaseState", title = "Relative abudance plot", keep.other = FALSE, group.order = c("H", "CRC", "nonCRC"), group.colors = mycols ) print(pn + theme_biome_utils()) ## End(Not run)## Not run: # Example data library(microbiomeutilities) library(RColorBrewer) data("zackular2014") ps0 <- zackular2014 mycols <- c("brown3", "steelblue", "grey50") pn <- plot_taxa_boxplot(ps0, taxonomic.level = "Phylum", top.otu = 6, group = "DiseaseState", title = "Relative abudance plot", keep.other = FALSE, group.order = c("H", "CRC", "nonCRC"), group.colors = mycols ) print(pn + theme_biome_utils()) ## End(Not run)
Plot taxon abundance for samples. It is a legacy function from microbiome.
plot_taxa_composition( x, sample.sort = NULL, taxonomic.level = "Phylum", transform = "compositional", otu.sort = NULL, palette = brewer.pal(12, "Paired"), x.label = "sample", plot.type = "barplot", average_by = NULL, verbose = FALSE, mar = c(5, 12, 1, 1), ... )plot_taxa_composition( x, sample.sort = NULL, taxonomic.level = "Phylum", transform = "compositional", otu.sort = NULL, palette = brewer.pal(12, "Paired"), x.label = "sample", plot.type = "barplot", average_by = NULL, verbose = FALSE, mar = c(5, 12, 1, 1), ... )
x |
|
sample.sort |
Order samples. Various criteria are available:
|
taxonomic.level |
Merge the OTUs (for phyloseq object) into a higher taxonomic level. This has to be one from colnames(tax_table(x)). |
transform |
Data transform to be used in plotting (but not in sample/taxon ordering). The options are 'Z-OTU', 'Z-Sample', 'log10' and 'compositional'. See the |
otu.sort |
Order taxa. Same options as for the sample.sort argument but instead of metadata, taxonomic table is used. Also possible to sort by 'abundance'. |
palette |
The number and palette |
x.label |
Specify how to label the x axis. This should be one of the variables in sample_variables(x). |
plot.type |
Plot type: 'barplot' or 'lineplot'. |
average_by |
Variable to group. |
verbose |
verbose. |
mar |
Figure margins. |
... |
Arguments to be passed (for |
A ggplot plot object.
## Not run: # Example data library(microbiome) library(microbiomeutilities) data("biogeogut") pseq <- biogeogut plot_taxa_composition(pseq, taxonomic.level = "Phylum") ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeutilities) data("biogeogut") pseq <- biogeogut plot_taxa_composition(pseq, taxonomic.level = "Phylum") ## End(Not run)
Plots CV for OTUs/ASVs.
plot_taxa_cv(x, plot.type)plot_taxa_cv(x, plot.type)
x |
|
plot.type |
scatter or hist (histogram) |
Check if there are spurious OTUs/ASVs.
A ggplot plot object.
## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p <- plot_taxa_cv(p0, plot.type = "hist") print(p) ## End(Not run)## Not run: # Example data library(microbiome) library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p <- plot_taxa_cv(p0, plot.type = "hist") print(p) ## End(Not run)
phyloseq-class and pheatmap
Plot heatmap using phyloseq-class object as input.
plot_taxa_heatmap( x, subset.top, transformation, VariableA, heatcolors = NULL, ... )plot_taxa_heatmap( x, subset.top, transformation, VariableA, heatcolors = NULL, ... )
x |
|
subset.top |
either NA or number of Top OTUs to use for plotting. |
transformation |
either 'log10', 'clr','Z', 'compositional', or NA |
VariableA |
main variable of Interest. |
heatcolors |
is the option for colors in |
... |
Arguments to be passed |
A pheatmap plot object.
Sudarshan A. Shetty ([email protected])
library(microbiomeutilities) library(viridis) library(RColorBrewer) data("zackular2014") ps0 <- zackular2014 heat.sample <- plot_taxa_heatmap(ps0, subset.top = 20, VariableA = "DiseaseState", heatcolors = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100), transformation = "log10" )library(microbiomeutilities) library(viridis) library(RColorBrewer) data("zackular2014") ps0 <- zackular2014 heat.sample <- plot_taxa_heatmap(ps0, subset.top = 20, VariableA = "DiseaseState", heatcolors = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100), transformation = "log10" )
Create a table for ternary plot ggtern package.
prep_tern_otu(x, abund.thres = 1e-04, prev.thres = 0.1, group = NULL)prep_tern_otu(x, abund.thres = 1e-04, prev.thres = 0.1, group = NULL)
x |
|
abund.thres |
= 0.0001 check |
prev.thres |
= 0.1 check |
group |
Grouping variable to compare, for this plot there has to be three groups in the data |
Plots the mean relative abundance of taxa in 3 groups being compared.
tibble object.
# library(microbiome) # library(microbiomeutilities) # library(dplyr) # data("zackular2014") # p0 <- zackular2014 # prep_tern_otu(p0, group="DiseaseState", # abund.thres=0.0001, prev.thres=0.25)# library(microbiome) # library(microbiomeutilities) # library(dplyr) # data("zackular2014") # p0 <- zackular2014 # prep_tern_otu(p0, group="DiseaseState", # abund.thres=0.0001, prev.thres=0.25)
Create a table for ternary plot ggtern R package.
prep_ternary( x, abund.thres = 1e-04, prev.thres = 0.1, group = NULL, level = "lowest" )prep_ternary( x, abund.thres = 1e-04, prev.thres = 0.1, group = NULL, level = "lowest" )
x |
|
abund.thres |
= 0.0001 check |
prev.thres |
= 0.1 check |
group |
Grouping variable to compare, for this plot there has to be three groups in the data |
level |
= "Genus" Taxonomic level. If OTU/ASV level specify="lowest" Does not support phylum level aggregation |
Plots the mean relative abundance of taxa in 3 groups being compared.
Tibble object.
library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 prep_ternary(p0, group = "DiseaseState", abund.thres = 0.0001, level = "Genus", prev.thres = 0.25)library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 prep_ternary(p0, group = "DiseaseState", abund.thres = 0.0001, level = "Genus", prev.thres = 0.25)
phyloseq-class
Prints an overview phyloseq-class.
print_ps(x)print_ps(x)
x |
|
Prints information about the phyloseq-class object.
Contact: Sudarshan A. Shetty [email protected]
library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 print_ps(pseq)library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 print_ps(pseq)
Create a simple heatmap with ggplot2 package.
simple_heatmap( x, group.facet = "DiseaseState", group.order = c("H", "CRC", "nonCRC"), abund.thres = 0.01, prev.thres = 0.1, level = "Genus", scale.color = "log10", na.fill = "white", color.fill = NULL, taxa.arrange = TRUE, panel.arrange = NULL, remove.other = TRUE, ncol = NULL, nrow = NULL )simple_heatmap( x, group.facet = "DiseaseState", group.order = c("H", "CRC", "nonCRC"), abund.thres = 0.01, prev.thres = 0.1, level = "Genus", scale.color = "log10", na.fill = "white", color.fill = NULL, taxa.arrange = TRUE, panel.arrange = NULL, remove.other = TRUE, ncol = NULL, nrow = NULL )
x |
|
group.facet |
Variable to make facet/panel the plot. |
group.order |
Default is NULL. a list specifing order of x-axis. E.g. c("H","CRC","nonCRC") |
abund.thres |
= 0.01 check |
prev.thres |
= 0.1 check |
level |
= "Genus" Taxonomic level. OTU/ASV level not supported. Check plot_taxa_heatmap |
scale.color |
Scale the tiles colors "log10" or "sqrt" |
na.fill |
Color to fill NAs. e.g. "white" |
color.fill |
User specified color vectors. |
taxa.arrange |
Arrange the order of taxa. User can supply a list of vectors. |
panel.arrange |
panels "grid" or "wrap" ggplot's facet_XXX |
remove.other |
Rare clubbed as Other to be removed. Logical TRUE/FALSE. |
ncol |
if wrap, specify number of columns. |
nrow |
if wrap, specify number of rows. |
Wrapper converts phyloseq-class object to long data frame
and generates a heatmap.
ggplot object.
library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 p0.rel <- transform(p0, "compositional") p <- simple_heatmap(p0.rel, group.facet = "DiseaseState", group.order = c("H", "CRC", "nonCRC"), abund.thres = 0.01, prev.thres = 0.1, level = "Genus", scale.color = "log10", na.fill = "white", color.fill = NULL, taxa.arrange = TRUE, remove.other = TRUE, panel.arrange = "wrap", ncol = 2, nrow = 2 ) print(p)library(microbiome) library(microbiomeutilities) library(dplyr) data("zackular2014") p0 <- zackular2014 p0.rel <- transform(p0, "compositional") p <- simple_heatmap(p0.rel, group.facet = "DiseaseState", group.order = c("H", "CRC", "nonCRC"), abund.thres = 0.01, prev.thres = 0.1, level = "Genus", scale.color = "log10", na.fill = "white", color.fill = NULL, taxa.arrange = TRUE, remove.other = TRUE, panel.arrange = "wrap", ncol = 2, nrow = 2 ) print(p)
Plots distribution of taxa.
taxa_distribution( x, color.level = "Phylum", color.taxa = brewer.pal(12, "Paired") )taxa_distribution( x, color.level = "Phylum", color.taxa = brewer.pal(12, "Paired") )
x |
|
color.level |
Taxonomic level to color |
color.taxa |
vector of colors specified by user Default is brewer.pal(12,"Paired") |
ggplot2 object
library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 p <- taxa_distribution(pseq) plibrary(microbiomeutilities) data("zackular2014") pseq <- zackular2014 p <- taxa_distribution(pseq) p
Creates a list of dataframes at different taxonomic levels.
taxa_pooler_mcola(x)taxa_pooler_mcola(x)
x |
|
# library(phyloseq) # library(microbiome) # data("zackular2014") # sub.sm <- sample(sample_names(zackular2014), 20) # pseq <- prune_samples(sub.sm,zackular2014) # taxa_pooler_mcola(pseq)# library(phyloseq) # library(microbiome) # data("zackular2014") # sub.sm <- sample(sample_names(zackular2014), 20) # pseq <- prune_samples(sub.sm,zackular2014) # taxa_pooler_mcola(pseq)
Data frame with mean, max, median standard deviation of relative abundance.
taxa_summary(x, level)taxa_summary(x, level)
x |
|
level |
Taxonomic level for which summary is required |
returns a data frame with relative abundance summary.
Contact: Sudarshan A. Shetty [email protected]
## Not run: # Example data library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.rel <- microbiome::transform(p0, "compositional") tx.sum1 <- taxa_summary(p0, "Phylum") tx.sum2 <- taxa_summary(p0.rel, "Phylum") ## End(Not run)## Not run: # Example data library(microbiomeutilities) data("zackular2014") p0 <- zackular2014 p0.rel <- microbiome::transform(p0, "compositional") tx.sum1 <- taxa_summary(p0, "Phylum") tx.sum2 <- taxa_summary(p0.rel, "Phylum") ## End(Not run)
Opiniated elegant theme.
theme_biome_utils()theme_biome_utils()
Data from a Zackular, Joseph P., et al. "The gut microbiome modulates colon tumorigenesis." MBio 4.6 (2013): e00692 -13.
data("zackular2014")data("zackular2014")
An object of class "phyloseq".
Zackular, Joseph P., et al. "The gut microbiome modulates colon tumorigenesis." MBio 4.6 (2013): e00692-13., https://mbio.asm.org/content/4/6/e00692-13.short
## Not run: library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 print(zackular2014) ## End(Not run)## Not run: library(microbiomeutilities) data("zackular2014") pseq <- zackular2014 print(zackular2014) ## End(Not run)