1. Anatomy of a MPSE
MicrobiotaProcess introduces MPSE S4 class. This class inherits the SummarizedExperiment(Morgan et al. 2021) class. Here, the assays slot is used to store the rectangular abundance matrices of features for a microbiome experimental results. The colData slot is used to store the meta-data of sample and some results about samples in the downstream analysis. The rowData is used to store the meta-data of features and some results about the features in the downstream analysis. Compared to the SummarizedExperiment object, MPSE introduces the following additional slots:
- taxatree: is a
treedata(Wang et al. 2020; Yu 2021) class contained phylo class (hierarchical structure) and tibble class (associated data) to store the taxonomy information, the tip labels of taxonomy tree are the rows of theassays, but the internal node labels contain the differences level taxonomy of the rows of theassays. The tibble class contains the taxonomy classification of node labels. - otutree: is also a
treedataclass to store the phylogenetic tree (based with reference sequences) and the associated data, which its tip labels are also the rows of the assays. - refseq: is a
XStringSet(Pagès et al. 2021) class contained reference sequences, which its names are also identical with the rows of the assays.
The structure of the MPSE class.
2. Overview of the design of MicrobiotaProcess package
With this data structure, MicrobiotaProcess will be more interoperable with the existing computing ecosystem. For example, the slots inherited SummarizedExperiment can be extracted via the methods provided by SummarizedExperiment. The taxatree and otutree can also be extracted via mp_extract_tree, and they are compatible with ggtree(Yu et al. 2017), ggtreeExtra(Xu et al. 2021), treeio(Wang et al. 2020) and tidytree(Yu 2021) ecosystem since they are all treedata class, which is a data structure used directly by these packages.
Moreover, the results of upstream analysis of microbiome based some tools, such as qiime2(Bolyen et al. 2019), dada2(Callahan et al. 2016) and MetaPhlAn(Beghini et al. 2021) or other classes (SummarizedExperiment(Morgan et al. 2021), phyloseq(McMurdie and Holmes 2013) and TreeSummarizedExperiment(Huang et al. 2021)) used to store the result of microbiome can be loaded or transformed to the MPSE class.
In addition, MicrobiotaProcess also introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome analysis procedures under a unified and common framework (tidy-like framework). We believe MicrobiotaProcess can improve the efficiency of related researches, and it also bridges microbiome data analysis with the tidyverse(Wickham et al. 2019).
The Overview of the design of MicrobiotaProcess package
3. MicrobiotaProcess profiling
3.1 bridges other tools
MicrobiotaProcess provides several functions to parsing the output of upstream analysis tools of microbiome, such as qiime2(Bolyen et al. 2019), dada2(Callahan et al. 2016) and MetaPhlAn(Beghini et al. 2021), and return MPSE object. Some bioconductor class, such as phyloseq(McMurdie and Holmes 2013), TreeSummarizedExperiment(Huang et al. 2021) and SummarizedExperiment(Morgan et al. 2021) can also be converted to MPSE via as.MPSE().
library(MicrobiotaProcess)
#parsing the output of dada2
seqtabfile <- system.file("extdata", "seqtab.nochim.rds", package="MicrobiotaProcess")
seqtab <- readRDS(seqtabfile)
taxafile <- system.file("extdata", "taxa_tab.rds", package="MicrobiotaProcess")
taxa <- readRDS(taxafile)
# the seqtab and taxa are output of dada2
sampleda <- system.file("extdata", "mouse.time.dada2.txt", package="MicrobiotaProcess")
mpse1 <- mp_import_dada2(seqtab=seqtab, taxatab=taxa, sampleda=sampleda)
mpse1## # A MPSE-tibble (MPSE object) abstraction: 4,408 × 11
## # OTU=232 | Samples=19 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance time Kingdom Phylum Class Order Family Genus Species
## <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 OTU_1 F3D0 579 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 2 OTU_2 F3D0 345 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 3 OTU_3 F3D0 449 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 4 OTU_4 F3D0 430 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 5 OTU_5 F3D0 154 Early k__Bac… p__Ba… c__B… o__B… f__Ba… g__B… s__un_…
## 6 OTU_6 F3D0 470 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 7 OTU_7 F3D0 282 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 8 OTU_8 F3D0 184 Early k__Bac… p__Ba… c__B… o__B… f__Ri… g__A… s__un_…
## 9 OTU_9 F3D0 45 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 10 OTU_10 F3D0 158 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## # ℹ 4,398 more rows# parsing the output of qiime2
otuqzafile <- system.file("extdata", "table.qza", package="MicrobiotaProcess")
taxaqzafile <- system.file("extdata", "taxa.qza", package="MicrobiotaProcess")
mapfile <- system.file("extdata", "metadata_qza.txt", package="MicrobiotaProcess")
mpse2 <- mp_import_qiime2(otuqza=otuqzafile, taxaqza=taxaqzafile, mapfilename=mapfile)
mpse2## # A MPSE-tibble (MPSE object) abstraction: 12,006 × 32
## # OTU=138 | Samples=87 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance Sample_Name_s BarcodeSequence LinkerPrimerSequence
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 OTU_1 ERR13318… 901 LR53 AGTGTCGATTCG TATGGTAATTGT
## 2 OTU_2 ERR13318… 877 LR53 AGTGTCGATTCG TATGGTAATTGT
## 3 OTU_3 ERR13318… 239 LR53 AGTGTCGATTCG TATGGTAATTGT
## 4 OTU_4 ERR13318… 201 LR53 AGTGTCGATTCG TATGGTAATTGT
## 5 OTU_5 ERR13318… 168 LR53 AGTGTCGATTCG TATGGTAATTGT
## 6 OTU_6 ERR13318… 115 LR53 AGTGTCGATTCG TATGGTAATTGT
## 7 OTU_7 ERR13318… 107 LR53 AGTGTCGATTCG TATGGTAATTGT
## 8 OTU_8 ERR13318… 84 LR53 AGTGTCGATTCG TATGGTAATTGT
## 9 OTU_9 ERR13318… 67 LR53 AGTGTCGATTCG TATGGTAATTGT
## 10 OTU_10 ERR13318… 67 LR53 AGTGTCGATTCG TATGGTAATTGT
## # ℹ 11,996 more rows
## # ℹ 26 more variables: Subject <chr>, Sex <chr>, Age <int>, Pittsburgh <chr>,
## # Bell <dbl>, BMI <dbl>, sCD14ugml <dbl>, LBPugml <dbl>, LPSpgml <dbl>,
## # IFABPpgml <dbl>, Physical_functioning <dbl>, Role_physical <dbl>,
## # Role_emotional <dbl>, Energy_fatigue <dbl>, Emotional_well_being <dbl>,
## # Social_functioning <dbl>, Pain <dbl>, General_health <dbl>,
## # Description <lgl>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>, …# parsing the output of MetaPhlAn
file1 <- system.file("extdata/MetaPhlAn", "metaphlan_test.txt", package="MicrobiotaProcess")
sample.file <- system.file("extdata/MetaPhlAn", "sample_test.txt", package="MicrobiotaProcess")
mpse3 <- mp_import_metaphlan(profile=file1, mapfilename=sample.file)
mpse3## # A MPSE-tibble (MPSE object) abstraction: 5,260 × 11
## # OTU=263 | Samples=20 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus
## OTU Sample Abundance group taxid Kingdom Phylum Class Order Family Genus
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 s__Meth… GupDM… 0.596 testA 2157… k__Arc… p__Eu… c__M… o__M… f__Me… g__M…
## 2 s__Acti… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__A… f__Ac… g__A…
## 3 s__Acti… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__A… f__Ac… g__A…
## 4 s__Acti… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__A… f__Ac… g__A…
## 5 s__Bifi… GupDM… 0.948 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## 6 s__Bifi… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## 7 s__Bifi… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## 8 s__Bifi… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## 9 s__Bifi… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## 10 s__Bifi… GupDM… 0 testA 2|20… k__Bac… p__Ac… c__A… o__B… f__Bi… g__B…
## # ℹ 5,250 more rows# convert phyloseq object to mpse
#library(phyloseq)
#data(esophagus)
#esophagus
#mpse4 <- esophagus %>% as.MPSE()
#mpse4
# convert TreeSummarizedExperiment object to mpse
# library(curatedMetagenomicData)
# tse <- curatedMetagenomicData::curatedMetagenomicData("ZhuF_2020.relative_abundance", dryrun=F)
# tse[[1]] %>% as.MPSE() -> mpse5
# mpse53.2 alpha diversity analysis
Rarefaction, based on sampling technique, was used to compensate for the effect of sample size on the number of units observed in a sample(Siegel 2004). MicrobiotaProcess provided mp_cal_rarecurve and mp_plot_rarecurve to calculate and plot the curves based on rrarefy of vegan(Oksanen et al. 2019).
## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 11
## # OTU=218 | Samples=19 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance time Kingdom Phylum Class Order Family Genus Species
## <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 OTU_1 F3D0 579 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 2 OTU_2 F3D0 345 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 3 OTU_3 F3D0 449 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 4 OTU_4 F3D0 430 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 5 OTU_5 F3D0 154 Early k__Bac… p__Ba… c__B… o__B… f__Ba… g__B… s__un_…
## 6 OTU_6 F3D0 470 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 7 OTU_7 F3D0 282 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 8 OTU_8 F3D0 184 Early k__Bac… p__Ba… c__B… o__B… f__Ri… g__A… s__un_…
## 9 OTU_9 F3D0 45 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## 10 OTU_10 F3D0 158 Early k__Bac… p__Ba… c__B… o__B… f__Mu… g__u… s__un_…
## # ℹ 4,132 more rows# Rarefied species richness
mouse.time.mpse %<>% mp_rrarefy()
# 'chunks' represent the split number of each sample to calculate alpha
# diversity, default is 400. e.g. If a sample has total 40000
# reads, if chunks is 400, it will be split to 100 sub-samples
# (100, 200, 300,..., 40000), then alpha diversity index was
# calculated based on the sub-samples.
# '.abundance' the column name of abundance, if the '.abundance' is not be
# rarefied calculate rarecurve, user can specific 'force=TRUE'.
mouse.time.mpse %<>%
mp_cal_rarecurve(
.abundance = RareAbundance,
chunks = 400
)
# The RareAbundanceRarecurve column will be added the colData slot
# automatically (default action="add")
mouse.time.mpse %>% print(width=180)## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 13
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance | Taxonomy=Kingdom,
## # Phylum, Class, Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance time RareAbundanceRarecurve
## <chr> <chr> <int> <int> <chr> <list>
## 1 OTU_1 F3D0 579 214 Early <tibble [2,520 × 4]>
## 2 OTU_2 F3D0 345 116 Early <tibble [2,520 × 4]>
## 3 OTU_3 F3D0 449 179 Early <tibble [2,520 × 4]>
## 4 OTU_4 F3D0 430 167 Early <tibble [2,520 × 4]>
## 5 OTU_5 F3D0 154 54 Early <tibble [2,520 × 4]>
## 6 OTU_6 F3D0 470 174 Early <tibble [2,520 × 4]>
## 7 OTU_7 F3D0 282 115 Early <tibble [2,520 × 4]>
## 8 OTU_8 F3D0 184 74 Early <tibble [2,520 × 4]>
## 9 OTU_9 F3D0 45 16 Early <tibble [2,520 × 4]>
## 10 OTU_10 F3D0 158 59 Early <tibble [2,520 × 4]>
## Kingdom Phylum Class Order
## <chr> <chr> <chr> <chr>
## 1 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 2 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 3 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 4 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 5 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 6 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 7 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 8 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 9 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## 10 k__Bacteria p__Bacteroidetes c__Bacteroidia o__Bacteroidales
## Family Genus Species
## <chr> <chr> <chr>
## 1 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 2 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 3 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 4 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 5 f__Bacteroidaceae g__Bacteroides s__un_g__Bacteroides
## 6 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 7 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 8 f__Rikenellaceae g__Alistipes s__un_g__Alistipes
## 9 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## 10 f__Muribaculaceae g__un_f__Muribaculaceae s__un_f__Muribaculaceae
## # ℹ 4,132 more rows# default will display the confidence interval around smooth.
# se=TRUE
p1 <- mouse.time.mpse %>%
mp_plot_rarecurve(
.rare = RareAbundanceRarecurve,
.alpha = Observe,
)
p2 <- mouse.time.mpse %>%
mp_plot_rarecurve(
.rare = RareAbundanceRarecurve,
.alpha = Observe,
.group = time
) +
scale_color_manual(values=c("#00A087FF", "#3C5488FF")) +
scale_fill_manual(values=c("#00A087FF", "#3C5488FF"), guide="none")
# combine the samples belong to the same groups if
# plot.group=TRUE
p3 <- mouse.time.mpse %>%
mp_plot_rarecurve(
.rare = RareAbundanceRarecurve,
.alpha = "Observe",
.group = time,
plot.group = TRUE
) +
scale_color_manual(values=c("#00A087FF", "#3C5488FF")) +
scale_fill_manual(values=c("#00A087FF", "#3C5488FF"),guide="none")
p1 + p2 + p3
The rarefaction of samples or groups
3.3 calculate alpha index and visualization
Alpha diversity can be estimated the species richness and evenness of some species communities. MicrobiotaProcess provides mp_cal_alpha to calculate alpha index (Observe, Chao1, ACE, Shannon, Simpson and J (Pielou’s evenness)) and the mp_plot_alpha to visualize the result.
library(ggplot2)
library(MicrobiotaProcess)
mouse.time.mpse %<>%
mp_cal_alpha(.abundance=RareAbundance)
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 19
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance | Taxonomy=Kingdom,
## # Phylum, Class, Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance time RareAbundanceRarecurve Observe
## <chr> <chr> <int> <int> <chr> <list> <dbl>
## 1 OTU_1 F3D0 579 214 Early <tibble [2,520 × 4]> 104
## 2 OTU_2 F3D0 345 116 Early <tibble [2,520 × 4]> 104
## 3 OTU_3 F3D0 449 179 Early <tibble [2,520 × 4]> 104
## 4 OTU_4 F3D0 430 167 Early <tibble [2,520 × 4]> 104
## 5 OTU_5 F3D0 154 54 Early <tibble [2,520 × 4]> 104
## 6 OTU_6 F3D0 470 174 Early <tibble [2,520 × 4]> 104
## 7 OTU_7 F3D0 282 115 Early <tibble [2,520 × 4]> 104
## 8 OTU_8 F3D0 184 74 Early <tibble [2,520 × 4]> 104
## 9 OTU_9 F3D0 45 16 Early <tibble [2,520 × 4]> 104
## 10 OTU_10 F3D0 158 59 Early <tibble [2,520 × 4]> 104
## # ℹ 4,132 more rows
## # ℹ 12 more variables: Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>,
## # Pielou <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
## # Family <chr>, Genus <chr>, Species <chr>f1 <- mouse.time.mpse %>%
mp_plot_alpha(
.group=time,
.alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou)
) +
scale_fill_manual(values=c("#00A087FF", "#3C5488FF"), guide="none") +
scale_color_manual(values=c("#00A087FF", "#3C5488FF"), guide="none")
f2 <- mouse.time.mpse %>%
mp_plot_alpha(
.alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou)
)
f1 / f2
The alpha diversity comparison
Users can extract the result with mp_extract_sample() to extract the result of mp_cal_alpha and visualized the result manually, see the example of mp_cal_alpha.
3.4 The visualization of taxonomy abundance
MicrobiotaProcess provides the mp_cal_abundance, mp_plot_abundance to calculate and plot the composition of species communities. And the mp_extract_abundance can extract the abundance of specific taxonomy level. User can also extract the abundance table to perform external analysis such as visualize manually (see the example of mp_cal_abundance).
## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 19
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance | Taxonomy=Kingdom,
## # Phylum, Class, Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance time RareAbundanceRarecurve Observe
## <chr> <chr> <int> <int> <chr> <list> <dbl>
## 1 OTU_1 F3D0 579 214 Early <tibble [2,520 × 4]> 104
## 2 OTU_2 F3D0 345 116 Early <tibble [2,520 × 4]> 104
## 3 OTU_3 F3D0 449 179 Early <tibble [2,520 × 4]> 104
## 4 OTU_4 F3D0 430 167 Early <tibble [2,520 × 4]> 104
## 5 OTU_5 F3D0 154 54 Early <tibble [2,520 × 4]> 104
## 6 OTU_6 F3D0 470 174 Early <tibble [2,520 × 4]> 104
## 7 OTU_7 F3D0 282 115 Early <tibble [2,520 × 4]> 104
## 8 OTU_8 F3D0 184 74 Early <tibble [2,520 × 4]> 104
## 9 OTU_9 F3D0 45 16 Early <tibble [2,520 × 4]> 104
## 10 OTU_10 F3D0 158 59 Early <tibble [2,520 × 4]> 104
## # ℹ 4,132 more rows
## # ℹ 12 more variables: Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>,
## # Pielou <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
## # Family <chr>, Genus <chr>, Species <chr>mouse.time.mpse %<>%
mp_cal_abundance( # for each samples
.abundance = RareAbundance
) %>%
mp_cal_abundance( # for each groups
.abundance=RareAbundance,
.group=time
)
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 20
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample | Taxonomy=Kingdom, Phylum, Class, Order, Family,
## # Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySample time
## <chr> <chr> <int> <int> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 Early
## 2 OTU_2 F3D0 345 116 4.61 Early
## 3 OTU_3 F3D0 449 179 7.11 Early
## 4 OTU_4 F3D0 430 167 6.63 Early
## 5 OTU_5 F3D0 154 54 2.14 Early
## 6 OTU_6 F3D0 470 174 6.91 Early
## 7 OTU_7 F3D0 282 115 4.57 Early
## 8 OTU_8 F3D0 184 74 2.94 Early
## 9 OTU_9 F3D0 45 16 0.635 Early
## 10 OTU_10 F3D0 158 59 2.34 Early
## # ℹ 4,132 more rows
## # ℹ 14 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>, Family <chr>,
## # Genus <chr>, Species <chr># visualize the relative abundance of top 20 phyla for each sample.
p1 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance=RareAbundance,
.group=time,
taxa.class = Phylum,
topn = 20,
relative = TRUE
)
# visualize the abundance (rarefied) of top 20 phyla for each sample.
p2 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance=RareAbundance,
.group=time,
taxa.class = Phylum,
topn = 20,
relative = FALSE
)
p1 / p2
The relative abundance and abundance of phyla of all samples
The abundance of features also can be visualized by mp_plot_abundance with heatmap plot by setting geom='heatmap'.
h1 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance = RareAbundance,
.group = time,
taxa.class = Phylum,
relative = TRUE,
topn = 20,
geom = 'heatmap',
features.dist = 'euclidean',
features.hclust = 'average',
sample.dist = 'bray',
sample.hclust = 'average'
)
h2 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance = RareAbundance,
.group = time,
taxa.class = Phylum,
relative = FALSE,
topn = 20,
geom = 'heatmap',
features.dist = 'euclidean',
features.hclust = 'average',
sample.dist = 'bray',
sample.hclust = 'average'
)
# the character (scale or theme) of figure can be adjusted by set_scale_theme
# refer to the mp_plot_dist
aplot::plot_list(gglist=list(h1, h2), tag_levels="A")
The relative abundance and abundance of phyla of all samples
# visualize the relative abundance of top 20 phyla for each .group (time)
p3 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance=RareAbundance,
.group=time,
taxa.class = Phylum,
topn = 20,
plot.group = TRUE
)
# visualize the abundance of top 20 phyla for each .group (time)
p4 <- mouse.time.mpse %>%
mp_plot_abundance(
.abundance=RareAbundance,
.group= time,
taxa.class = Phylum,
topn = 20,
relative = FALSE,
plot.group = TRUE
)
p3 / p4
The relative abundance and abundance of phyla of groups
3.5 Beta diversity analysis
Beta diversity is used to quantify the dissimilarities between the communities (samples). Some distance indexes, such as Bray-Curtis index, Jaccard index, UniFrac (weighted or unweighted) index, are useful for or popular with the community ecologists. Many ordination methods are used to estimated the dissimilarities in community ecology. MicrobiotaProcess implements mp_cal_dist to calculate the common distance, and provided mp_plot_dist to visualize the result. It also provides several commonly-used ordination methods, such as PCA (mp_cal_pca), PCoA (mp_cal_pcoa), NMDS (mp_cal_nmds), DCA (mp_cal_dca), RDA (mp_cal_rda), CCA (mp_cal_cca), and a function (mp_envfit) fits environmental vectors or factors onto an ordination. Moreover, it also wraps several statistical analysis for the distance matrices, such as adonis (mp_adonis), anosim (mp_anosim), mrpp (mp_mrpp) and mantel (mp_mantel). All these functions are developed based on tidy-like framework, and provided unified grammar, we believe these functions will help users to do the ordination analysis more conveniently.
3.5.1 The distance between samples or groups
# standardization
# mp_decostand wraps the decostand of vegan, which provides
# many standardization methods for community ecology.
# default is hellinger, then the abundance processed will
# be stored to the assays slot.
mouse.time.mpse %<>%
mp_decostand(.abundance=Abundance)
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 21
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 14 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>, Family <chr>,
## # Genus <chr>, Species <chr># calculate the distance between the samples.
# the distance will be generated a nested tibble and added to the
# colData slot.
mouse.time.mpse %<>% mp_cal_dist(.abundance=hellinger, distmethod="bray")
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 22
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 15 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # bray <list>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
## # Family <chr>, Genus <chr>, Species <chr># mp_plot_dist provides there methods to visualize the distance between the samples or groups
# when .group is not provided, the dot heatmap plot will be return
p1 <- mouse.time.mpse %>% mp_plot_dist(.distmethod = bray)
p1
the distance between samples
# when .group is provided, the dot heatmap plot with group information will be return.
p2 <- mouse.time.mpse %>% mp_plot_dist(.distmethod = bray, .group = time)
# The scale or theme of dot heatmap plot can be adjusted using set_scale_theme function.
p2 %>% set_scale_theme(
x = scale_fill_manual(
values=c("orange", "deepskyblue"),
guide = guide_legend(
keywidth = 1,
keyheight = 0.5,
title.theme = element_text(size=8),
label.theme = element_text(size=6)
)
),
aes_var = time # specific the name of variable
) %>%
set_scale_theme(
x = scale_color_gradient(
guide = guide_legend(keywidth = 0.5, keyheight = 0.5)
),
aes_var = bray
) %>%
set_scale_theme(
x = scale_size_continuous(
range = c(0.1, 3),
guide = guide_legend(keywidth = 0.5, keyheight = 0.5)
),
aes_var = bray
)
The distance between samples with group information
# when .group is provided and group.test is TRUE, the comparison of different groups will be returned
p3 <- mouse.time.mpse %>% mp_plot_dist(.distmethod = bray, .group = time, group.test=TRUE, textsize=2)
p3 
The comparison of distance among the groups
3.5.2 The PCoA analysis
The distance can be used to do the ordination analysis, such as PCoA, NMDS, etc. Here, we only show the example of PCoA analysis, other ordinations can refer to the examples and the usages of the corresponding functions.
mouse.time.mpse %<>%
mp_cal_pcoa(.abundance=hellinger, distmethod="bray")
# The dimensions of ordination analysis will be added the colData slot (default).
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 25
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 18 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # bray <list>, `PCo1 (46.6%)` <dbl>, `PCo2 (13.31%)` <dbl>,
## # `PCo3 (8.22%)` <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>,
## # Order <chr>, Family <chr>, Genus <chr>, Species <chr># We also can perform adonis or anosim to check whether it is significant to the dissimilarities of groups.
mouse.time.mpse %<>%
mp_adonis(.abundance=hellinger, .formula=~time, distmethod="bray", permutations=9999, action="add")
mouse.time.mpse %>% mp_extract_internal_attr(name=adonis)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
##
## vegan::adonis2(formula = .formula, data = sampleda, permutations = permutations, method = distmethod)
## Df SumOfSqs R2 F Pr(>F)
## time 1 0.58216 0.44137 13.431 1e-04 ***
## Residual 17 0.73683 0.55863
## Total 18 1.31899 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(ggplot2)
p1 <- mouse.time.mpse %>%
mp_plot_ord(
.ord = pcoa,
.group = time,
.color = time,
.size = 1.2,
.alpha = 1,
ellipse = TRUE,
show.legend = FALSE # don't display the legend of stat_ellipse
) +
scale_fill_manual(values=c("#00A087FF", "#3C5488FF")) +
scale_color_manual(values=c("#00A087FF", "#3C5488FF"))
# The size of point also can be mapped to other variables such as Observe, or Shannon
# Then the alpha diversity and beta diversity will be displayed simultaneously.
p2 <- mouse.time.mpse %>%
mp_plot_ord(
.ord = pcoa,
.group = time,
.color = time,
.size = Observe,
.alpha = Shannon,
ellipse = TRUE,
show.legend = FALSE # don't display the legend of stat_ellipse
) +
scale_fill_manual(
values = c("#00A087FF", "#3C5488FF"),
guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=6.5))
) +
scale_color_manual(
values=c("#00A087FF", "#3C5488FF"),
guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=6.5))
) +
scale_size_continuous(
range=c(0.5, 3),
guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=6.5))
)
p1 + p2
The PCoA result
3.5.3 Hierarchical cluster analysis
The distance of samples can also be used to perform the hierarchical cluster analysis to estimated the dissimilarities of samples. MicrobiotaProcess presents mp_cal_clust to perform this analysis. It also is implemented with the tidy-like framework.
mouse.time.mpse %<>%
mp_cal_clust(
.abundance = hellinger,
distmethod = "bray",
hclustmethod = "average", # (UPGAE)
action = "add" # action is used to control which result will be returned
)
mouse.time.mpse## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 25
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 18 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # bray <list>, `PCo1 (46.6%)` <dbl>, `PCo2 (13.31%)` <dbl>,
## # `PCo3 (8.22%)` <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>,
## # Order <chr>, Family <chr>, Genus <chr>, Species <chr># if action = 'add', the result of hierarchical cluster will be added to the MPSE object
# mp_extract_internal_attr can extract it. It is a treedata object, so it can be visualized
# by ggtree.
sample.clust <- mouse.time.mpse %>% mp_extract_internal_attr(name='SampleClust')
sample.clust## 'treedata' S4 object'.
##
## ...@ phylo:
##
## Phylogenetic tree with 19 tips and 18 internal nodes.
##
## Tip labels:
## F3D0, F3D1, F3D141, F3D142, F3D143, F3D144, ...
##
## Rooted; includes branch lengths.
##
## with the following features available:
## '', 'time', 'RareAbundanceRarecurve', 'Observe', 'Chao1', 'ACE', 'Shannon',
## 'Simpson', 'Pielou', 'bray', 'PCo1 (46.6%)', 'PCo2 (13.31%)', 'PCo3 (8.22%)'.
##
## # The associated data tibble abstraction: 37 × 15
## # The 'node', 'label' and 'isTip' are from the phylo tree.
## node label isTip time RareAbundanceRarecurve Observe Chao1 ACE Shannon
## <int> <chr> <lgl> <chr> <list> <dbl> <dbl> <dbl> <dbl>
## 1 1 F3D0 TRUE Early <tibble [2,520 × 4]> 104 104. 105. 3.88
## 2 2 F3D1 TRUE Early <tibble [2,520 × 4]> 99 102 101. 3.97
## 3 3 F3D141 TRUE Late <tibble [2,520 × 4]> 74 74 74.2 3.41
## 4 4 F3D142 TRUE Late <tibble [2,520 × 4]> 48 48 48 3.12
## 5 5 F3D143 TRUE Late <tibble [2,520 × 4]> 56 56 56 3.29
## 6 6 F3D144 TRUE Late <tibble [2,520 × 4]> 47 47 47.2 2.98
## 7 7 F3D145 TRUE Late <tibble [2,520 × 4]> 71 73.1 74.0 3.12
## 8 8 F3D146 TRUE Late <tibble [2,520 × 4]> 83 84.5 83.8 3.60
## 9 9 F3D147 TRUE Late <tibble [2,520 × 4]> 97 107 106. 3.31
## 10 10 F3D148 TRUE Late <tibble [2,520 × 4]> 92 93.3 94.7 3.44
## # ℹ 27 more rows
## # ℹ 6 more variables: Simpson <dbl>, Pielou <dbl>, bray <list>,
## # `PCo1 (46.6%)` <dbl>, `PCo2 (13.31%)` <dbl>, `PCo3 (8.22%)` <dbl>library(ggtree)
p <- ggtree(sample.clust) +
geom_tippoint(aes(color=time)) +
geom_tiplab(as_ylab = TRUE) +
ggplot2::scale_x_continuous(expand=c(0, 0.01))
p
The hierarchical cluster result of samples
Since the result of hierarchical cluster is treedata object, so it is very easy to display the result with associated data. For example, we can display the result of hierarchical cluster and the abundance of specific taxonomy level to check whether some biological pattern can be found.
library(ggtreeExtra)
library(ggplot2)
phyla.tb <- mouse.time.mpse %>%
mp_extract_abundance(taxa.class=Phylum, topn=30)
# The abundance of each samples is nested, it can be flatted using the unnest of tidyr.
phyla.tb %<>% tidyr::unnest(cols=RareAbundanceBySample) %>% dplyr::rename(Phyla="label")
phyla.tb## # A tibble: 171 × 7
## Phyla nodeClass Sample RareAbundance RelRareAbundanceBySa…¹ time
## <fct> <chr> <chr> <int> <dbl> <chr>
## 1 p__Actinobacteria Phylum F3D0 15 0.596 Early
## 2 p__Actinobacteria Phylum F3D1 0 0 Early
## 3 p__Actinobacteria Phylum F3D141 11 0.437 Late
## 4 p__Actinobacteria Phylum F3D142 28 1.11 Late
## 5 p__Actinobacteria Phylum F3D143 10 0.397 Late
## 6 p__Actinobacteria Phylum F3D144 18 0.715 Late
## 7 p__Actinobacteria Phylum F3D145 6 0.238 Late
## 8 p__Actinobacteria Phylum F3D146 4 0.159 Late
## 9 p__Actinobacteria Phylum F3D147 15 0.596 Late
## 10 p__Actinobacteria Phylum F3D148 19 0.755 Late
## # ℹ 161 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 1 more variable: RareAbundanceBytime <list>p1 <- p +
geom_fruit(
data=phyla.tb,
geom=geom_col,
mapping = aes(x = RelRareAbundanceBySample,
y = Sample,
fill = Phyla
),
orientation = "y",
#offset = 0.4,
pwidth = 3,
axis.params = list(axis = "x",
title = "The relative abundance of phyla (%)",
title.size = 4,
text.size = 2,
vjust = 1),
grid.params = list()
)
p1
The hierarchical cluster result of samples and the abundance of Phylum
3.6 Biomarker discovery
The MicrobiotaProcess presents mp_diff_analysis for the biomarker discovery based on tidy-like framework. The rule of mp_diff_analysis is similar with the LEfSe(Nicola Segata and Huttenhower 2011). First, all features are tested whether values in different classes are differentially distributed. Second, the significantly different features are tested whether all pairwise comparisons between subclass in different classes distinctly consistent with the class trend. Finally, the significantly discriminative features are assessed by LDA (linear discriminant analysis) or rf(randomForest). However, mp_diff_analysis is more flexible. The test method of two step can be set by user, and we used the general fold change(Wirbel et al. 2019) and wilcox.test(default) to test whether all pairwise comparisons between subclass in different classes distinctly consistent with the class trend. And the result is stored to the treedata object, which can be processed and displayed via treeio(Wang et al. 2020), tidytree(Yu 2021), ggtree(Yu et al. 2017) and ggtreeExtra(Xu et al. 2021).
library(ggtree)
library(ggtreeExtra)
library(ggplot2)
library(MicrobiotaProcess)
library(tidytree)
library(ggstar)
library(forcats)
mouse.time.mpse %>% print(width=150)## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 25
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## RareAbundanceRarecurve Observe Chao1 ACE Shannon Simpson Pielou bray
## <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <list>
## 1 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 2 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 3 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 4 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 5 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 6 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 7 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 8 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 9 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## 10 <tibble [2,520 × 4]> 104 104. 105. 3.88 0.965 0.835 <tibble>
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 10 more variables: `PCo1 (46.6%)` <dbl>, `PCo2 (13.31%)` <dbl>, `PCo3 (8.22%)` <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
## # Family <chr>, Genus <chr>, Species <chr>mouse.time.mpse %<>%
mp_diff_analysis(
.abundance = RelRareAbundanceBySample,
.group = time,
first.test.alpha = 0.01
)
# The result is stored to the taxatree or otutree slot, you can use mp_extract_tree to extract the specific slot.
taxa.tree <- mouse.time.mpse %>%
mp_extract_tree(type="taxatree")
taxa.tree## 'treedata' S4 object'.
##
## ...@ phylo:
##
## Phylogenetic tree with 218 tips and 186 internal nodes.
##
## Tip labels:
## OTU_67, OTU_231, OTU_188, OTU_150, OTU_207, OTU_5, ...
## Node labels:
## r__root, k__Bacteria, p__Actinobacteria, p__Bacteroidetes, p__Cyanobacteria,
## p__Deinococcus-Thermus, ...
##
## Rooted; no branch lengths.
##
## with the following features available:
## 'nodeClass', 'nodeDepth', 'RareAbundanceBySample', 'RareAbundanceBytime',
## 'LDAupper', 'LDAmean', 'LDAlower', 'Sign_time', 'pvalue', 'fdr'.
##
## # The associated data tibble abstraction: 404 × 13
## # The 'node', 'label' and 'isTip' are from the phylo tree.
## node label isTip nodeClass nodeDepth RareAbundanceBySample
## <int> <chr> <lgl> <chr> <dbl> <list>
## 1 1 OTU_67 TRUE OTU 8 <tibble [19 × 4]>
## 2 2 OTU_231 TRUE OTU 8 <tibble [19 × 4]>
## 3 3 OTU_188 TRUE OTU 8 <tibble [19 × 4]>
## 4 4 OTU_150 TRUE OTU 8 <tibble [19 × 4]>
## 5 5 OTU_207 TRUE OTU 8 <tibble [19 × 4]>
## 6 6 OTU_5 TRUE OTU 8 <tibble [19 × 4]>
## 7 7 OTU_1 TRUE OTU 8 <tibble [19 × 4]>
## 8 8 OTU_2 TRUE OTU 8 <tibble [19 × 4]>
## 9 9 OTU_3 TRUE OTU 8 <tibble [19 × 4]>
## 10 10 OTU_4 TRUE OTU 8 <tibble [19 × 4]>
## # ℹ 394 more rows
## # ℹ 7 more variables: RareAbundanceBytime <list>, LDAupper <dbl>,
## # LDAmean <dbl>, LDAlower <dbl>, Sign_time <chr>, pvalue <dbl>, fdr <dbl># And the result tibble of different analysis can also be extracted
# with tidytree (>=0.3.5)
taxa.tree %>% select(label, nodeClass, LDAupper, LDAmean, LDAlower, Sign_time, pvalue, fdr) %>% dplyr::filter(!is.na(fdr))## # A tibble: 399 × 8
## label nodeClass LDAupper LDAmean LDAlower Sign_time pvalue fdr
## <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 OTU_67 OTU NA NA NA <NA> 0.00335 0.0194
## 2 OTU_231 OTU NA NA NA <NA> 0.343 0.408
## 3 OTU_188 OTU NA NA NA <NA> 0.563 0.634
## 4 OTU_150 OTU NA NA NA <NA> 0.235 0.355
## 5 OTU_207 OTU NA NA NA <NA> 0.878 0.894
## 6 OTU_5 OTU NA NA NA <NA> 0.568 0.634
## 7 OTU_1 OTU NA NA NA <NA> 0.744 0.773
## 8 OTU_2 OTU NA NA NA <NA> 0.437 0.515
## 9 OTU_3 OTU NA NA NA <NA> 0.437 0.515
## 10 OTU_4 OTU 4.41 4.38 4.36 Late 0.000444 0.00732
## # ℹ 389 more rows# Since taxa.tree is treedata object, it can be visualized by ggtree and ggtreeExtra
p1 <- ggtree(
taxa.tree,
layout="radial",
size = 0.3
) +
geom_point(
data = td_filter(!isTip),
fill="white",
size=1,
shape=21
)
# display the high light of phylum clade.
p2 <- p1 +
geom_hilight(
data = td_filter(nodeClass == "Phylum"),
mapping = aes(node = node, fill = label)
)
# display the relative abundance of features(OTU)
p3 <- p2 +
ggnewscale::new_scale("fill") +
geom_fruit(
data = td_unnest(RareAbundanceBySample),
geom = geom_star,
mapping = aes(
x = fct_reorder(Sample, time, .fun=min),
size = RelRareAbundanceBySample,
fill = time,
subset = RelRareAbundanceBySample > 0
),
starshape = 13,
starstroke = 0.25,
offset = 0.04,
pwidth = 0.8,
grid.params = list(linetype=2)
) +
scale_size_continuous(
name="Relative Abundance (%)",
range = c(.5, 3)
) +
scale_fill_manual(values=c("#1B9E77", "#D95F02"))
# display the tip labels of taxa tree
p4 <- p3 + geom_tiplab(size=2, offset=7.2)
# display the LDA of significant OTU.
p5 <- p4 +
ggnewscale::new_scale("fill") +
geom_fruit(
geom = geom_col,
mapping = aes(
x = LDAmean,
fill = Sign_time,
subset = !is.na(LDAmean)
),
orientation = "y",
offset = 0.3,
pwidth = 0.5,
axis.params = list(axis = "x",
title = "Log10(LDA)",
title.height = 0.01,
title.size = 2,
text.size = 1.8,
vjust = 1),
grid.params = list(linetype = 2)
)
# display the significant (FDR) taxonomy after kruskal.test (default)
p6 <- p5 +
ggnewscale::new_scale("size") +
geom_point(
data=td_filter(!is.na(Sign_time)),
mapping = aes(size = -log10(fdr),
fill = Sign_time,
),
shape = 21,
) +
scale_size_continuous(range=c(1, 3)) +
scale_fill_manual(values=c("#1B9E77", "#D95F02"))
p6 + theme(
legend.key.height = unit(0.3, "cm"),
legend.key.width = unit(0.3, "cm"),
legend.spacing.y = unit(0.02, "cm"),
legend.text = element_text(size = 7),
legend.title = element_text(size = 9),
)
The different species and the abundance of sample
The visualization methods of result can be various, you can refer to the article or book of ggtree(Yu et al. 2017, 2018) and the article of ggtreeExtra(Xu et al. 2021). In addition, we also developed mp_plot_diff_res to display the result of mp_diff_analysis, it can decreases coding burden.
## # A MPSE-tibble (MPSE object) abstraction: 4,142 × 31
## # OTU=218 | Samples=19 | Assays=Abundance, RareAbundance,
## # RelRareAbundanceBySample, hellinger | Taxonomy=Kingdom, Phylum, Class,
## # Order, Family, Genus, Species
## OTU Sample Abundance RareAbundance RelRareAbundanceBySam…¹ hellinger time
## <chr> <chr> <int> <int> <dbl> <dbl> <chr>
## 1 OTU_1 F3D0 579 214 8.50 0.298 Early
## 2 OTU_2 F3D0 345 116 4.61 0.230 Early
## 3 OTU_3 F3D0 449 179 7.11 0.262 Early
## 4 OTU_4 F3D0 430 167 6.63 0.257 Early
## 5 OTU_5 F3D0 154 54 2.14 0.154 Early
## 6 OTU_6 F3D0 470 174 6.91 0.268 Early
## 7 OTU_7 F3D0 282 115 4.57 0.208 Early
## 8 OTU_8 F3D0 184 74 2.94 0.168 Early
## 9 OTU_9 F3D0 45 16 0.635 0.0830 Early
## 10 OTU_10 F3D0 158 59 2.34 0.156 Early
## # ℹ 4,132 more rows
## # ℹ abbreviated name: ¹RelRareAbundanceBySample
## # ℹ 24 more variables: RareAbundanceRarecurve <list>, Observe <dbl>,
## # Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>,
## # bray <list>, `PCo1 (46.6%)` <dbl>, `PCo2 (13.31%)` <dbl>,
## # `PCo3 (8.22%)` <dbl>, Kingdom <chr>, Phylum <chr>, Class <chr>,
## # Order <chr>, Family <chr>, Genus <chr>, Species <chr>, LDAupper <dbl>, …# Because the released `ggnewscale` modified the internal new aesthetics name,
# The following code is to obtain the new aesthetics name according to version of
# `ggnewscale`
flag <- packageVersion("ggnewscale") >= "0.5.0"
new.fill <- ifelse(flag, "fill_ggnewscale_1", "fill_new_new")
new.fill2 <- ifelse(flag , "fill_ggnewscale_2", "fill_new")
p <- mouse.time.mpse %>%
mp_plot_diff_res(
group.abun = TRUE,
pwidth.abun=0.1
)
# if version of `ggnewscale` is >= 0.5.0, you can also use p$ggnewscale to view the renamed scales.
p <- p +
scale_fill_manual(values=c("deepskyblue", "orange")) +
scale_fill_manual(
aesthetics = new.fill2, # The fill aes was renamed to `new.fill` for the abundance dotplot layer
values = c("deepskyblue", "orange")
) +
scale_fill_manual(
aesthetics = new.fill, # The fill aes for hight light layer of tree was renamed to `new.fill2`
values = c("#E41A1C", "#377EB8", "#4DAF4A",
"#984EA3", "#FF7F00", "#FFFF33",
"#A65628", "#F781BF", "#999999"
)
)
p
The different species and the abundance of group
We also developed mp_plot_diff_cladogram to visualize the result.
f <- mouse.time.mpse %>%
mp_plot_diff_cladogram(
label.size = 2.5,
hilight.alpha = .3,
bg.tree.size = .5,
bg.point.size = 2,
bg.point.stroke = .25
) +
scale_fill_diff_cladogram( # set the color of different group.
values = c('deepskyblue', 'orange')
) +
scale_size_continuous(range = c(1, 4))
f
The cladogram of differential species
The result also can be visualized with mp_plot_diff_boxplot.
f.box <- mouse.time.mpse %>%
mp_plot_diff_boxplot(
.group = time,
) %>%
set_diff_boxplot_color(
values = c("deepskyblue", "orange"),
guide = guide_legend(title=NULL)
)
f.bar <- mouse.time.mpse %>%
mp_plot_diff_boxplot(
taxa.class = c(Genus, OTU), # select the taxonomy level to display
group.abun = TRUE, # display the mean abundance of each group
removeUnknown = TRUE, # whether mask the unknown taxa.
) %>%
set_diff_boxplot_color(
values = c("deepskyblue", "orange"),
guide = guide_legend(title=NULL)
)
aplot::plot_list(f.box, f.bar)
The abundance and LDA effect size of differential taxa
Or visualizing the results with mp_plot_diff_manhattan
f.mahattan <- mouse.time.mpse %>%
mp_plot_diff_manhattan(
.group = Sign_time,
.y = fdr,
.size = 2.4,
taxa.class = c('OTU', 'Genus'),
anno.taxa.class = Phylum
)
f.mahattan
The mahattan plot of differential taxa
4. Need helps?
If you have questions/issues, please visit github issue tracker.
5. Session information
Here is the output of sessionInfo() on the system on which this document was compiled:
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_1.0.0 ggstar_1.0.4 MicrobiotaProcess_1.16.1
## [4] tidytree_0.4.6 treeio_1.28.0 ggtreeExtra_1.14.0
## [7] ggtree_3.12.0 shadowtext_0.1.4 phyloseq_1.48.0
## [10] ggplot2_3.5.1 knitr_1.48
##
## loaded via a namespace (and not attached):
## [1] gridExtra_2.3 sandwich_3.1-0
## [3] permute_0.9-7 rlang_1.1.4
## [5] magrittr_2.0.3 multcomp_1.4-26
## [7] ade4_1.7-22 matrixStats_1.3.0
## [9] compiler_4.4.1 mgcv_1.9-1
## [11] ggalluvial_0.12.5 png_0.1-8
## [13] vctrs_0.6.5 reshape2_1.4.4
## [15] stringr_1.5.1 pkgconfig_2.0.3
## [17] crayon_1.5.3 fastmap_1.2.0
## [19] magick_2.8.4 XVector_0.44.0
## [21] labeling_0.4.3 utf8_1.2.4
## [23] rmarkdown_2.27 UCSC.utils_1.0.0
## [25] tinytex_0.52 purrr_1.0.2
## [27] modeltools_0.2-23 xfun_0.46
## [29] zlibbioc_1.50.0 cachem_1.1.0
## [31] aplot_0.2.3 GenomeInfoDb_1.40.1
## [33] jsonlite_1.8.8 biomformat_1.32.0
## [35] gghalves_0.1.4 highr_0.11
## [37] rhdf5filters_1.16.0 DelayedArray_0.30.1
## [39] Rhdf5lib_1.26.0 parallel_4.4.1
## [41] cluster_2.1.6 R6_2.5.1
## [43] coin_1.4-3 bslib_0.7.0
## [45] stringi_1.8.4 GenomicRanges_1.56.1
## [47] jquerylib_0.1.4 Rcpp_1.0.13
## [49] SummarizedExperiment_1.34.0 iterators_1.0.14
## [51] zoo_1.8-12 IRanges_2.38.1
## [53] Matrix_1.7-0 splines_4.4.1
## [55] igraph_2.0.3 tidyselect_1.2.1
## [57] abind_1.4-5 yaml_2.3.10
## [59] vegan_2.6-6.1 codetools_0.2-20
## [61] lattice_0.22-6 tibble_3.2.1
## [63] plyr_1.8.9 Biobase_2.64.0
## [65] withr_3.0.0 evaluate_0.24.0
## [67] gridGraphics_0.5-1 survival_3.7-0
## [69] Biostrings_2.72.1 BiocManager_1.30.23
## [71] pillar_1.9.0 MatrixGenerics_1.16.0
## [73] foreach_1.5.2 stats4_4.4.1
## [75] ggfun_0.1.5 generics_0.1.3
## [77] dtplyr_1.3.1 S4Vectors_0.42.1
## [79] munsell_0.5.1 scales_1.3.0
## [81] glue_1.7.0 lazyeval_0.2.2
## [83] tools_4.4.1 ggnewscale_0.5.0
## [85] data.table_1.15.4 ggsignif_0.6.4
## [87] ggside_0.3.1 mvtnorm_1.2-5
## [89] fs_1.6.4 rhdf5_2.48.0
## [91] grid_4.4.1 tidyr_1.3.1
## [93] ape_5.8 libcoin_1.0-10
## [95] colorspace_2.1-1 nlme_3.1-165
## [97] GenomeInfoDbData_1.2.12 patchwork_1.2.0
## [99] cli_3.6.3 fansi_1.0.6
## [101] viridisLite_0.4.2 S4Arrays_1.4.1
## [103] dplyr_1.1.4 ggh4x_0.2.8
## [105] gtable_0.3.5 yulab.utils_0.1.5
## [107] sass_0.4.9 digest_0.6.36
## [109] BiocGenerics_0.50.0 ggrepel_0.9.5
## [111] TH.data_1.1-2 SparseArray_1.4.8
## [113] ggplotify_0.1.2 farver_2.1.2
## [115] memoise_2.0.1 htmltools_0.5.8.1
## [117] multtest_2.60.0 lifecycle_1.0.4
## [119] prettydoc_0.4.1 httr_1.4.7
## [121] MASS_7.3-616. References
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