visual16S package demo

yeguanhua

Install dependencies

Before install the visual16S, there are some packages needed to be manually install from Bioconductor.

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("dada2", "phyloseq", "DESeq2", "EnhancedVolcano"))

Install and load package

Note:

You need to run install from the parent working directory that contains the visual16S folder.

devtools::install('visual16S')

If you can't install package in R Console, try this: open visual16S.Rproj first, then use RStudio → Build → Install and Restart.

Load package and demo data.

library(visual16S)
demo_phyloseq_object <- visual16S::demo_phyloseq_object
demo_dada2_result <- visual16S::demo_dada2_result

Data status

Primer:

CCTAYGGGRBGCASCAG ; GGACTACNNGGGTATCTAAT

DADA2 filter parameters:

dada2::filterAndTrim(truncLen=c(0,0), maxEE=c(2,2))

DADA2 taxonomy database:

silva_nr_v132

Metadata:

SampleID	diagnosis
s17118657	healthy
s17118661	healthy
s17118667	healthy
s17118714	healthy
s17118646	intestinal cancer
s17118664	intestinal cancer
s17118669	intestinal cancer
s17118686	intestinal cancer
s17118647	liver cancer
s17118684	liver cancer
s17118715	liver cancer
s17118730	liver cancer
s17118650	lung cancer
s17118680	lung cancer
s17118691	lung cancer
s17118703	lung cancer

DADA2 workflow reads track

First use the track_reads_dada2 function to check reads drop associated with every step in DADA2.

Note:

You can plot reads track in either absolute abundance or relative abundance.

If the sample size is too large to show on legend, set legend_position to "none".

track_reads_dada2(demo_dada2_result$reads_track, 
                  single_end = FALSE, 
                  relative_abundance = TRUE, 
                  legend_position = "top")

Plot sparsity - plot_sparsity()

plot_sparsity(demo_dada2_result$seq_tab, binwidth = 10)

Stacked barplot of phylogenetic composition - plot_stacked_bar()

Use plot_stacked_bar function to plot the taxonomy level abundance in every sample. You can change the 'level' argument to plot abundance in different level, or change the 'feature' to choose different feature you want to show in x-axis.

Minimum usage

plot_stacked_bar(phyloseq = demo_phyloseq_object, 
                 level = "Class")

Plot in relative abundance

plot_stacked_bar(phyloseq = demo_phyloseq_object, 
                 level = "Class", 
                 relative_abundance = TRUE)

Set "feature" parameter to show feature information

plot_stacked_bar(phyloseq = demo_phyloseq_object, 
                 level = "Class", 
                 relative_abundance = TRUE, 
                 feature = "diagnosis")

Pass ordered sample names to "order" parameter to plot in specific order

sample_order <- extract_metadata_phyloseq(demo_phyloseq_object) %>% arrange(diagnosis) %>% .$SampleID
plot_stacked_bar(phyloseq = demo_phyloseq_object, 
                 level = "Class", 
                 relative_abundance = TRUE, 
                 feature = "diagnosis", 
                 order = sample_order)

Alpha diversity - plot_alpha_diversity()

Use plot_alpha_diversity to plot alpha diversity.

All alpha diversity

plot_alpha_diversity(phyloseq = demo_phyloseq_object, feature = "diagnosis") %>% 
  # Show result in a table in markdown
  knitr::kable()

diagnosis	samples	Observed	Chao1	ACE	Shannon	Simpson	InvSimpson	Fisher
healthy	s17118657	256	259.5000	259.7298	3.886926	0.9528851	21.224687	32.61626
healthy	s17118661	236	238.8125	241.4922	3.583856	0.9452327	18.259087	30.78986
healthy	s17118667	214	225.0000	220.5107	2.379044	0.6687893	3.019226	27.28225
healthy	s17118714	265	274.0000	269.1680	3.405226	0.8781308	8.205520	34.21672
intestinal cancer	s17118646	317	322.0000	321.9582	3.840584	0.9298637	14.257945	41.17106
intestinal cancer	s17118664	383	408.0000	398.4252	4.615068	0.9716829	35.314377	53.15833
intestinal cancer	s17118669	280	314.0000	288.6257	3.642227	0.9299103	14.267425	36.75522
intestinal cancer	s17118686	463	499.9091	479.0042	4.765762	0.9826985	57.798311	66.58539
liver cancer	s17118647	246	252.8750	252.3801	3.885810	0.9543788	21.919626	32.03147
liver cancer	s17118684	416	426.9091	422.5903	4.762457	0.9827857	58.091385	60.36914
liver cancer	s17118715	325	377.5000	335.9106	4.119560	0.9571293	23.325945	43.60859
liver cancer	s17118730	265	268.7500	269.9306	3.901370	0.9587435	24.238613	35.69064
lung cancer	s17118650	258	266.2727	266.3957	1.928436	0.4850172	1.941812	33.23692
lung cancer	s17118680	344	359.1111	353.7075	4.479324	0.9733006	37.453969	46.98003
lung cancer	s17118691	197	213.5000	204.0969	3.331857	0.9208330	12.631526	24.27305
lung cancer	s17118703	286	293.2000	289.6606	4.050606	0.9521931	20.917464	37.23602

Or you can change 'measures' argument to use different measurement.

Chao1

plot_alpha_diversity(phyloseq = demo_phyloseq_object, 
                     feature = "diagnosis", 
                     measures = "Chao1", 
                     p_test = "kruskal")

##      samples         diagnosis variable    value
## 1  s17118657           healthy    Chao1 259.5000
## 2  s17118661           healthy    Chao1 238.8125
## 3  s17118667           healthy    Chao1 225.0000
## 4  s17118714           healthy    Chao1 274.0000
## 5  s17118646 intestinal cancer    Chao1 322.0000
## 6  s17118664 intestinal cancer    Chao1 408.0000
## 7  s17118669 intestinal cancer    Chao1 314.0000
## 8  s17118686 intestinal cancer    Chao1 499.9091
## 9  s17118647      liver cancer    Chao1 252.8750
## 10 s17118684      liver cancer    Chao1 426.9091
## 11 s17118715      liver cancer    Chao1 377.5000
## 12 s17118730      liver cancer    Chao1 268.7500
## 13 s17118650       lung cancer    Chao1 266.2727
## 14 s17118680       lung cancer    Chao1 359.1111
## 15 s17118691       lung cancer    Chao1 213.5000
## 16 s17118703       lung cancer    Chao1 293.2000

Beta diversity - plot_beta_diversity()

Use plot_beta_diversity to plot beta diversity. Change 'method' to draw different beta diversity plot. You can locate specific sample in beta diversity plot by the table printed to the screen.

Bray-Curtis

plot_beta_diversity(phyloseq = demo_phyloseq_object, 
                    feature = "diagnosis", 
                    method = "bray")

##     SampleID         diagnosis          PC1          PC2
## 1  s17118657           healthy -0.290927341  0.072252800
## 2  s17118661           healthy -0.307596849 -0.036986534
## 3  s17118667           healthy -0.212996044  0.005072186
## 4  s17118714           healthy  0.221843750  0.030950997
## 5  s17118646 intestinal cancer  0.236062504 -0.027898721
## 6  s17118664 intestinal cancer -0.080723593  0.126565760
## 7  s17118669 intestinal cancer  0.283441638 -0.026430612
## 8  s17118686 intestinal cancer -0.147153213  0.094026537
## 9  s17118647      liver cancer -0.026290448  0.190685061
## 10 s17118684      liver cancer -0.134091098  0.129184659
## 11 s17118715      liver cancer  0.118559730  0.078255342
## 12 s17118730      liver cancer  0.066050081 -0.240211951
## 13 s17118650       lung cancer -0.136990056 -0.527612428
## 14 s17118680       lung cancer  0.004727498  0.113546285
## 15 s17118691       lung cancer  0.236329177 -0.076007805
## 16 s17118703       lung cancer  0.169754263  0.094608425

Log2 fold change - log2fc()

Use log2fc function to show differential analysis result.

Minimum usage

log2fc(phyloseq = demo_phyloseq_object, 
       feature = "diagnosis", 
       p_value = 0.05)

## [1] "log2 fold change (MLE): diagnosis lung.cancer vs healthy"
##      OTU log2FoldChange         padj
## 1 OTU154       22.40152 2.806171e-11
## 4 OTU164       21.85581 5.133843e-05
## 5 OTU312       20.74025 1.658810e-04
## 8 OTU228       19.48117 4.637397e-04
## 6 OTU331      -18.93621 1.699443e-04
## 7 OTU282      -20.06961 2.784319e-04
## 3 OTU109      -22.19975 1.731359e-06
## 2 OTU152      -25.34458 1.312528e-08

Choose a taxonomy level to calculate log2fc.

log2fc(phyloseq = demo_phyloseq_object, 
       feature = "diagnosis", 
       p_value = 0.05, 
       level = "Genus")

## [1] "log2 fold change (MLE): diagnosis lung.cancer vs healthy"
##                     Genus log2FoldChange         padj
## 1 Lachnospiraceae_UCG-003       9.587806 6.994975e-06
## 2            Prevotella_9      -5.937157 1.247866e-04

Set "reference" and "treatment" parameters to change log2fc treatment vs reference. Both "reference" and "treatment" should be one of the levels in "feature".

log2fc(phyloseq = demo_phyloseq_object, 
       feature = "diagnosis", 
       p_value = 0.05, 
       level = "Genus", 
       reference = 'healthy', 
       treatment = 'liver cancer')

## [1] "log2 fold change (MLE): diagnosis liver cancer vs healthy"
##                     Genus log2FoldChange       padj
## 2 Lachnospiraceae_UCG-003       6.486932 0.01652016
## 3           Oscillibacter       2.790685 0.02188510
## 1               Alistipes       1.900839 0.01481186
## 4            Prevotella_9      -4.309028 0.02188510

Correlation - plot_correlation()

# Construct correlation table
cor_tab <- demo_dada2_result$seq_tab %>% t() %>% as.data.frame() %>% 
  rownames_to_column("OTU") %>% 
  mutate(Healthy = s17118657 + s17118661 + s17118667 + s17118714) %>% 
  mutate(IntestinalCancer = s17118646 + s17118664 + s17118669 + s17118686) %>% 
  mutate(LiverCancer = s17118647 + s17118684 + s17118715 + s17118730) %>% 
  mutate(LungCancer = s17118650 + s17118680 + s17118691 + s17118703) %>% 
  select(OTU, Healthy, IntestinalCancer, LiverCancer, LungCancer) %>% 
  column_to_rownames("OTU")
# Normalization
cor_tab <- (cor_tab + 1) %>% log10()
knitr::kable(cor_tab[1:10,])

	Healthy	IntestinalCancer	LiverCancer	LungCancer
OTU1	3.344196	3.398114	3.685652	4.761228
OTU2	4.459920	3.800442	4.330170	3.850462
OTU3	4.607680	3.105510	3.398808	0.000000
OTU4	3.562293	3.815246	3.679882	4.287667
OTU5	3.841422	3.063709	3.291369	4.328970
OTU6	3.335257	4.333326	3.739018	3.129368
OTU7	3.749118	3.952453	3.788946	3.879383
OTU8	3.247728	4.188141	3.401917	3.894980
OTU9	3.950413	4.148788	3.278754	3.049218
OTU10	3.778368	3.604982	3.967922	3.808481

Minimum usage

plot_correlation(cor_tab, x = "IntestinalCancer", y = "Healthy")

Multiple correlation in one plot

plot_correlation(cor_tab, x = c("IntestinalCancer", "LiverCancer", "LungCancer"), y = "Healthy")

Name		Name	Last commit message	Last commit date
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README_files/figure-markdown_github		README_files/figure-markdown_github
data		data
man		man
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
visual16S.Rproj		visual16S.Rproj

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visual16S package demo

Install dependencies

Install and load package

If you can't install package in R Console, try this: open visual16S.Rproj first, then use RStudio → Build → Install and Restart.

Load package and demo data.

Data status

DADA2 workflow reads track

Plot sparsity - plot_sparsity()

Stacked barplot of phylogenetic composition - plot_stacked_bar()

Minimum usage

Plot in relative abundance

Set "feature" parameter to show feature information

Pass ordered sample names to "order" parameter to plot in specific order

Alpha diversity - plot_alpha_diversity()

All alpha diversity

Chao1

Beta diversity - plot_beta_diversity()

Bray-Curtis

Log2 fold change - log2fc()

Minimum usage

Choose a taxonomy level to calculate log2fc.

Set "reference" and "treatment" parameters to change log2fc treatment vs reference. Both "reference" and "treatment" should be one of the levels in "feature".

Correlation - plot_correlation()

Minimum usage

Multiple correlation in one plot

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visual16S package demo

Install dependencies

Install and load package

If you can't install package in R Console, try this: open visual16S.Rproj first, then use RStudio → Build → Install and Restart.

Load package and demo data.

Data status

DADA2 workflow reads track

Plot sparsity - plot_sparsity()

Stacked barplot of phylogenetic composition - plot_stacked_bar()

Minimum usage

Plot in relative abundance

Set "feature" parameter to show feature information

Pass ordered sample names to "order" parameter to plot in specific order

Alpha diversity - plot_alpha_diversity()

All alpha diversity

Chao1

Beta diversity - plot_beta_diversity()

Bray-Curtis

Log2 fold change - log2fc()

Minimum usage

Choose a taxonomy level to calculate log2fc.

Set "reference" and "treatment" parameters to change log2fc treatment vs reference. Both "reference" and "treatment" should be one of the levels in "feature".

Correlation - plot_correlation()

Minimum usage

Multiple correlation in one plot

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