Dataset

First, let’s load some packages including HTSSIP.

library(dplyr)
library(ggplot2)
library(HTSSIP)

See HTSSIP introduction vignette for a description on why dataset parsing (all treatment-control comparisons) is needed.

Let’s see the already parsed dataset

physeq_S2D2_l

## $`(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10001 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Cel' & Day == '14')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10001 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Glu' & Day == '14')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10001 taxa and 47 samples ]
## sample_data() Sample Data:       [ 47 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10001 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]

HR-SIP

Let’s set some parameters used later.

# adjusted P-value cutoff 
padj_cutoff = 0.1
# number of cores for parallel processing (increase depending on your computational hardware)
ncores = 2

One treatment-control comparison

First, we’ll just run HR-SIP on 1 treatment-control comparison. Let’s get the individual phyloseq object.

physeq = physeq_S2D2_l[[1]]
physeq

## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 10001 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]

Let’s check that the samples belong to either a 13C-treatment or 12C-control.

physeq %>% sample_data %>% .$Substrate %>% table

## .
## 12C-Con 13C-Cel 
##      23      23

OK, we should be ready to run HR-SIP!

Note that the design parameter for HRSIP() is the experimental design parameter for calculating log2 fold change (l2fc) values with DESeq. Here, it’s used to distinguish label-treatment and unlabel-control samples.

df_l2fc = HRSIP(physeq,
                design = ~Substrate,
                padj_cutoff = padj_cutoff,
                sparsity_threshold = c(0,0.15,0.3))   # just using 3 thresholds to reduce time

## Sparsity threshold: 0 
## Density window: 1.7-1.75 
## Sparsity threshold: 0.15 
## Density window: 1.7-1.75 
## Sparsity threshold: 0.3 
## Density window: 1.7-1.75 
## Sparsity threshold with the most rejected hypotheses: 0.15

df_l2fc %>% head(n=3)

## # A tibble: 3 × 17
##        OTU log2FoldChange         p  padj    Rank1            Rank2
##      <chr>          <dbl>     <dbl> <dbl>   <fctr>           <fctr>
## 1  OTU.514     -0.2450007 0.8528092     1 Bacteria __Proteobacteria
## 2  OTU.816     -0.5302828 0.9141156     1 Bacteria __Proteobacteria
## 3 OTU.1099     -0.3096987 0.8362421     1 Bacteria  __Acidobacteria
## # ... with 11 more variables: Rank3 <fctr>, Rank4 <fctr>, Rank5 <fctr>,
## #   Rank6 <fctr>, Rank7 <fctr>, Rank8 <fctr>, density_min <dbl>,
## #   density_max <dbl>, sparsity_threshold <dbl>, sparsity_apply <chr>,
## #   l2fc_threshold <dbl>

How many “incorporators”" (rejected hypotheses)?

df_l2fc %>% 
  filter(padj < padj_cutoff) %>%
  group_by() %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length)

## # A tibble: 1 × 1
##   n_incorp_OTUs
##           <int>
## 1             6

Let’s plot a breakdown of incorporators for each phylum.

# summarizing
df_l2fc_s = df_l2fc %>% 
  filter(padj < padj_cutoff) %>%
  mutate(Rank2 = gsub('^__', '', Rank2)) %>%
  group_by(Rank2) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) %>%
  ungroup()

# plotting
ggplot(df_l2fc_s, aes(Rank2, n_incorp_OTUs)) +
    geom_bar(stat='identity') +
    labs(x='Phylum', y='Number of incorporators') +
    theme_bw() +
    theme(
      axis.text.x = element_text(angle=45, hjust=1)
    )

All treatment-control comparisons

Let’s now run HR-SIP on all treatment-control comparisons in the dataset:

# Number of comparisons
physeq_S2D2_l %>% length

## [1] 4

The function plyr::ldply() is useful (compared to lapply()) beccause it can be run in parallel and returns a data.frame object.

# Running in parallel; you may need to change the backend for your environment.
# Or you can just set .parallel=FALSE. 
doParallel::registerDoParallel(ncores)

df_l2fc = plyr::ldply(physeq_S2D2_l, 
                      HRSIP, 
                      design = ~Substrate, 
                      padj_cutoff = padj_cutoff,
                      sparsity_threshold = c(0,0.15,0.3),  # just using 3 thresholds to reduce run time
                      .parallel=TRUE)
df_l2fc %>% head(n=3)

##                                                                       .id
## 1 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
## 2 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
## 3 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
##        OTU log2FoldChange         p padj    Rank1            Rank2
## 1  OTU.514     -0.2450007 0.8528092    1 Bacteria __Proteobacteria
## 2  OTU.816     -0.5302828 0.9141156    1 Bacteria __Proteobacteria
## 3 OTU.1099     -0.3096987 0.8362421    1 Bacteria  __Acidobacteria
##                   Rank3                  Rank4            Rank5
## 1 __Deltaproteobacteria    __Desulfobacterales __Nitrospinaceae
## 2 __Deltaproteobacteria    __Desulfobacterales __Nitrospinaceae
## 3               __32-21 __uncultured_bacterium             <NA>
##          Rank6                  Rank7 Rank8 density_min density_max
## 1 __uncultured __uncultured_bacterium  <NA>         1.7        1.75
## 2 __uncultured __uncultured_bacterium  <NA>         1.7        1.75
## 3         <NA>                   <NA>  <NA>         1.7        1.75
##   sparsity_threshold sparsity_apply l2fc_threshold
## 1               0.15            all           0.25
## 2               0.15            all           0.25
## 3               0.15            all           0.25

Each specific phyloseq subset (treatment-control comparison) is delimited with the “.id” column.

df_l2fc %>% .$.id %>% unique

## [1] "(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')"  
## [2] "(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Cel' & Day == '14')"
## [3] "(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Glu' & Day == '14')"
## [4] "(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')"

For clarity, let’s edit these long strings to make them more readable when plotted.

df_l2fc = df_l2fc %>%
  mutate(.id = gsub(' \\| ', '\n', .id))
df_l2fc %>% .$.id %>% unique

## [1] "(Substrate=='12C-Con' & Day=='3')\n(Substrate=='13C-Cel' & Day == '3')"  
## [2] "(Substrate=='12C-Con' & Day=='14')\n(Substrate=='13C-Cel' & Day == '14')"
## [3] "(Substrate=='12C-Con' & Day=='14')\n(Substrate=='13C-Glu' & Day == '14')"
## [4] "(Substrate=='12C-Con' & Day=='3')\n(Substrate=='13C-Glu' & Day == '3')"

How many incorporators (rejected hypotheses) & which sparsity cutoff was used for each comparison?

Note: you could set one sparsity cutoff for all comparisons by setting the sparsity_cutoff to a specific value.

df_l2fc %>% 
  filter(padj < padj_cutoff) %>%
  group_by(.id, sparsity_threshold) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) %>%
  as.data.frame

##                                                                        .id
## 1 (Substrate=='12C-Con' & Day=='14')\n(Substrate=='13C-Cel' & Day == '14')
## 2 (Substrate=='12C-Con' & Day=='14')\n(Substrate=='13C-Glu' & Day == '14')
## 3   (Substrate=='12C-Con' & Day=='3')\n(Substrate=='13C-Cel' & Day == '3')
## 4   (Substrate=='12C-Con' & Day=='3')\n(Substrate=='13C-Glu' & Day == '3')
##   sparsity_threshold n_incorp_OTUs
## 1               0.30           141
## 2               0.30           204
## 3               0.15             6
## 4               0.30            45

How about a breakdown of incorporators for each phylum in each comparision.

# summarizing
df_l2fc_s = df_l2fc %>% 
  filter(padj < padj_cutoff) %>%
  mutate(Rank2 = gsub('^__', '', Rank2)) %>%
  group_by(.id, Rank2) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) %>%
  ungroup()

# plotting
ggplot(df_l2fc_s, aes(Rank2, n_incorp_OTUs)) +
    geom_bar(stat='identity') +
    labs(x='Phylum', y='Number of incorporators') +
    facet_wrap(~ .id, scales='free') +
    theme_bw() +
    theme(
      axis.text.x = element_text(angle=55, hjust=1)
    )

MW-HR-SIP

MW-HR-SIP is run very similarly to HRSIP, but it uses multiple buoyant density (BD) windows. MW-HR-SIP is performed with the HRSIP() function, but multiple BD windows are specified.

Let’s use 3 buoyant density windows (g/ml):

1.70-1.73, 1.72-1.75, 1.74-1.77

windows = data.frame(density_min=c(1.70, 1.72, 1.74), 
                     density_max=c(1.73, 1.75, 1.77))
windows

##   density_min density_max
## 1        1.70        1.73
## 2        1.72        1.75
## 3        1.74        1.77

Running HRSIP with all 3 BD windows. Let’s run this in parallel to speed things up.

You can turn off parallel processing by setting the parallel option to FALSE. Also, there’s 2 different levels that could be parallelized: either the ldply() or HRSIP(). Here, I’m running HRSIP in parallel, but it may make sense in other situations (eg., many comparisons but few density windows and/or sparsity cutoffs) to use ldply in parallel only.

doParallel::registerDoParallel(ncores)

df_l2fc = plyr::ldply(physeq_S2D2_l, 
                      HRSIP, 
                      density_windows = windows,
                      design = ~Substrate, 
                      padj_cutoff = padj_cutoff,
                      sparsity_threshold = c(0,0.15,0.3), # just using 3 thresholds to reduce run time
                      .parallel = TRUE)
df_l2fc %>% head(n=3)

##                                                                       .id
## 1 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
## 2 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
## 3 (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
##        OTU log2FoldChange          p padj    Rank1            Rank2
## 1  OTU.514     -0.1860702 0.77035746    1 Bacteria __Proteobacteria
## 2 OTU.1415      0.6690787 0.23939655    1 Bacteria  __Acidobacteria
## 3 OTU.1136      1.4397854 0.02300216    1 Bacteria  __Acidobacteria
##                   Rank3                  Rank4            Rank5
## 1 __Deltaproteobacteria    __Desulfobacterales __Nitrospinaceae
## 2               __DA023                   <NA>             <NA>
## 3               __DA023 __uncultured_bacterium             <NA>
##          Rank6                  Rank7 Rank8 density_min density_max
## 1 __uncultured __uncultured_bacterium  <NA>         1.7        1.73
## 2         <NA>                   <NA>  <NA>         1.7        1.73
## 3         <NA>                   <NA>  <NA>         1.7        1.73
##   sparsity_threshold sparsity_apply l2fc_threshold
## 1                0.3            all           0.25
## 2                0.3            all           0.25
## 3                0.3            all           0.25

Let’s check that we have all treatment-control comparisons.

df_l2fc %>% .$.id %>% unique

## [1] "(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')"  
## [2] "(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Cel' & Day == '14')"
## [3] "(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Glu' & Day == '14')"
## [4] "(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')"

How many incorporators (rejected hypotheses) & which sparsity cutoff was used for each comparison?

Note: one sparsity cutoff could be set for all comparisons by setting the sparsity_cutoff to a specific value.

df_l2fc %>% 
  filter(padj < padj_cutoff) %>%
  group_by(.id, sparsity_threshold) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) %>%
  as.data.frame

##                                                                         .id
## 1 (Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Cel' & Day == '14')
## 2 (Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Glu' & Day == '14')
## 3   (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Cel' & Day == '3')
## 4   (Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')
##   sparsity_threshold n_incorp_OTUs
## 1                0.3           158
## 2                0.3           276
## 3                0.3             8
## 4                0.3            67

The density windows can vary for each OTU. Let’s plot which density windows were used for the OTUs in the dataset.

# summarizing
df_l2fc_s = df_l2fc %>% 
  mutate(.id = gsub(' \\| ', '\n', .id)) %>%
  filter(padj < padj_cutoff) %>%
  mutate(density_range = paste(density_min, density_max, sep='-')) %>% 
  group_by(.id, sparsity_threshold, density_range) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) 

#plotting
ggplot(df_l2fc_s, aes(.id, n_incorp_OTUs, fill=density_range)) +
    geom_bar(stat='identity', position='fill') +
    labs(x='Control-treatment comparision', y='Fraction of incorporators') +
    scale_y_continuous(expand=c(0,0)) +
    theme_bw() +
    theme(
      axis.text.x = element_text(angle=55, hjust=1)
    )

Different BD windows were used for different treatment-control comparisons because the amount of BD shift likely varied among taxa. For example, if a taxon incorporates 100% 13C isotope, then a very ‘heavy’ BD window may show a larger l2fc than a less ‘heavy’ BD window.

Let’s look at a breakdown of incorporators for each phylum in each comparision.

# summarizing
df_l2fc_s = df_l2fc %>% 
  mutate(.id = gsub(' \\| ', '\n', .id)) %>%
  filter(padj < padj_cutoff) %>%
  mutate(Rank2 = gsub('^__', '', Rank2)) %>%
  group_by(.id, Rank2) %>%
  summarize(n_incorp_OTUs = OTU %>% unique %>% length) %>%
  ungroup()

# plotting
ggplot(df_l2fc_s, aes(Rank2, n_incorp_OTUs)) +
    geom_bar(stat='identity') +
    labs(x='Phylum', y='Number of incorporators') +
    facet_wrap(~ .id, scales='free') +
    theme_bw() +
    theme(
      axis.text.x = element_text(angle=55, hjust=1)
    )

Note that the MW-HR-SIP method identifies more incorporators than the HR-SIP method (which uses just one BD window).

MW-HR-SIP detects more taxa for 2 main reasons. First, taxa vary in G+C content, so using only 1 BD window likely encompasses BD shifts for taxa of certain G+C contents (eg., ~50% G+C), but may miss other taxa with higher or lower G+C content. Second, taxa can vary in how much isotope was incorporated, which will affect where each taxon’s DNA is in the density gradient.

Session info

sessionInfo()

## R version 3.3.3 (2017-03-06)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] phyloseq_1.19.1 HTSSIP_1.2.0    ggplot2_2.2.1   tidyr_0.6.1    
## [5] dplyr_0.5.0    
## 
## loaded via a namespace (and not attached):
##  [1] Biobase_2.34.0             jsonlite_1.4              
##  [3] splines_3.3.3              foreach_1.4.3             
##  [5] Formula_1.2-1              assertthat_0.2.0          
##  [7] stats4_3.3.3               latticeExtra_0.6-28       
##  [9] yaml_2.1.14                RSQLite_1.1-2             
## [11] backports_1.0.5            lattice_0.20-35           
## [13] digest_0.6.12              GenomicRanges_1.26.4      
## [15] RColorBrewer_1.1-2         XVector_0.14.1            
## [17] checkmate_1.8.2            colorspace_1.3-2          
## [19] htmltools_0.3.5            Matrix_1.2-8              
## [21] plyr_1.8.4                 XML_3.98-1.6              
## [23] DESeq2_1.14.1              genefilter_1.56.0         
## [25] zlibbioc_1.20.0            xtable_1.8-2              
## [27] scales_0.4.1               BiocParallel_1.8.2        
## [29] annotate_1.52.1            tibble_1.3.0              
## [31] htmlTable_1.9              mgcv_1.8-17               
## [33] IRanges_2.8.2              SummarizedExperiment_1.4.0
## [35] nnet_7.3-12                BiocGenerics_0.20.0       
## [37] lazyeval_0.2.0             survival_2.41-3           
## [39] magrittr_1.5               memoise_1.1.0             
## [41] evaluate_0.10              doParallel_1.0.10         
## [43] nlme_3.1-131               MASS_7.3-45               
## [45] foreign_0.8-67             vegan_2.4-3               
## [47] tools_3.3.3                data.table_1.10.4         
## [49] stringr_1.2.0              S4Vectors_0.12.2          
## [51] locfit_1.5-9.1             munsell_0.4.3             
## [53] cluster_2.0.6              AnnotationDbi_1.36.2      
## [55] Biostrings_2.42.1          ade4_1.7-6                
## [57] compiler_3.3.3             GenomeInfoDb_1.10.3       
## [59] rhdf5_2.18.0               grid_3.3.3                
## [61] coenocliner_0.2-2          RCurl_1.95-4.8            
## [63] iterators_1.0.8            biomformat_1.2.0          
## [65] htmlwidgets_0.8            igraph_1.0.1              
## [67] bitops_1.0-6               base64enc_0.1-3           
## [69] labeling_0.3               rmarkdown_1.4             
## [71] gtable_0.2.0               codetools_0.2-15          
## [73] multtest_2.30.0            DBI_0.6-1                 
## [75] reshape2_1.4.2             R6_2.2.0                  
## [77] gridExtra_2.2.1            knitr_1.15.1              
## [79] Hmisc_4.0-2                rprojroot_1.2             
## [81] permute_0.9-4              ape_4.1                   
## [83] stringi_1.1.5              parallel_3.3.3            
## [85] Rcpp_0.12.10               geneplotter_1.52.0        
## [87] rpart_4.1-10               acepack_1.4.1

MW-HR-SIP

Nick Youngblut

2017-07-24

Introduction

HR-SIP method workflow:

MW-HR-SIP method workflow:

Dataset

HR-SIP

One treatment-control comparison

All treatment-control comparisons

MW-HR-SIP

Session info