MW-HR-SIP

Introduction

HR-SIP method workflow:

  • Remove sparsely occuring OTUs under the assumption that they are sequencing errors.
    • This is assuming that DNA from and OTU is likely going to be found in multiple gradient fractions due to differences in G+C content, diffusion, etc.
    • So, if an OTU is just detected in only one fraction of a gradient, we can’t tell whether it is a sequencing artifact or a real, but rare OTU.
  • For each OTU, use DESeq2 to detect significant enrichment in ‘heavy’ gradient fractions.
    • ‘heavy’ gradients are selected ad hoc
    • Multiple hypotheses are adjusted for.
    • Significantly enriched OTUs are considered to have incorporated isotope (incorporators)

MW-HR-SIP method workflow:

  • The method is similar to HR-SIP, except instead of 1 ‘heavy’ BD window used, multiple windows are used, but the tradeoff is the higher number of multiple hypotheses that must be corrected for.
    • For each OTU, the BD window with highest log2 fold change (l2fc) value is used, so the BD window can vary among OTUs.
  • This method has been shown to be generally more sensitive (more true positives) than HR-SIP and more specific (less false positives) than q-SIP. See the manuscript “Multiple window High Resolution Stable Isotope Probing (MW-HR-SIP)” for more information.

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:         [ 1072 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 1072 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1072 tips and 1071 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Cel' & Day == '14')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1072 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 1072 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1072 tips and 1071 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='14') | (Substrate=='13C-Glu' & Day == '14')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1072 taxa and 47 samples ]
## sample_data() Sample Data:       [ 47 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 1072 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1072 tips and 1071 internal nodes ]
## 
## $`(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')`
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1072 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 1072 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1072 tips and 1071 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:         [ 1072 taxa and 46 samples ]
## sample_data() Sample Data:       [ 46 samples by 17 sample variables ]
## tax_table()   Taxonomy Table:    [ 1072 taxa by 8 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1072 tips and 1071 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
## Warning: multiple methods tables found for 'union'
## Warning: multiple methods tables found for 'intersect'
## Warning: multiple methods tables found for 'setdiff'
## 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
df_l2fc %>% head(n=3)
## # A tibble: 3 × 17
##   OTU    log2FoldChange       p   padj Rank1 Rank2 Rank3 Rank4 Rank5 Rank6 Rank7
##   <chr>           <dbl>   <dbl>  <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 OTU.5…         -0.133 0.745   1.00   Bact… __Pr… __De… __De… __Ni… __un… __un…
## 2 OTU.7…          1.39  0.0486  0.427  Bact… __Ac… __RB… __un… <NA>  <NA>  <NA> 
## 3 OTU.3…          1.53  0.00326 0.0975 Bact… __Ac… __DA… __un… <NA>  <NA>  <NA> 
## # ℹ 6 more variables: Rank8 <chr>, 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            36

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)
## eria __Proteobacteria
## 2 OTU.729      1.3853296 0.048578911 0.42708959 Bacteria  __Acidobacteria
## 3 OTU.322      1.5298155 0.003257549 0.09752411 Bacteria  __Acidobacteria
##                   Rank3                                Rank4            Rank5
## 1 __Deltaproteobacteria                  __Desulfobacterales __Nitrospinaceae
## 2                __RB25 __uncultured_Acidobacteria_bacterium             <NA>
## 3               __DA023               __uncultured_bacterium             <NA>
##          Rank6                  Rank7 Rank8 density_min density_max
## 1 __uncultured __uncultured_bacterium  <NA>         1.7        1.75
## 2         <NA>                   <NA>  <NA>         1.7        1.75
## 3         <NA>                   <NA>  <NA>         1.7        1.75
##   sparsity_threshold sparsity_apply l2fc_threshold
## 1                  0            all           0.25
## 2                  0            all           0.25
## 3                  0            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
## `summarise()` has grouped output by '.id'. You can override using the `.groups`
## argument.
##                                                                        .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                0.3            97
## 2                0.0           189
## 3                0.0            36
## 4                0.3            65

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()
## `summarise()` has grouped output by '.id'. You can override using the `.groups`
## argument.
# 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)
##   1.73
## 3         <NA>                   <NA>  <NA>         1.7        1.73
##   sparsity_threshold sparsity_apply l2fc_threshold
## 1                  0            all           0.25
## 2                  0            all           0.25
## 3                  0            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
## `summarise()` has grouped output by '.id'. You can override using the `.groups`
## argument.
##                                                                         .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           119
## 2                0.0           245
## 3                0.0            43
## 4                0.3            85

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) 
## `summarise()` has grouped output by '.id', 'sparsity_threshold'. You can
## override using the `.groups` argument.
#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()
## `summarise()` has grouped output by '.id'. You can override using the `.groups`
## argument.
# 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 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## 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       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] phyloseq_1.51.0 HTSSIP_1.4.1    ggplot2_3.5.1   tidyr_1.3.1    
## [5] dplyr_1.1.4     rmarkdown_2.29 
## 
## loaded via a namespace (and not attached):
##  [1] ade4_1.7-22                 tidyselect_1.2.1           
##  [3] farver_2.1.2                Biostrings_2.75.1          
##  [5] fastmap_1.2.0               lazyeval_0.2.2             
##  [7] digest_0.6.37               lifecycle_1.0.4            
##  [9] cluster_2.1.6               survival_3.7-0             
## [11] magrittr_2.0.3              compiler_4.4.2             
## [13] rlang_1.1.4                 sass_0.4.9                 
## [15] tools_4.4.2                 igraph_2.1.1               
## [17] utf8_1.2.4                  yaml_2.3.10                
## [19] data.table_1.16.2           knitr_1.49                 
## [21] S4Arrays_1.7.1              labeling_0.4.3             
## [23] DelayedArray_0.33.2         plyr_1.8.9                 
## [25] abind_1.4-8                 BiocParallel_1.41.0        
## [27] withr_3.0.2                 purrr_1.0.2                
## [29] BiocGenerics_0.53.3         sys_3.4.3                  
## [31] grid_4.4.2                  stats4_4.4.2               
## [33] fansi_1.0.6                 multtest_2.63.0            
## [35] biomformat_1.35.0           colorspace_2.1-1           
## [37] Rhdf5lib_1.29.0             scales_1.3.0               
## [39] iterators_1.0.14            MASS_7.3-61                
## [41] SummarizedExperiment_1.37.0 cli_3.6.3                  
## [43] vegan_2.6-8                 crayon_1.5.3               
## [45] generics_0.1.3              httr_1.4.7                 
## [47] reshape2_1.4.4              ape_5.8                    
## [49] cachem_1.1.0                rhdf5_2.51.0               
## [51] stringr_1.5.1               zlibbioc_1.52.0            
## [53] splines_4.4.2               parallel_4.4.2             
## [55] XVector_0.47.0              matrixStats_1.4.1          
## [57] vctrs_0.6.5                 Matrix_1.7-1               
## [59] jsonlite_1.8.9              IRanges_2.41.1             
## [61] S4Vectors_0.45.2            coenocliner_0.2-3          
## [63] maketools_1.3.1             locfit_1.5-9.10            
## [65] foreach_1.5.2               jquerylib_0.1.4            
## [67] glue_1.8.0                  codetools_0.2-20           
## [69] stringi_1.8.4               gtable_0.3.6               
## [71] GenomeInfoDb_1.43.0         GenomicRanges_1.59.0       
## [73] UCSC.utils_1.3.0            munsell_0.5.1              
## [75] tibble_3.2.1                pillar_1.9.0               
## [77] htmltools_0.5.8.1           rhdf5filters_1.19.0        
## [79] GenomeInfoDbData_1.2.13     R6_2.5.1                   
## [81] doParallel_1.0.17           evaluate_1.0.1             
## [83] lattice_0.22-6              Biobase_2.67.0             
## [85] bslib_0.8.0                 Rcpp_1.0.13-1              
## [87] SparseArray_1.7.2           nlme_3.1-166               
## [89] permute_0.9-7               mgcv_1.9-1                 
## [91] DESeq2_1.47.1               xfun_0.49                  
## [93] MatrixGenerics_1.19.0       buildtools_1.0.0           
## [95] pkgconfig_2.0.3