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 ]
##
## $`(Substrate=='12C-Con' & Day=='3') | (Substrate=='13C-Glu' & Day == '3')`
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 10001 taxa and 46 samples ]
## sample_data() Sample Data: [ 46 samples by 17 sample variables ]
## tax_table() Taxonomy Table: [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]
Let's set some parameters used later.
# adjusted P-value cutoff
padj_cutoff = 0.1
# number of cores for parallel processing (increase depending on your computational hardware)
ncores = 2
First, we'll just run HR-SIP on 1 treatment-control comparison. Let's get the individual phyloseq object.
physeq = physeq_S2D2_l[[1]]
physeq
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 10001 taxa and 46 samples ]
## sample_data() Sample Data: [ 46 samples by 17 sample variables ]
## tax_table() Taxonomy Table: [ 10001 taxa by 8 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 10001 tips and 10000 internal nodes ]
Let's check that the samples belong to either a 13C-treatment or 12C-control.
physeq %>% sample_data %>% .$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
df_l2fc %>% head(n=3)
## # A tibble: 3 x 17
## OTU log2FoldChange p padj Rank1 Rank2
## <chr> <dbl> <dbl> <dbl> <fctr> <fctr>
## 1 OTU.514 0.63132141 0.2572392 0.7365421 Bacteria __Proteobacteria
## 2 OTU.816 0.06245494 0.5802043 0.7365421 Bacteria __Proteobacteria
## 3 OTU.1099 0.43012112 0.4272620 0.7365421 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 x 1
## n_incorp_OTUs
## <int>
## 1 322
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)
)
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 0.63132141 0.2572392 0.7365421 Bacteria __Proteobacteria
## 2 OTU.816 0.06245494 0.5802043 0.7365421 Bacteria __Proteobacteria
## 3 OTU.1099 0.43012112 0.4272620 0.7365421 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 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
## .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 0.15 110
## 2 0.00 369
## 3 0.00 322
## 4 0.30 58
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 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 0.6142907 0.33161270 0.7656738 Bacteria __Proteobacteria
## 2 OTU.729 2.1595721 0.02327603 0.3587444 Bacteria __Acidobacteria
## 3 OTU.2590 1.1371666 0.35387359 0.7656738 Bacteria __Acidobacteria
## Rank3 Rank4
## 1 __Deltaproteobacteria __Desulfobacterales
## 2 __RB25 __uncultured_Acidobacteria_bacterium
## 3 __DA023 __uncultured_bacterium
## Rank5 Rank6 Rank7 Rank8 density_min
## 1 __Nitrospinaceae __uncultured __uncultured_bacterium <NA> 1.7
## 2 <NA> <NA> <NA> <NA> 1.7
## 3 <NA> <NA> <NA> <NA> 1.7
## density_max sparsity_threshold sparsity_apply l2fc_threshold
## 1 1.73 0 all 0.25
## 2 1.73 0 all 0.25
## 3 1.73 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
## .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 0.30 128
## 2 0.15 368
## 3 0.00 282
## 4 0.30 73
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.
sessionInfo()
## R version 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
## LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0
##
## 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 phyloseq_1.20.0 HTSSIP_1.3.0 ggplot2_2.2.1
## [5] tidyr_0.7.2 dplyr_0.7.4
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-131 bitops_1.0-6
## [3] matrixStats_0.52.2 bit64_0.9-7
## [5] doParallel_1.0.11 RColorBrewer_1.1-2
## [7] GenomeInfoDb_1.12.3 tools_3.4.2
## [9] backports_1.1.1 R6_2.2.2
## [11] coenocliner_0.2-2 vegan_2.4-4
## [13] rpart_4.1-11 Hmisc_4.0-3
## [15] DBI_0.7 lazyeval_0.2.0
## [17] BiocGenerics_0.22.1 mgcv_1.8-22
## [19] colorspace_1.3-2 permute_0.9-4
## [21] ade4_1.7-8 nnet_7.3-12
## [23] tidyselect_0.2.2 gridExtra_2.3
## [25] DESeq2_1.16.1 bit_1.1-12
## [27] compiler_3.4.2 Biobase_2.36.2
## [29] htmlTable_1.9 DelayedArray_0.2.7
## [31] labeling_0.3 scales_0.5.0
## [33] checkmate_1.8.5 genefilter_1.58.1
## [35] stringr_1.2.0 digest_0.6.12
## [37] foreign_0.8-69 XVector_0.16.0
## [39] base64enc_0.1-3 pkgconfig_2.0.1
## [41] htmltools_0.3.6 highr_0.6
## [43] htmlwidgets_0.9 rlang_0.1.2
## [45] RSQLite_2.0 bindr_0.1
## [47] jsonlite_1.5 BiocParallel_1.10.1
## [49] acepack_1.4.1 RCurl_1.95-4.8
## [51] magrittr_1.5 GenomeInfoDbData_0.99.0
## [53] Formula_1.2-2 biomformat_1.4.0
## [55] Matrix_1.2-11 Rcpp_0.12.13
## [57] munsell_0.4.3 S4Vectors_0.14.7
## [59] ape_4.1 stringi_1.1.5
## [61] MASS_7.3-47 SummarizedExperiment_1.6.5
## [63] zlibbioc_1.22.0 rhdf5_2.20.0
## [65] plyr_1.8.4 blob_1.1.0
## [67] grid_3.4.2 parallel_3.4.2
## [69] lattice_0.20-35 Biostrings_2.44.2
## [71] splines_3.4.2 multtest_2.32.0
## [73] annotate_1.54.0 locfit_1.5-9.1
## [75] knitr_1.17 igraph_1.1.2
## [77] GenomicRanges_1.28.6 markdown_0.8
## [79] geneplotter_1.54.0 reshape2_1.4.2
## [81] codetools_0.2-15 stats4_3.4.2
## [83] XML_3.98-1.9 glue_1.1.1
## [85] evaluate_0.10.1 latticeExtra_0.6-28
## [87] data.table_1.10.4-2 foreach_1.4.3
## [89] gtable_0.2.0 purrr_0.2.4
## [91] assertthat_0.2.0 mime_0.5
## [93] xtable_1.8-2 survival_2.41-3
## [95] tibble_1.3.4 iterators_1.0.8
## [97] memoise_1.1.0 AnnotationDbi_1.38.2
## [99] IRanges_2.10.5 cluster_2.0.6