MW-HR-SIP


Introduction

HR-SIP method workflow:

MW-HR-SIP method workflow:

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

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

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

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

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
df_l2fc %>% head(n=3)

How many "incorporators"" (rejected hypotheses)?

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

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

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)

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

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

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

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

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

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)

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

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

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

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()


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HTSSIP documentation built on May 15, 2018, 9:03 a.m.