There are two prevaling methods for using HTS-SIP data to estimate the amount of isotope that each OTU incorporated:
In this vignette, we are going to show how to run both analyses and also compare the results a bit.
First, let’s load some packages including HTSSIP
.
OK. We’re going to be using 2 data files:
We’ll be using the dataset that we simulated in the HTSSIP_sim vignette.
The phyloseq object is similar to the dataset in the other vignettes.
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 6 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 5 sample variables ]
The associated qPCR data is a list of length = 2.
## [1] "raw" "summary"
For the analyses in this vignette, we only need the ‘summary’ table.
# qPCR data (list object)
physeq_rep3_qPCR_sum = physeq_rep3_qPCR$summary
physeq_rep3_qPCR_sum %>% head(n=4)
## IS_CONTROL Sample Buoyant_density qPCR_tech_rep_mean
## 1 FALSE 13C-Glu_rep1_1.671712_2 1.671712 46598172
## 2 FALSE 13C-Glu_rep1_1.671722_1 1.671722 42299449
## 3 FALSE 13C-Glu_rep1_1.680311_3 1.680311 53536519
## 4 FALSE 13C-Glu_rep1_1.683540_4 1.683540 51449609
## qPCR_tech_rep_sd Gradient Fraction Treatment Replicate
## 1 9055833 13C-Glu_rep1 2 13C-Glu 1
## 2 16369298 13C-Glu_rep1 1 13C-Glu 1
## 3 18265667 13C-Glu_rep1 3 13C-Glu 1
## 4 3796576 13C-Glu_rep1 4 13C-Glu 1
OK. Let’s quantify isotope incorporation witht the q-SIP method.
# transforming OTU counts
physeq_rep3_t = OTU_qPCR_trans(physeq_rep3, physeq_rep3_qPCR_sum)
# calculating atom fraction excess
atomX = qSIP_atom_excess(physeq_rep3_t,
control_expr='Treatment=="12C-Con"',
treatment_rep='Replicate')
atomX %>% names
## [1] "W" "A"
The resulting list object contains 2 data.frames. We are interested in the ‘A’ table, which contains estimated BD shifts (Z) and atom fraction excess (A).
## # A tibble: 4 × 9
## OTU Wlab Wlight Z Gi Mlight Mheavymax Mlab A
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OTU.1 1.72 1.70 0.0222 0.636 308. 318. 312. 0.412
## 2 OTU.2 1.71 1.69 0.0117 0.586 308. 318. 310. 0.218
## 3 OTU.3 1.73 1.70 0.0347 0.608 308. 318. 314. 0.644
## 4 OTU.4 1.73 1.70 0.0261 0.705 308. 318. 313. 0.484
Next, let’s calculate bootstrap confidence intervales for the atom fraction excess estimations.
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## # A tibble: 4 × 11
## OTU Wlab Wlight Z Gi Mlight Mheavymax Mlab A A_CI_low
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OTU.1 1.72 1.70 0.0222 0.636 308. 318. 312. 0.412 -0.0401
## 2 OTU.2 1.71 1.69 0.0117 0.586 308. 318. 310. 0.218 0.121
## 3 OTU.3 1.73 1.70 0.0347 0.608 308. 318. 314. 0.644 0.431
## 4 OTU.4 1.73 1.70 0.0261 0.705 308. 318. 313. 0.484 0.337
## # ℹ 1 more variable: A_CI_high <dbl>
Now for delta_BD. The setup is easier because we are not using qPCR data, just relative abundances from 16S rRNA sequence data.
## Warning: Supplying `...` without names was deprecated in tidyr 1.0.0.
## ℹ Please specify a name for each selection.
## ℹ Did you want `data = nest_cols`?
## ℹ The deprecated feature was likely used in the HTSSIP package.
## Please report the issue to the authors.
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## # A tibble: 4 × 4
## OTU CM_control CM_treatment delta_BD
## <chr> <dbl> <dbl> <dbl>
## 1 OTU.1 1.69 1.72 0.0260
## 2 OTU.2 1.69 1.70 0.0130
## 3 OTU.3 1.69 1.74 0.0562
## 4 OTU.4 1.71 1.73 0.0208
Let’s plot the data and compare all of the results. First, let’s join all of the data into one table for plotting. We’ll also format it for plotting.
# checking & joining data
stopifnot(nrow(df_atomX_boot) == nrow(df_dBD))
df_j = dplyr::inner_join(df_atomX_boot, df_dBD, c('OTU'='OTU'))
stopifnot(nrow(df_atomX_boot) == nrow(df_j))
# formatting data for plotting
df_j = df_j %>%
dplyr::mutate(OTU = reorder(OTU, -delta_BD))
OK. Time to plot!
# plotting BD shift (Z)
ggplot(df_j, aes(OTU)) +
geom_point(aes(y=Z), color='blue') +
geom_point(aes(y=delta_BD), color='red') +
geom_hline(yintercept=0, linetype='dashed', alpha=0.5) +
labs(x='OTU', y='BD shift (Z)') +
theme_bw() +
theme(
axis.text.x = element_blank()
)
In the figure, red points are delta_BD and blue points are q-SIP. It’s easy to see that delta_BD is a lot more variable than q-SIP. This is likely due to a high influence of compositional data artifacts on delta_BD versus q-SIP.
Let’s make a boxplot to show the difference in estimation variance between the two methods.
# plotting BD shift (Z): boxplots
## formatting the table
df_j_g = df_j %>%
dplyr::select(OTU, Z, delta_BD) %>%
tidyr::gather(Method, BD_shift, Z, delta_BD) %>%
mutate(Method = ifelse(Method == 'Z', 'q-SIP', 'delta-BD'))
## plotting
ggplot(df_j_g, aes(Method, BD_shift)) +
geom_boxplot() +
geom_hline(yintercept=0, linetype='dashed', alpha=0.5) +
labs(x='Method', y='BD shift (Z)') +
theme_bw()
The boxplot helps to summarize how much more variance delta_BD produces versus q-SIP.
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] phyloseq_1.49.0 HTSSIP_1.4.1 ggplot2_3.5.1 tidyr_1.3.1
## [5] dplyr_1.1.4 rmarkdown_2.28
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