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4 and 5 plus corrections
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@3mmaRand
3mmaRand committed Apr 4, 2024
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185 changes: 174 additions & 11 deletions omics/kelly/workshop.qmd
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Expand Up @@ -519,7 +519,7 @@ but isohexanoate and hexanoate differ. The difference might be because
isohexanoate is especially low in the NC replicates at time = 1 and
hexanoate is especially high in the NC replicate 2 at time = 22

## 4. Calculate the flux - pending.
## 4. Calculate the flux

Calculate the flux(change in VFA concentration over a period of time,
divided by weight or volume of material) of each VFA, by mM and by
Expand All @@ -539,8 +539,7 @@ output.**

📢 Kelly asked for ".. a simple flux measurement, which is the change in VFA concentration over a period of time, divided by weight or volume of material. In this case it might be equal to == Delta(Acetate at 3 days - Acetate at 1 day)/Delta (3days - 1day)/50 mls sludge. This would provide a final flux with the units of mg acetate per ml sludge per day."

Note: Kelly says mg/ml where earlier he used g/L. These are the same (but I called
my column `conc_g_l`)
Note: Kelly says mg/ml where earlier he used g/L. These are the same (but I called my column `conc_g_l`)

We need to use the `vfa_delta` data frame. It contains the change in VFA concentration and the change in time. We will add a column for the flux of each VFA in g/L/day. (mg/ml/day)

Expand Down Expand Up @@ -787,7 +786,8 @@ Repeat for the delta data.
vfa_delta_protein <- vfa_delta_protein |>
pivot_longer(cols = -c(treatment,
replicate,
time_day),
time_day,
delta_time),
values_to = "conc_mM",
names_to = "vfa")
```
Expand All @@ -804,7 +804,7 @@ vfa_delta_protein <- vfa_delta_protein |>
mutate(conc_g_l = conc_mM * 0.001 * mw)
```

## 3. Calculate the percent representation of each VFA
#### 3. Calculate the percent representation of each VFA

by mM and by weight

Expand All @@ -819,7 +819,7 @@ vfa_cummul_protein <- vfa_cummul_protein |>
```

## Graphs for info so far
#### Graphs for info so far

🎬 Make summary data for graphing

Expand Down Expand Up @@ -887,8 +887,6 @@ vfa_delta_protein_summary |>
facet_wrap(~treatment) +
theme(strip.background = element_blank())
```

🎬 Graph the mean percent representation of each VFA g/l. Note
Expand All @@ -906,6 +904,88 @@ vfa_cummul_protein_summary |>
theme(strip.background = element_blank())
```

#### 4. Calculate the flux

Calculate the flux(change in VFA concentration over a period of time,
divided by weight or volume of material) of each VFA, by mM and by
weight.
Emma's note: I think the terms flux and reaction rate are used
interchangeably

The sludge volume is constant, at 30 mls.
Flux units are mg vfa per ml sludge per day


Note: Kelly says mg/ml where earlier he used g/L. These are the same (but I called my column `conc_g_l`)

We need to use the `vfa_delta_protein` data frame. It contains the change in VFA concentration and the change in time. We will add a column for the flux of each VFA in g/L/day. (mg/ml/day)

```{r}
sludge_volume <- 30 # ml
vfa_delta_protein <- vfa_delta_protein |>
mutate(flux = conc_g_l / delta_time / sludge_volume)
```

NAs at time 1 are expected because there's no time before that to calculate a changes



#### 5. Graph and extract the reaction rate

We can now plot the observed fluxes (reaction rates) over time

I've summarised the data to add error bars and means
```{r}
vfa_delta_protein_summary <- vfa_delta_protein |>
group_by(treatment, time_day, vfa) |>
summarise(mean_flux = mean(flux),
se_flux = sd(flux)/sqrt(length(flux))) |>
ungroup()
```


```{r}
ggplot(data = vfa_delta_protein, aes(x = time_day, colour = vfa)) +
geom_point(aes(y = flux), alpha = 0.6) +
geom_errorbar(data = vfa_delta_protein_summary,
aes(ymin = mean_flux - se_flux,
ymax = mean_flux + se_flux),
width = 1) +
geom_errorbar(data = vfa_delta_protein_summary,
aes(ymin = mean_flux,
ymax = mean_flux),
width = 0.8) +
scale_color_viridis_d(name = NULL) +
scale_x_continuous(name = "Time (days)") +
scale_y_continuous(name = "VFA Flux mg/ml/day") +
theme_bw() +
facet_wrap(~treatment) +
theme(strip.background = element_blank())
```

Or maybe this is easier to read:

```{r}
ggplot(data = vfa_delta_protein, aes(x = time_day, colour = treatment)) +
geom_point(aes(y = flux), alpha = 0.6) +
geom_errorbar(data = vfa_delta_protein_summary,
aes(ymin = mean_flux - se_flux,
ymax = mean_flux + se_flux),
width = 1) +
geom_errorbar(data = vfa_delta_protein_summary,
aes(ymin = mean_flux,
ymax = mean_flux),
width = 0.8) +
scale_color_viridis_d(name = NULL, begin = 0.2, end = 0.7) +
scale_x_continuous(name = "Time (days)") +
scale_y_continuous(name = "VFA Flux mg/ml/day") +
theme_bw() +
facet_wrap(~ vfa, nrow = 2) +
theme(strip.background = element_blank(),
legend.position = "top")
```


### Set 2: VFA treatments
Expand Down Expand Up @@ -974,7 +1054,8 @@ Repeat for the delta data.
vfa_delta_vfa <- vfa_delta_vfa |>
pivot_longer(cols = -c(treatment,
replicate,
time_day),
time_day,
delta_time),
values_to = "conc_mM",
names_to = "vfa")
```
Expand All @@ -991,7 +1072,7 @@ vfa_delta_vfa <- vfa_delta_vfa |>
mutate(conc_g_l = conc_mM * 0.001 * mw)
```

## 3. Calculate the percent representation of each VFA
#### 3. Calculate the percent representation of each VFA

by mM and by weight

Expand All @@ -1006,7 +1087,7 @@ vfa_cummul_vfa <- vfa_cummul_vfa |>
```

## Graphs for info so far
#### Graphs for info so far

🎬 Make summary data for graphing

Expand Down Expand Up @@ -1095,9 +1176,91 @@ vfa_cummul_vfa_summary |>



#### 4. Calculate the flux

Calculate the flux(change in VFA concentration over a period of time,
divided by weight or volume of material) of each VFA, by mM and by
weight.
Emma's note: I think the terms flux and reaction rate are used
interchangeably

The sludge volume is constant, at 30 mls.
Flux units are mg vfa per ml sludge per day


Note: Kelly says mg/ml where earlier he used g/L. These are the same (but I called my column `conc_g_l`)

We need to use the `vfa_delta_vfa` data frame. It contains the change in VFA concentration and the change in time. We will add a column for the flux of each VFA in g/L/day. (mg/ml/day)

```{r}
sludge_volume <- 30 # ml
vfa_delta_vfa <- vfa_delta_vfa |>
mutate(flux = conc_g_l / delta_time / sludge_volume)
```

NAs at time 1 are expected because there's no time before that to calculate a changes



#### 5. Graph and extract the reaction rate

We can now plot the observed fluxes (reaction rates) over time

I've summarised the data to add error bars and means
```{r}
vfa_delta_vfa_summary <- vfa_delta_vfa |>
group_by(treatment, time_day, vfa) |>
summarise(mean_flux = mean(flux),
se_flux = sd(flux)/sqrt(length(flux))) |>
ungroup()
```


```{r}
ggplot(data = vfa_delta_vfa, aes(x = time_day, colour = vfa)) +
geom_point(aes(y = flux), alpha = 0.6) +
geom_errorbar(data = vfa_delta_vfa_summary,
aes(ymin = mean_flux - se_flux,
ymax = mean_flux + se_flux),
width = 1) +
geom_errorbar(data = vfa_delta_vfa_summary,
aes(ymin = mean_flux,
ymax = mean_flux),
width = 0.8) +
scale_color_viridis_d(name = NULL) +
scale_x_continuous(name = "Time (days)") +
scale_y_continuous(name = "VFA Flux mg/ml/day") +
theme_bw() +
facet_wrap(~treatment) +
theme(strip.background = element_blank())
```

Or maybe this is easier to read:

```{r}
ggplot(data = vfa_delta_vfa, aes(x = time_day, colour = treatment)) +
geom_point(aes(y = flux), alpha = 0.6) +
geom_errorbar(data = vfa_delta_vfa_summary,
aes(ymin = mean_flux - se_flux,
ymax = mean_flux + se_flux),
width = 1) +
geom_errorbar(data = vfa_delta_vfa_summary,
aes(ymin = mean_flux,
ymax = mean_flux),
width = 0.8) +
scale_color_viridis_d(name = NULL, begin = 0.2, end = 0.7) +
scale_x_continuous(name = "Time (days)") +
scale_y_continuous(name = "VFA Flux mg/ml/day") +
theme_bw() +
facet_wrap(~ vfa, nrow = 2) +
theme(strip.background = element_blank(),
legend.position = "top")
```


## ph data


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