README.Rmd 3.39 KB
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---
title: "32 drugs"
output: 
    md_document:
      preserve_yaml: FALSE
      fig_width: 7
      fig_height: 5
      toc: yes
      toc_depth: 2
---

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# Motivation
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Getting up to speed with R using dose-response for 32 drugs against 6
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bacterial strains.
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```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE, dpi=300, 
  fig.path = "output/fig/")
knitr::opts_knit$set(global.par = TRUE)
library('tidyverse')
```


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# Tasks

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In the following, we go through the most common steps in data analysis:
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exploration and transformation (i.e. deriving new variables). Integral to both
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steps is visualization i.e. making graphs.
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## Explore

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As a first look, the eploratory plots are informative and serve as a quality
control i.e. you know now that there is nothing extra suspicious goin on. Raw OD will suffice for that.

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1. Plot growth curves following raw OD in time. Input
   [data](doc/tasks/01_dat.csv) and expected [output](doc/tasks/01_out.pdf)
   plot are provided. The data is for azithromycin against _S. flexneri_ M90T
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   from day 2022-05-04 (first replicate). _A tip: Use `facet_wrap` with `ncol = 1` argument to have different concentrations on separate plots._
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2. Try again, now with [data](doc/tasks/02_dat.csv) from two days (let us plot
   days in different color). In addition, transform the y-axis to logarithmic
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   scale. Expected [output](doc/tasks/02_out.pdf). _A tip: you need to turn
   the `Date` variable into a factor._

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3. Once more, now with [data](doc/tasks/03_dat.csv) from three days. Expected
   [output](doc/tasks/03_out.pdf). You will encounter an issue because there
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   were two biological replicates on third day. There are multiple ways to
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   overcome this, but for now, I recommend to solve by using `group` parameter 
   of `aes` e.g. `ggplot(aes(..., group = Plt))`.
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## Transform

To quantify the growth (either rate or yield) one needs to substract the
background from raw OD. There are two ways to do that: 1) using a readout from
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just the medium; 2) using the smallest value per well (i.e. OD in one of the
first timepoints of a particular well). I prefer to use the former whenever
possible.
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4. Add an `OD` variable to your dataframe for background subtracted OD. You
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   need two things: 1) to `group` the data and 2) a way to point to background
   wells. Since grouping takes a bit practice until it becomes easy, I will
   just say that you need to subtract background on each day, on each plate,
   in each timepoint. The wells with no bacteria were encoded to have `uM =
   -1` i.e. after appropriate grouping it comes down to: `OD = OD/OD[uM ==
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   -1]`. Input [data](doc/tasks/03_dat.csv) (the same as in step 3 above).
   And if you now plot everything exactly as in step 3 above, except having OD on y-axis, here's what [output](doc/tasks/04_out.pdf) should look like.
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5. Constrain the OD at limit of detection. You might have noticed on the
   previous plot that some of the growth curves start at very low values. In
   fact, some of the ODs ended up negative. This is because the values are
   actually lower bound by limit of detection (LOD). Experience tells that at
   OD~595~ with 30 µL/well in LB, the limit of detection is ~0.03. So the
   final step for deriving background subtracted ODs is to constrain OD at
   0.03. Multiple ways are again possible, I would go for `ifelse` statement.
   Here's what the resulting [output](doc/tasks/05_out.pdf) plot should look
   like.