README.md

In raim/platexpress: Inspection and Analysis of Microbial Growth and Gene Expression Data

platexpress - microbial growth & gene expression

The platexpress package provides a quick & easy interface to microbial growth & gene expression data as measured in parallel growth platforms such as microplate readers. It allows for quick inspection of raw data, blank normalization, and summarization over replicates.

A few data conversion routines allow to interface other R packages for analysis of microbial growth such as grofit and growthrates, and plot results within platexpress.

Partial interfaces to cellGrowth exist, and growthcurver may be added on request.

Installation

platexpress is currently under heavy development and there is no official release yet. You can download from github and run:

R CMD build platexpress/
R CMD INSTALL platexpress_0.1.tar.gz

or use the library devtools:

install.packages("devtools")
library(devtools)
install_github("raim/platexpress")

A Typical Workflow in platexpress

NOTE: the following examples are all executed step-by-step in an R demo() that comes with the library. For a quick tour of the following, open R and type:

library(platexpress)
demo("demo_ap12",package="platexpress")

1) Parse the plate layout and measurements

The plate layout will later allow to do blank correction and group experiments. Here, we take the example file that ships with the platexpress library:

plate.file <- system.file("extdata", "AP12_layout.csv", package = "platexpress")
plate <- readPlateMap(file = plate.file, blank.id = "blank",fsep = "\n", fields = c("strain","samples"))

... and parse the data, as exported from platereader "Synergy Mx" software in the shipped example file:

data.file <- system.file("extdata", "AP12.csv", package = "platexpress")
raw <- readPlateData(file = data.file, type = "Synergy", data.ids = c("600","YFP_50:500,535"), time.format = "%H:%M:%S", time.conversion = 1/3600)

Here we include some specifics for this data set. First, we need to specify from which plate-reader the file was exported. platexpress can currently read files exported from "Synergy Mx" and "BMG Optima" software. Please let us know, if you need other formats to be implemented. Even, if we now the plate-reader some things always change between exported data sets. E.g., the example file was exported with times in format HH:MM:SS, and we need to tell it to our parser via the argument time.format (see R base function base::strptime). The resulting time is in seconds but we would prefer hours, i.e., divide it by 3600. This is done by the time.conversion argument, although in this case you could simply convert it manually with raw$Time <- raw$Time/3600 as well.

Generally, parsing a new data file, freshly exported from the plate-reader or from a spread-sheet program is likely to cause problems. You should take care at this step. Problems with data parsing are usually the most time-consuming step in data analysis!

2) Inspect and process the raw data

Take a first look:

viewPlate(raw)

... and note that there is no growth in well A9, so let's skip it. To be safe, let's also skip it from the plate layout map.

raw <- skipWells(raw, skip = "A9")
plate <- skipWells(plate, skip = "A9")

... correct for blank well measurements (defined in the plate layout map!), assign colors and nicer IDs, and view only the present rows (A, B and C) and columns (1-9):

data <- correctBlanks(data=raw, plate=plate)
viewPlate(data, rows = c("A","B","C"), cols = 1:9)

Now the raw data processing is done, so let's assign more informative IDs and select nicer colors, as R RGB strings:

data <- prettyData(data = raw,yids = c(OD="600",YFP="YFP_50:500,535"), 
                   colors= c(OD="#000000",YFP=wavelength2RGB(690)))

Note that helper functions showSpectrum() and findWavelength(1) allow to select colors from a spectrum of visible light and gives you the required RGB strings. Or you can convert a wavlength in nm directly to RGB using wavelength2RGB(500) for the estimated RGB value of 500 nm light.

3) Group replicates

Next, generate groups over replicates and strains and view summarized growth vs. expression curves for these groups. The areas indicate the t-test based 95% confidence interval, the thick line is the mean, and optionally, you can keep also the original data in the plot (by choosing a lwd.orig > 0):

groups <- getGroups(plate, by = "strain")
viewGroups(data, groups = groups, lwd.orig = 0, nrow = 1)

or view all in one

viewGroups(data, groups2 = groups, lwd.orig = 0, nrow = 1)

4) Advanced analyses

Fit Growth Rate Models

TODO:

• grofit and growthrates interfaces

Correct For Lag Phase

The above plots already include a statistical analysis. If the 95% confidence intervals don't overlap between different groups you already have your signficant result. However, we can see for the example data that the lag phase of the group "EVC" is much longer. We could estimate the lag-phase by using the R package grofit (available at CRAN) via platexpress conversion function data2grofit. However, here we can see that a 3 h lag phase for "EVC" wells may do. First we need to assign a lag-phase to each well, simply by generating an R vector with the well ID as names. First we need to assign a lag-phase to each well, simply by generating an R vector with the well ID as names.

lag <- rep(3, length(groups$EVC))
names(lag) <- groups$EVC
data <- shiftData(data, lag = lag)
viewGroups(data, groups2 = groups, lwd.orig = 0, nrow = 1)

Great! Now the OD curves are aligned, and they seem to be very similar, allowing a direct comparison of fluorescence over time.

Generate Box- or Barplots

For some applications you may want to boil data down to core messages. Various functions allow to further summarize and visualize results, e.g.,

boxData(data, rng = 7, groups = groups, yid = "YFP")
boxData(data, rng = 7, groups = groups, yid = "YFP", type = "bar")

gives you boxplots or barplots (with standard errors or 95% confidence intervals as errors bars) of the selected data at 7 h (or the time-point closest to 7h with option interpolate=FALSE).

TODO:

• dose-response curves from platemap annotation

Retrieve and Process Data

The OD growth curves were nicely aligned, however, you may still want to normalize your data by OD and look at YFP expression in dependence of OD. You can getData and addData, e.g., to calculate YFP/OD:

yfp <- getData(data, "YFP")
od <- getData(data, "OD")
data <- addData(data, dat = yfp/od, ID = "YFP/OD", col = wavelength2RGB(459))
viewGroups(data, groups2 = groups, lwd.orig = 0, yids = c("OD","YFP/OD"))

Interpolate to new x-axis

You could also plot data against another x-axis, any data set in your data, e.g., by using xid="OD" in the call to viewPlate or viewGroup. However, since the x-axis is distinct for each data point, you won't get the nice 95% confidence intervals. For this we can interpolate data to a common value. This generates a new data structure, and the two should not be mixed up:

od.data <- interpolatePlateData(data, xid = "OD")
viewGroups(od.data, groups2 = groups, yids = c("YFP","YFP/OD"))

And now we are ready for nice compact result figure:

boxData(od.data, rng = 0.7, groups = groups, yid = "YFP/OD", type = "bar")

raim/platexpress documentation built on Jan. 18, 2022, 1:41 p.m.