Package for qPCR analysis made especially for data obtained with Viia 7 Real-time PCR system.
You can also find explanation about this package on https://scil-leuven.github.io/qpcrviia7/
Make sure you have some knowledge about R and tidyverse. You can find R and tidyverse tutorials online, a good website with online courses is Datacamp. Rmarkdown is a way to easily report your data, the example analysis that you find in this repository make use of this. You can find a tutorial here.
For a guideline how to set-up everything on mac click here.
install.packages(c("devtools", "knitr" ,"tidyverse", "Hmisc", "ggthemes", "RColorBrewer"))
devtools::install_github("SCIL-leuven/qpcrviia7")
devtools::build_vignettes()
(Possible that this doesn't work because it requires pandoc)or Open .Rmd file in folder vignettes and press Knit to html
Import all the raw data as an excel file without any selections. In the basic template on the Viia7 genes are called Target and samples Sample. You call the first gene Target 1, the second Target 2 and so on. You do the same for the samples. If you do this on new experiments it's the easiest to call everything Target and Sample, else you can adapt the data file. The third sheet named results from row 35 should look like this:
Well | Well Position |...| Sample Name | Target Name |...| CT | CT Treshold |...| Tm1 | Tm2 | Tm3 -----|---------------|---|-------------|-------------|---|----|-------------|---|-----|-----|----- 1 | A1 |...| Sample 1 | Target 1 |...| 21 | 0.4 |...| 79 | ... | ... 2 | A2 |...| Sample 2 | Target 1 |...| 24 | 0.4 |...| 79 | ... | ... 3 | A3 |...| Sample 1 | Target 2 |...| 18 | 0.4 |...| 70 | 82 | 89
IMPORTANT: For the moment all these input paremeters are mandotory! The use of melt curves will become optional in the future.
Two annotation files need to be created.
One for primers, this one should have two columns: one Target column that resembles the names you gave to the primers. I recommend to use the basic names from the machine: Target 1, Target 2 ... The other columns are the gene names you want to analyze and if these are housekeeping genes or not.
Target | Gene | HKG ---------|-------|----- Target 1 | Psma3 | Y Target 2 | Rab35 | Y Target 4 | Sca1 | N
Another annotation file holds informatation about the samples. This also has one column named Sample that resembles the names you gave the samples on the machine, I recommend to name these: Sample 1, Sample 2 ... The other columns contain specific information regarding your experiment. Also specify if you used a blanc, this will make it easy for to inscept and remove blancs. See the example here:
Sample | Blanc |Cell_type | Condition | Mouse | Passage | ... ----------|-------|-----------|-----------|-----------|-----------|------ Sample 1 | N |TA | healthy | C57BL6-1 | NA | ... Sample 2 | N |TA | healthy | C57BL6-2 | NA | ... Sample 3 | Y | | | | |
IMPORTANT: Don't put column names with spaces in them, always use an underscore!
Example analyses were made with Rmarkdown, this is a very neat tool for reporting and makes it possible to run a script in chunks and afterwards knit it into an html or PDF file and even presentations. You can open the .rmd file to run the analysis in chunks by yourself.
To check the example analyses run this code in the terminal:
devtools::build_vignettes()
You can also run the code chunk by chunk with Rmarkdown. Open an .Rmd file in the folder vignettes and run the chunks by pressing on the green play button top right.
Basic steps of this workflow are: 1. Read and prepare files * read_qpcr() * read_annotation() * list_bad_tech_rep() * qc_tech_rep() * join_and_clean_qpcr() 2. Quality control * filter_sample() * remove_sample() * split_genes() * set_min_max_CT() * list_double_meltcurves() * remove_bad_meltcurves() * qc_hkg() * remove_primer() * remove_CT() * qc_mean_hkg() * remove_outlier() 3. Calculate Delta CT * calculate_DCT() * plot_scatter() * plot_scatter_per_gene()
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