initial commit

  pt.size = 0.5,

  kde2d.n = 1000,

  contour.nlevels = 100,

  bg.color='#F9F2E4'

  bg.color='#F9F2E4',

  col.use=NULL

  ) {

  data_plot %>%

    dplyr::group_by(Cluster) %>%

  z <- MASS::kde2d(data_plot$x, data_plot$y, n=kde2d.n)

  celltypes<-names(table(data_plot$Cluster))

  cell_colors <- scales::hue_pal()(length(celltypes))

  if(!is.null(col.use)){

    cell_colors=col.use

  }

  circos.clear()

  par(bg = bg.color)

  circos.par(cell.padding=c(0,0,0,0), track.margin=c(0.01,0),"track.height" = 0.01, gap.degree =c(rep(2, (length(celltypes)-1)),12))

  pt.size = 0.5,

  kde2d.n = 1000,

  contour.nlevels = 100,

  bg.color = "#F9F2E4"

  bg.color = "#F9F2E4",

  col.use = NULL

)

}

\arguments{

---

title: "plot_qPCR"

output: rmarkdown::github_document

vignette: >

  %\VignetteIndexEntry{plot_qPCR}

  %\VignetteEngine{knitr::rmarkdown}

  %\VignetteEncoding{UTF-8}

---

# qPCR-processing

## A script to process the Cq file generated by the qPCR machine in our lab

### How to use:

#### 1. In R <br />

```{r, include = FALSE}

library(plot1cell)

plot_qpcr(qPCR_file="cq.csv", metadata_file="metadata.csv", ref_gene="GAPDH", ref_sample="A1", file_name="qPCR_plot")

```

Usage: plot_qpcr.R [options]

Options:

	-q, --cqfile

		The Cq file from qPCR machine. Must be in csv format.

	-m, --metadata

		A metadata file to assign your samples into groups. Must be in csv format.

	-g, --refgene

		The gene used for normalization. e.g. GAPDH.

	-s, --refsample

		The sample used as reference sample. e.g. sample from the control group.

	-n, --name

		The output file name

	-h, --help

		Show this help message and exit

Here is an example for the metadata file (please keep the column names exactly as "Sample" and "Group"):<br />

 Sample |  Group

---  | ---

A1 |  Ctrl

A2 | Ctrl

A3 | Ctrl

A4 | Disease 

A5 | Disease  

A6 | Disease  

A7 | Treatment 

<br />

#### 2. Output files  <br />

There are three output files produced from this script. The csv file contains the average quantitative value (2^-ΔΔCt) for each sample (and each gene) after normalized by the reference gene (e.g. GAPDH) and the reference sample (e.g. sample from the control group). This file can be input into Graphpad Prism. The txt file includes all statistics from comparisons of any two given groups. If the run has two groups only, Welch's t test will be performed. Otherwise, one-way ANOVA with post-hoc Tukey's test will be performed. Finally, the tiff file is a boxplot graph to visualize the gene expression across groups. <br />

Example of the csv file output:

 Sample | Gene1 |  Gene2 | Group

--- | --- | --- | ---

A1 | 1.00 | 1.00 | Ctrl

A2 | 0.85 | 1.12 |Ctrl

A3 | 0.89 | 1.10 |Ctrl

A4 | 0.93 | 1.04 |Disease 

A5 | 1.08 | 0.86 |Disease  

A6 | 0.99 | 1.16 |Disease  

A7 | 0.98 | 0.87 |Treatment 

<br />

Example of the txt file output: <br />

$`Gene3 ~ Group` <br />

  Tukey multiple comparisons of means <br />

    95% family-wise confidence level <br />

<br />

Fit: aov(formula = x, data = new.data2) <br />

$Group <br />

Comparison  |    diff   |     lwr    |   upr  |   p adj 

 --- | --- | --- | --- | ---

Disease-Ctrl  |   0.3255648 | -0.1188723 | 0.7700018 | 0.1408691 

Treatment-Ctrl   |    0.1660909 | 0.2216539 | 1.1105280 | 0.8805601 

Treatment-Disease | 0.3405262 | -0.1039109 | 0.7849633 | 0.1234654 

<br />

Example of the tiff output:<br />

<br />

![alt text](https://github.com/HaojiaWu/qPCR-processing/blob/main/plate11.png)