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142 lines
4.3 KiB
142 lines
4.3 KiB
---
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title: "CitFuns"
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output: rmarkdown::html_vignette
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description: |
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How to use CitFuns functions.
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vignette: >
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%\VignetteIndexEntry{CitFuns Package}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE, message = F, warning = F, error = F)
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```
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## Installation
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In order to install CitFuns package from Git reppository, you must install `devtools` package:
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```{r, eval=F}
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install.packages('devtools')
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```
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In order to install it, you will have to install (if not already done) [Rtools](https://cran.r-project.org/bin/windows/Rtools/rtools40.html).
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You will also need [Git](https://git-scm.com/downloads) installed in your computer.
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Now we are ready to install CitFuns package:
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```{r, eval=F}
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devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git", build_vignettes = T)
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```
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## Update
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Any time you want to update the package, you must *reinstall* it:
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```{r, eval=F}
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detach("package:CitFuns", unload = TRUE) # Only required if you have loaded the package in this session.
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devtools::install_git("https://git.ratg.cat/marcelcosta/CitFuns.git")
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```
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## ggheatmap
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**ggheatmap** generates ggplot heatmaps easily.
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We start by loading required packages, including *CitFuns*
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```{r}
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library(tidyverse)
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library(CitFuns)
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```
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Now we will create an example dataframe:
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```{r}
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df<-data.frame("pats"=paste0("PAT", 1:20), "CytA"=rnorm(20,5), "CytB"=rnorm(20,5),
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"CytC"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)),"CytD"=rnorm(20,5),
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"CytE"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)),"CytF"=rnorm(20,5),
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"CytG"=c(rnorm(5,10),rnorm(5,5),rnorm(5,10),rnorm(5,5)))
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df<-gather(df, Cyt, Value,-pats)
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head(df)
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```
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Usually, this package works with the dataframes in **Long** format, as it is intended in ggplot workflow.
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And now we will generate the heatmap.
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```{r, fig.width=8}
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ggheatmap(df)
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```
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As we can observe, X and Y axis are sorted by cluster detection (using *hclust*).
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*ggheatmap* groups the results if more there is more than one observation for each X-Y coordinate. By default, it calculates the mean, but the median can also be used instead. To show this, we will create another variable, "Met", and we plot Cytokine expression *versus* Met status.
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```{r, fig.width=8}
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clinics<-data.frame("pats"=df$pats %>% unique, "Met"=rep(c("0","1"), 10))
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df<-merge(df, clinics)
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ggheatmap(df, x="Cyt",y="Met", grouping = "median")
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```
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By default, *other variables not used are eliminated* during the grouping process. However, if you want to further use them (for faceting, for example), you can use the *exclude_group* parameter to keep them in the data.frame.
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```{r, fig.width=8}
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ggheatmap(df, exclude_group = "Met")+facet_grid(.~Met, scales = "free")
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```
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You can specify a color instead of being transparent to the tiles:
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```{r, fig.width=8}
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ggheatmap(df, color="black")
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```
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Finally, you can scale the heatmap either by rows or by columns:
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```{r, fig.width=8}
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ggheatmap(df, scale="rows")
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```
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It is worth noticing that ggheatmap outputs a ggplot object, so you can further modify it as you are used to:
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```{r, fig.width=8, fig.height=5}
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ggheatmap(df)+
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scale_fill_gradient(low = "black", high = "yellow")+
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scale_y_discrete(position = "right")+
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theme(legend.position = "bottom")
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```
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## ggcorrplot
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ggcorrplot generates a correlation matrix. Using the same example dataframe, you have to specify which variable and value columns will be used to test correlation.
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```{r, fig.width=5, fig.height=4}
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ggcorrplot(df, var = "Cyt", value = "Value")
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```
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You can specify a color for the tile lines, transparent by default:
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```{r, fig.width=5, fig.height=4}
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ggcorrplot(df, var = "Cyt", value = "Value", color = "white")
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```
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By default, ggcorrplot converts the p-value into *star* significance equivalence. You can show the pvalue or nothing ("none").
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```{r, fig.width=5, fig.height=4}
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ggcorrplot(df, var = "Cyt", value = "Value", color = "white", stat="pval")
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```
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ggcorrplot uses "pearson" by default to obtain a correlation coeficient, although "spearman" is also available.
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```{r, fig.width=5, fig.height=4}
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ggcorrplot(df, var = "Cyt", value = "Value", color = "white", stat="pval", method="spearman")
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```
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Finally, you can show only the upper part or the lower part of the specular matrix.
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```{r, fig.width=5, fig.height=4}
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ggcorrplot(df, var = "Cyt", value = "Value", color = "black", tri="lower")
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```
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