flow_multi_timepoint_analysis.Rmd

---
title: "Gresham Lab Flow Cytometry Multi Timepoint Analysis"
date: "`r Sys.Date()`"
output: html_notebook
---

## R Markdown

This is the code to generate figures about CNV dynamics from aggregated flow cyto data.


This notebook contains the code necessary to to generate figures from flow cytometry data across timepoints in the Gresham Lab. 

To analyze flow cytometry data, you MUST use the latest version of this code, available on the [Gresham Lab github](https://github.com/GreshamLab/flow).

**Experimental overview**

Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.   
If you still see this text it means that you have not described the experiment and whatever follows is meaningless.

**Requirements**

In order to run this code you need:

* have run the "Gresham Lab Floww Cytometry Single Timepoint Analysis" and saved the proportional data, the filtered individual flow data, and/or the statistical data on one or more flowcyto runs


**Output**  
This script combines the data from multiple flow cyto runs and can save the combined runs in the following formats

1. As a dataframe converted from fcs with all or some of the data.
2. As a .csv with summary statistics (e.g. median FL1 per sample)
3. As a .csv with proportional data  (e.g. cells flourescent in population)

**Libraries**

```{r Load libs}

#Load libraries
library(openCyto)
library(ggcyto)
library(tidyverse)
library(ggjoy)
library(ggforce)
```


```{r User Defined Variables}
#working directory
dir = '.'

#file location
path.data = "/Users/Brandt/Google Drive/MiniStatRun_10_2018/2019-01-24/"


#name of the experiment
name <- "LTEE_mCitrine_GAP1_Variants"

#date of extracted data to use

#style of input
#Reading Data in
#Read in and combine individual Proportional Data
read.prop <- "No"
#Read in and combine individual DataFrames 
read.df <- "No"
#Read in and combine individual Experiment Statistics
read.stats <- "No"

#Appending Data to previous data
#use the date to identify the combined data file you wish to append a new file to
appendto.date <- "2019-02-14"
#Use the Timepoint identifier and the date to identify what file you wish to add to your old file
append.timepoint <- "T00" 
append.date <- "2019-01-24"

#Read in combined Proportional data file and append a single Proportional Data timepoint
append.prop <- "Yes"
#Read in combined DataFrame file and append a single DataFrame timepoint
append.df <- "Yes"
#Read in combined Statistics data file and append a single Statistics Data timepoint
append.stats <- "Yes"

#Saving curent Files
#Save combined Proportional Data
save.prop <- "Yes"
#Save combined DataFrame - Individual data points
save.df <- "Yes"
#Save combined Experiment Statistics
save.stats <- "Yes"

#Loading
#Date of combined data files you wish to load
load.date <- ""
#Load combined Proportional Data
load.prop <- "No"
#Load combined DataFrame - Individual data points
load.df <- "No"
#Load combined Experiment Statistics
load.stats <- "No"


#filenames
file.prop <- paste(name,"_propAllData_",Sys.Date(),sep="")
file.df <- paste(name,"_dfAllData_",Sys.Date(),sep="")
file.stats <- paste(name,"_statsAllData_",Sys.Date(),sep="")

```


```{r Read or load in  Data}


#Reading in Data

#Read in and Combine all Proportional Data files found in path

#Creates a list of proportional data files, identifed by have _prop_ and .csv in the file name
prop.list <- list.files(path=path.data, pattern = "_prop_[[:print:]]*.csv")

#Load each file and combine them
if(read.prop=="Yes"){
  data.prop <- read_csv(paste(path.data,prop.list[1], sep=""))
  for(i in 2:length(prop.list)){
    data.prop_temp <- read_csv(paste(path.data,prop.list[i], sep=""))
    data.prop<- bind_rows(data.prop, data.prop_temp)
  }
  data.prop <- select(data.prop,-c(1))
}

#Read in and Combine data frames found in path

#Creates a list of data frames in Rdata files, identifed by have _ddf_ and .Rdata in the file name
df.list <- list.files(path=path.data, pattern = "_df_[[:print:]]*.Rdata")

#Load each file and combine them
if(read.df=="Yes"){
  load(paste(path.data,df.list[1], sep=""))
  data.df <- filtered.data
  for(i in 2:length(df.list)) {
    load(paste(path.data,df.list[i], sep=""))
    data.df_temp<-filtered.data
    data.df <- rbind(data.df, data.df_temp)
  }
}

#Read in and Combine Statistical Data in path

#Creates a list of Statistical data files, identifed by have _stats_ and .csv in the file name
stats.list <- list.files(path=path.data, pattern = "_stats_[[:print:]]*.csv" )

#Load each file and combine them
if(read.stats=="Yes"){
  data.stats <- read_csv(paste(path.data,stats.list[1], sep=""))
  for(i in 2:length(stats.list)){
    data.stats_temp <- read_csv(paste(path.data,stats.list[i], sep=""))
    data.stats<- bind_rows(data.stats, data.stats_temp)
  }
  data.stats <- select(data.stats,-c(1))
} 


#Append Data - Requires a previously saved all data file as well as a new timepoint

#Read in and add new Proportinal timepoint
if(append.prop=="Yes"){
  data.prop <- read_csv(paste(path.data,name,"_propAllData_",appendto.date,".csv",sep=""))
  
  data.prop_temp <- read_csv(paste(path.data,list.files(path=path.data, pattern =paste(append.timepoint,"_prop_",append.date,sep="")), sep=""))
  data.prop<- bind_rows(data.prop, data.prop_temp)
  
  data.stats <- select(data.stats,-c(1))
}
#Read in and and add new data frames
if(append.df=="Yes"){
  load(paste(path.data,name,"_dfAllData_",appendto.date,".Rdata",sep=""))
  data.df <- filtered.data
  
  load(paste(path.data,list.files(path=path.data, pattern =paste(append.timepoint,"_df_",append.date,sep="")), sep=""))
  data.df_temp<-filtered.data
  data.df <- rbind(data.df, data.df_temp)
}
#Read in and add new Statistical Data timepoint
if(append.stats=="Yes"){
  data.stats <- read_csv(paste(path.data,name,"_statsAllData_",appendto.date,".csv",sep=""))
  
  data.stats_temp <- read_csv(paste(path.data,list.files(path=path.data, pattern =paste(append.timepoint,"_stats_",append.date,sep="")), sep=""))
  data.stats<- bind_rows(data.stats, data.stats_temp)
  
  data.stats <- select(data.stats,-c(1))
}


#Saving Current Data

#Save Proportional Data
if(save.prop=="Yes"){
  write.csv(data.prop, file= paste(path.data,file.prop,".csv",sep=""), row.names=TRUE, quote=F)
}
#Save dataframes
if(save.df=="Yes"){
  save(data.df, file=paste(path.data,file.df,".Rdata",sep=""))
}
#Save Statistical Data
if(save.stats=="Yes"){
  write.csv(data.stats, file= paste(path.data,file.stats,".csv",sep=""), row.names=TRUE, quote=F)
}

#Loading Data

#Read in and Combine Proportional Data
if(load.prop=="Yes"){
  data.prop <- read_csv(paste(path.data,name,"_propAllData_",load.date,".csv",sep=""))
}
#Read in and Combine data frames
if(load.df=="Yes"){
  load(paste(path.data,name,"_dfAllData_",load.date,".Rdata",sep=""))
}
#Read in and Combine Statistical Data
if(load.stats=="Yes"){
  data.stats <- read_csv(paste(path.data,name,"_statsAllData_",load.date,".csv",sep=""))
}
```

##Plots