####Run through this example and try to understand what is going on with the data

####So, lets load RMarkdown

library(rmarkdown)

set messages to FALSE on everything (prevents certain boring things from being shown in the results)

knitr::opts_chunk$set(echo = TRUE, message=FALSE,warning=FALSE,collapse = TRUE)

PACKAGES

library(reshape2)
library(ggplot2)
library(dplyr)
library(plotly)
library(viridis)
library(data.table)
library(pheatmap)
library(tidyverse)
library(ggthemes)
library(clipr)
library(tidyr)
library(Rcpp)

COLORS

mycolors<-c(viridis(15))
felix_cols<-mycolors[c(5,2)]
felix_4cols<-mycolors[c(15,10,8,2)]
plain_cols1<-c("blue","gray")
plain_cols2<-c("red","gray")

pats_cols<-colorRampPalette(c("#FDE725FF", "white","#440154FF"))(21)
leos_cols<-colorRampPalette(c("white","blue"))(10)

LOAD DATA AND MAKE AN OVERALL HEATMAP

## load the dataset (breast cancer cells)
BCC_data<-read_csv(file="breast_cancer_cells.csv")

now let’s visualize the dataset and look for initial trends. We can do this by making a matrix so and then a heatmap to visualize the data

#make some new columns that store the log ratios

#make a matrix of just raw the data
BCC_mat1<-BCC_data %>% select(MCF10A_1:SKBR3_2) %>% as.matrix() %>% round(.,2)
head(BCC_mat1)
##      MCF10A_1 MCF10A_2 MCF7_1 MCF7_2 MDA231_1 MDA231_2 MDA468_1 MDA468_2
## [1,]     9.54     4.58   5.07   5.42    25.43    27.42     4.56     3.88
## [2,]    14.00    11.58   6.49   6.64     9.80    10.31     6.84     8.75
## [3,]    10.22     8.29  11.55  10.82    12.48    10.11     9.54     8.46
## [4,]     9.00     6.35  13.40  14.82    14.94    11.33     6.87     7.92
## [5,]     8.21     4.44  12.08   9.82    16.51    12.34    15.43     8.50
## [6,]    12.51    15.84   8.05   8.38     6.78     8.46     4.48     7.18
##      SKBR3_1 SKBR3_2
## [1,]    8.46    5.64
## [2,]   11.91   13.67
## [3,]    9.10    9.43
## [4,]    8.54    6.82
## [5,]    7.93    4.75
## [6,]   13.27   15.05

# what does that look like?

## make a heatmap from the data
pheatmap(BCC_mat1, color=pats_cols,cellwidth=30,cellheight=.03,cluster_cols=FALSE,cluster_rows=TRUE,legend=TRUE,fontsize = 7,scale="column")


##INTERPRETATION##
## What can you see in this figure? are the repeated measures/reps similar or different? What does this say about the precision and accuracy of them? 
##How does the control compare to the variables? Is this what you might expect? Why? What would you look for in the literature to support this idea?  

#looks like _______ line  is different than the other cells lines.  We will take a closer look at that in the next section, once we've done some further data manipulations

Data Manipulation


## first, make new columns that combine the two reps for each variable by averaging them  

BCC_data2<-BCC_data %>% mutate(
  mean_MCF10A= ((MCF10A_1 + MCF10A_2)/2),
  mean_MCF7= ((MCF7_1 + MCF7_2)/2), 
  mean_MDA231= ((MDA231_1 + MDA231_2)/2), 
  mean_MDA468= ((MDA468_1 + MDA468_2)/2),
  mean_SKBR3= ((SKBR3_1 + SKBR3_2)/2)) 
 
view(BCC_data2)
 

#did it create your new columns?

## then make some new columns that store the log ratios of the variable means you just created, as compared to the control

BCC_data2<-BCC_data2 %>% mutate(
  log_MCF7=log2(mean_MCF7/mean_MCF10A), 
  log_MDA231=log2(mean_MDA231/mean_MCF10A), 
  log_MDA468=log2(mean_MDA468/mean_MCF10A),
  log_SKBR3=log2(mean_SKBR3/mean_MCF10A)) 
 
colnames(BCC_data2)
##  [1] "Gene_Symbol"             "Description"            
##  [3] "Peptides"                "MCF10A_1"               
##  [5] "MCF10A_2"                "MCF7_1"                 
##  [7] "MCF7_2"                  "MDA231_1"               
##  [9] "MDA231_2"                "MDA468_1"               
## [11] "MDA468_2"                "SKBR3_1"                
## [13] "SKBR3_2"                 "pvalue_MCF7_vs_MCF10A"  
## [15] "pvalue_MDA231_vs_MCF10A" "pvalue_MDA468_vs_MCF10A"
## [17] "pvalue_SKBR3_vs_MCF10A"  "mean_MCF10A"            
## [19] "mean_MCF7"               "mean_MDA231"            
## [21] "mean_MDA468"             "mean_SKBR3"             
## [23] "log_MCF7"                "log_MDA231"             
## [25] "log_MDA468"              "log_SKBR3"
 

#did it create your new columns?
## ALTERNATIVE CHOICE: make a matrix of just log values the data and a heat map of that. How do the two heat maps compare? This is not possible if your values contain 0's

Diving deeping with VOLCANO PLOTS

##BASIC VOLCANO PLOT

## Add a column that stores the negative log of the pvalue of interest (in this case, _____)
BCC_data2<-BCC_data2 %>% mutate(neglog_MCF7=-log10(pvalue_MCF7_vs_MCF10A))


## Use ggplot to plot the log ratio of ____ against the -log p-value ____

volcano_plot_MCF7<-BCC_data2 %>% ggplot(aes(x=log_MCF7,y=neglog_MCF7,description=Gene_Symbol))+
  geom_point(alpha=0.7,color="royalblue")
#to view it, type: 
volcano_plot_MCF7


  
## BETTER VOLCANO PLOT

## Define the significant ones (by using ifelse and setting # parameters) so they can be colored
BCC_data2<-BCC_data2 %>% mutate(significance=ifelse((log_MCF7>2 & neglog_MCF7>4.50),"UP", ifelse((log_MCF7<c(-2) & neglog_MCF7>3.99),"DOWN","NOT SIG")))

## Some standard colors
plain_cols3<-c("red","gray","blue")

## volcano plot as before with some added things
better_volcano_plot_MCF7<-BCC_data2 %>% ggplot(aes(x=log_MCF7,y=neglog_MCF7,description=Gene_Symbol,color=significance))+
  geom_point(alpha=0.7)+
  scale_color_manual(values=plain_cols3)+
  xlim(-6,6)+
  theme_bw()+
  theme(axis.text = element_text(colour = "black",size=14))+
  theme(text = element_text(size=14))+
  labs(x="log ratio of MCF cells compared to control MCF10A cells",y="-log(p-value)")



  
#to view it, type
better_volcano_plot_MCF7


#check viewer and / or plots to see it 

## use ggplotly to see hover over the points to see the gene names. Record these for the next step 
ggplotly(better_volcano_plot_MCF7)


##INTERPRETATION##
## Significant up regulated are: _VAV2__, _EPPK1__ and __C17orf28___
## Significant down regulated are: _CTSZ____. __APOA1___ and __ADCK3__
#Why? How?

Barplots of significant points of interest

EXAMPLES OF A COUPLE PROTEINS or GENES

# Make a pivot longer table of the BCC data for MCF10A_1:SKBR3_2
BCC_long<-pivot_longer(BCC_data2, cols = c(MCF10A_1:SKBR3_2), names_to = 'variable')%>% select(-c(pvalue_MCF7_vs_MCF10A:significance))

head(BCC_long)
## # A tibble: 6 × 5
##   Gene_Symbol Description                             Peptides variable value
##   <chr>       <chr>                                      <dbl> <chr>    <dbl>
## 1 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MCF10A_1  9.54
## 2 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MCF10A_2  4.58
## 3 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MCF7_1    5.07
## 4 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MCF7_2    5.42
## 5 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MDA231_1 25.4 
## 6 NES         Nestin OS=Homo sapiens GN=NES PE=1 SV=2        7 MDA231_2 27.4

## based on the gene symbols from plotly, select a few proteins from the table. Select just the data and gene symbols and pivot longer
Examples_Down<-BCC_long %>% filter(Gene_Symbol=="CTSZ" | Gene_Symbol=="APOA1" | Gene_Symbol=="ADCK3") 

head(Examples_Down)
## # A tibble: 6 × 5
##   Gene_Symbol Description                                Peptides variable value
##   <chr>       <chr>                                         <dbl> <chr>    <dbl>
## 1 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MCF10A_1 19.8 
## 2 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MCF10A_2 19.8 
## 3 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MCF7_1    0.83
## 4 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MCF7_2    0.91
## 5 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MDA231_1 11.2 
## 6 CTSZ        Cathepsin Z OS=Homo sapiens GN=CTSZ PE=1 …        8 MDA231_2 15.4


  
## make barplots facetted by Gene Symbol (when working with other data sets - change x=order to x = variable)
Example_plot_down<-Examples_Down %>% 
  ggplot(aes(x=variable,y=value))+ 
  geom_bar(stat="identity",fill="red")+
  facet_wrap(~Gene_Symbol)+
  theme_bw()+
  theme(axis.text = element_text(colour = "black",size=10))+
  theme(text = element_text(size=14))+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  labs(x="sample",y="relative intensity")
  
Example_plot_down






  


#check viewer and / or plots to see it

##INTERPRETATION## ## What can you see in this figure? are the repeated measures/reps similar or different? What does this say about the precision and accuracy of them? ##How does the control compare to the variables? Is this what you might expect? Why? What would you look for in the literature to support this idea?

## Same process for the upregulated ones
Examples_Up<-BCC_long %>% filter(Gene_Symbol=="VAV2" | Gene_Symbol=="EPPK1" | Gene_Symbol=="C17orf28")

head(Examples_Up)
## # A tibble: 6 × 5
##   Gene_Symbol Description                                Peptides variable value
##   <chr>       <chr>                                         <dbl> <chr>    <dbl>
## 1 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MCF10A_1  2.76
## 2 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MCF10A_2  2.89
## 3 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MCF7_1   22.4 
## 4 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MCF7_2   22.3 
## 5 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MDA231_1  1.9 
## 6 EPPK1       Uncharacterized protein OS=Homo sapiens G…        4 MDA231_2  1.76


Example_plot_up<-Examples_Up %>% ggplot(aes(x=variable,y=value))+ 
  geom_bar(stat="identity",fill="royalblue")+
  facet_wrap(~Gene_Symbol)+
  theme_bw()+
  theme(axis.text = element_text(colour = "black",size=10))+
  theme(text = element_text(size=14))+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  labs(x="sample",y="relative intensity")
  
Example_plot_up





  


#check viewer and / or plots to see it

##INTERPRETATION## ## What can you see in this figure? are the repeated measures/reps similar or different? What does this say about the precision and accuracy of them? ##How does the control compare to the variables? Is this what you might expect? Why? What would you look for in the literature to support this idea?

#interpretation HINT:insert a chunk and create two seprate lines of code that filter for your specific upregulated genes/proteins of interest and selects for only their gene symbols and descriptions. Do this for the downregulated as well. This will generate two list of the descriptors for each gene of interest, helping you understand your figures. Be sure to view it, not just ask for the head of the table generated.

library(dplyr)

# Upregulated genes of interest
upregulated_genes <- BCC_data %>%
  filter(Gene_Symbol %in% c("VAV2", "EPPK1", "C17orf28")) %>%
  select(Gene_Symbol, Description)

# Downregulated genes of interest
downregulated_genes <- BCC_data %>%
  filter(Gene_Symbol %in% c("ADCK3", "CTSZ", "APOA1")) %>%
  select(Gene_Symbol, Description)

# View full tables

WRAP UP

##now you can knit this and publish to save and share your code. Use this to work with either the brain or breast cells and the Part_C_template to complete your lab 6 ELN. ##Annotate when you have trouble and reference which line of code you need help on ## good luck and have fun!