Leah McIsaac, breast_cancer_cells
####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<-read_csv(file="breast_cancer_cells.csv")
#and then view it
view(breast_cancer_cells)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 a matrix of just raw the data
breast_cancer_cells_mat1<-breast_cancer_cells %>% select(MCF10A_1:MCF7_2:MDA231_2:MDA468_2:SKBR3_2) %>% as.matrix() %>% round(.,2)
# what does that look like?
head(breast_cancer_cells_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## make a heatmap from the data
pheatmap(breast_cancer_cells_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? they are similar. 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 SKBR3 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
breast_cancer_cells2<-breast_cancer_cells %>% 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(breast_cancer_cells2)
#did it create your new columns? Yes 5
## then make some new columns that store the log ratios of the variable means you just created, as compared to the control
breast_cancer_cells2<-breast_cancer_cells2 %>% 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(breast_cancer_cells2)
## [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? Yes 4 new## 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, _SKBR3_)
breast_cancer_cells2<-breast_cancer_cells2 %>% mutate(neglog_SKBR3=-log10(pvalue_SKBR3_vs_MCF10A))
## Use ggplot to plot the log ratio of ____ against the -log p-value ____
volcano_plot<-breast_cancer_cells2 %>% ggplot(aes(x=log_SKBR3,y=neglog_SKBR3,description=Gene_Symbol))+
geom_point(alpha=0.7,color="blue")
#to view it, type:
volcano_plot
## BETTER VOLCANO PLOT
## Define the significant ones (by using ifelse and setting # parameters) so they can be colored
breast_cancer_cells2<-breast_cancer_cells2 %>% mutate(significance=ifelse((log_SKBR3>5.3 & neglog_SKBR3>1.4),"UP", ifelse((log_SKBR3<c(-2) & neglog_SKBR3>3.05),"DOWN","NOT SIG")))
## Some standard colors
plain_cols3<-c("red","gray","blue")
## volcano plot as before with some added things
better_volcano_plot<-breast_cancer_cells2 %>% ggplot(aes(x=log_SKBR3,y=neglog_SKBR3,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 SKRB3 compared to control (MCF10A",y="-log(p-value)")
#to view it, type
better_volcano_plot
#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)
##INTERPRETATION##
## Significant up regulated are: C17orf28, GPNMB and CAPS
## Significant down regulated are: APOA1. HLA-A and MYO1B
#Why? How?Barplots of significant points of interest
EXAMPLES OF A COUPLE PROTEINS or GENES
# Make a pivot longer table of the C19 data for Control_2h_1:Virus_24h_2
breast_cancer_cells_long<-pivot_longer(breast_cancer_cells2, cols = c(MCF10A_1:SKBR3_2), names_to = 'variable')%>% select(-c(pvalue_SKBR3_vs_MCF10A:significance))
head(breast_cancer_cells_long)
## # A tibble: 6 × 8
## Gene_Symbol Description Peptides pvalue_MCF7_vs_MCF10A pvalue_MDA231_vs_MCF…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 NES Nestin OS=H… 7 0.542 0.0185
## 2 NES Nestin OS=H… 7 0.542 0.0185
## 3 NES Nestin OS=H… 7 0.542 0.0185
## 4 NES Nestin OS=H… 7 0.542 0.0185
## 5 NES Nestin OS=H… 7 0.542 0.0185
## 6 NES Nestin OS=H… 7 0.542 0.0185
## # ℹ abbreviated name: ¹pvalue_MDA231_vs_MCF10A
## # ℹ 3 more variables: pvalue_MDA468_vs_MCF10A <dbl>, variable <chr>,
## # value <dbl>
## 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<-breast_cancer_cells_long %>% filter(Gene_Symbol=="APOA1" | Gene_Symbol=="HLA-A" | Gene_Symbol=="MYO1B")
head(Examples_Down)
## # A tibble: 6 × 8
## Gene_Symbol Description Peptides pvalue_MCF7_vs_MCF10A pvalue_MDA231_vs_MCF…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 HLA-A HLA class I… 3 0.0312 0.122
## 2 HLA-A HLA class I… 3 0.0312 0.122
## 3 HLA-A HLA class I… 3 0.0312 0.122
## 4 HLA-A HLA class I… 3 0.0312 0.122
## 5 HLA-A HLA class I… 3 0.0312 0.122
## 6 HLA-A HLA class I… 3 0.0312 0.122
## # ℹ abbreviated name: ¹pvalue_MDA231_vs_MCF10A
## # ℹ 3 more variables: pvalue_MDA468_vs_MCF10A <dbl>, variable <chr>,
## # value <dbl>
## 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=factor(variable,levels=c('MCF10A_1','MCF10A_2','MCF7_1','MCF7_2','MDA231_1','MDA231_2','MDA468_1','MDA468_2','SKBR3_1','SKBR3_2')),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<-breast_cancer_cells_long %>% filter(Gene_Symbol=="C17orf28" | Gene_Symbol=="GPNMB" | Gene_Symbol=="CAPS")
head(Examples_Up)
## # A tibble: 6 × 8
## Gene_Symbol Description Peptides pvalue_MCF7_vs_MCF10A pvalue_MDA231_vs_MCF…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 CAPS Calcyphosin… 7 0.00456 0.297
## 2 CAPS Calcyphosin… 7 0.00456 0.297
## 3 CAPS Calcyphosin… 7 0.00456 0.297
## 4 CAPS Calcyphosin… 7 0.00456 0.297
## 5 CAPS Calcyphosin… 7 0.00456 0.297
## 6 CAPS Calcyphosin… 7 0.00456 0.297
## # ℹ abbreviated name: ¹pvalue_MDA231_vs_MCF10A
## # ℹ 3 more variables: pvalue_MDA468_vs_MCF10A <dbl>, variable <chr>,
## # value <dbl>
Example_plot_up<-Examples_Up %>% ggplot(aes(x=factor(variable,levels=c('MCF10A_1','MCF10A_2','MCF7_1','MCF7_2','MDA231_1','MDA231_2','MDA468_1','MDA468_2','SKBR3_1','SKBR3_2')),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.
gene_of_interest_upregulated <- breast_cancer_cells %>% select(Gene_Symbol,Description) %>% filter(Gene_Symbol=="C17orf28"| Gene_Symbol=="GPNMB" | Gene_Symbol=="CAPS")
head(gene_of_interest_upregulated)
## # A tibble: 3 × 2
## Gene_Symbol Description
## <chr> <chr>
## 1 CAPS Calcyphosin OS=Homo sapiens GN=CAPS PE=1 SV=1
## 2 GPNMB Isoform 2 of Transmembrane glycoprotein NMB OS=Homo sapiens GN=GP…
## 3 C17orf28 Isoform 2 of UPF0663 transmembrane protein C17orf28 OS=Homo sapie…
gene_of_interest_downregulated <- breast_cancer_cells %>% select(Gene_Symbol,Description) %>% filter(Gene_Symbol=="APOA1"| Gene_Symbol=="HLA-A" | Gene_Symbol=="MYO1B")
head(gene_of_interest_downregulated)
## # A tibble: 6 × 2
## Gene_Symbol Description
## <chr> <chr>
## 1 HLA-A HLA class I histocompatibility antigen, A-23 alpha chain OS=Homo …
## 2 HLA-A HLA class I histocompatibility antigen, A-30 alpha chain OS=Homo …
## 3 HLA-A HLA class I histocompatibility antigen, A-2 alpha chain OS=Homo s…
## 4 MYO1B Isoform 2 of Myosin-Ib OS=Homo sapiens GN=MYO1B
## 5 HLA-A HLA class I histocompatibility antigen, A-1 alpha chain OS=Homo s…
## 6 APOA1 Apolipoprotein A-I OS=Homo sapiens GN=APOA1 PE=1 SV=1WRAP 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!