Lab 13: Analysis of label-free shotgun proteomics data
Jaylie Hicklin
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options(repos=c(CRAN="https://mirror.las.iastate.edu/CRAN/"))C. Analysis of differential protein expression in RStudio.
24. Set the working directory
knitr::opts_knit$set(root.dir = "R:/Biology/BIOL-4129-5129/hickljaa/Lab13_LC-MS")
getwd()## [1] "R:/Biology/BIOL-4129-5129/hickljaa/Lab13_LC-MS"Load the packages needed to prepare and generate the markdown report using the library() function.
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library(rmarkdown)
library(knitr)
library(yaml)25. Check the packages BiocManager, limma, edgeR, vsn, pCaMethods, psych, gplots, and missMethods are installed. If not install them.
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library(BiocManager)
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## cluster26. Install CRAN packages psych, gplots, and missMethods.
install.packages(c("psych", "gplots", "missMethods"))## Installing packages into 'C:/Users/hickljaa/AppData/Local/R/win-library/4.4'
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## C:\Users\hickljaa\AppData\Local\Temp\5\RtmpSQ4S9e\downloaded_packages27. Load the packages using the “library” function. You can also install them from Packages list.
library("limma")
library("edgeR")
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## lowesslibrary(missMethods) Load the mass spectrometer data
28. Load the proteinGroups_filtered.txt data
proteinGroups <-read.table(file = "proteinGroups_Lab13.tabular", header = TRUE, row.names = 1, sep="\t")29. Open the proteinGroups file and explore the data. The Majority protein IDs (second) column contains multiple proteins in most row. This is because the same peptide matches multiple proteins in the database, primarily because these proteins are isoforms.
View(proteinGroups)– How many proteins/protein groups did you identify in this study? This number is displayed below the table.
30. Remove contaminating sequences (reversed sequences, potential contaminants, and proteins identified only by sites). These are indicated with a “+” in columns 70 - 72.
filtered_data = proteinGroups[!proteinGroups$Only.identified.by.site=="+",] # the $ operator is used to subset (extract) a specific part of the data. Here we are pulling elements (rows) from the data when the Only.identified.by.site column contains a +.
filtered_data = filtered_data[!filtered_data$Reverse=="+",] # Here we are pulling elements from the data when Reverse is +.Note that, ! is logical NOT operator in R.
filtered_data = filtered_data[!filtered_data$Potential.contaminant=="+",] # And here we are pulling elements from the data when the values for the Potential.contaminant column is +.– How many proteins/protein groups does the filtered data contain?
31. Extract LFQ values
LFQ = filtered_data[,57:62]32. Zeros cannot be log-transformed. We need to replace them with NA and save the file with the same name.
LFQ[LFQ==0] <- NA33. Make barplots of the dataframe.
total<-(colSums(LFQ[,1:6],na.rm=TRUE)) # to find column totals using the colSums() function and na.rm to ignor missing values
barplot(t(as.matrix(total)),mar=c(5.1, 4.1, 4.1, 2.1)) # vertical barplot of column totals
34. Barplot of percent missing values
col_nas= colSums(is.na(LFQ)) # Total number of NAs per column in a data frame
col_total=colSums (LFQ, na.rm = T, dims = 1) # Column totals
col_percent = (col_nas * 100 )/(nrow(LFQ))# Percentage of missing values per column
barplot(t(as.matrix(col_percent)))
35. Return rows that have at least two non-NA values across all samples.
Filtered_LFQ=LFQ[rowSums(is.na(LFQ))<(length(LFQ)-1),]37. Normalize the raw data using variance stabilization normalization (VSN) method.
Normalized <- justvsn(as.matrix(Filtered_LFQ))38. Use the function meanSdPlot to verify variance stabilization as a plot of empirical standard deviation on the y-axis versus the rank of the average on the x-axis.
meanSdPlot(Normalized, ranks = TRUE)
- Describe the quality of the normalized data based on the meanSdPlot.
39. Distribution of the normalized data.
39a. Plot multiple histograms with density and normal fits on one page using the psyc package.
multi.hist(Normalized, dcol= c("blue","red"),dlty=c("dotted", "solid"), main="histograms with density plots of normalized data_Lab13_38a")
39b. Boxplot is another tool to visualize the distribution of normalized data.
boxplot(Normalized, main="Boxplot of normalized data_Lab13_38b", col=" light blue")
39c. Make MDS plot to visualize data clustering.
plotMDS(Normalized, main="MDS plot of normalized data_Lab13_38c")
- What is your assessment of the normalized data.
Impute missing values
40a. Use the lsaImpute function of the MissMethods package.
imputed<-impute_LS_adaptive(Normalized)40b. Check the imputed samples are ordered correctly.
View(imputed)40c. Visualize the distribution of each sample after imputation using histograms, boxplots, density plots (like a distribution histogram), and plotMDS.
multi.hist(imputed, dcol= c("blue","red"),dlty=c("dotted", "solid"), main="histograms with density plots of normalized and imputed data_Lab13_39b")
boxplot(imputed, main="Boxplot of normalized and imputed data_Lab13_39b", cex.main = 1, col="light blue") 
plotDensities(imputed,legend="topright",inset=c(-3, 0),names(samples), main="Density plots of normalized and imputed data_Lab13_39b") # The inset(x, y) argument can be used to control the location of the legend to the right of the plot. For example, we can make the x argument more negative to push the legend even further to the right and make y more positive to push it lower.## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
## Warning: In density.default(E[, a], n = npoint, na.rm = TRUE, ...) :
## extra argument 'inset' will be disregarded
plotMDS(imputed, main="MDS plot of normalized and imputed data_Lab13_39b", cex.main = 1)
40d. Draw a heatmap of log values using the Coolmap function. The function calls heatmap.2 in the gplots package
coolmap(imputed, "Heatmap of normalized and imputed data_Lab13_39b", cluster.by="de pattern", col=NULL, linkage.row="complete", linkage.col="complete", show.dendrogram="both")
43. Define treatment groups and biological replicates.
If we do not tell R, it will use one of the factors as an intercept (baseline) for comparison based on their alphabetical order. We need to tell R to use the “healthy” tissue as the reference. One way to explicitly set the intercept is to customize the order of the factor levels using the “levels” argument with the factor() function and provide level names in the desired sequence.
Group <-factor(rep(c("healthy","psoriasis"), each=3)) # factor() converts the vector into a factor. rep(c("healthy","psoriasis"), each = 3) creates a vector by repeating the sequence ("healthy", "psoriasis") three times. The sequence is not dynamic — it's defined by you.
Group## [1] healthy healthy healthy psoriasis psoriasis psoriasis
## Levels: healthy psoriasis44. By default, R will use the first factor in alphabetical order as the intercept (reference or baseline) for comparison. In our case, the “healthy” tissue will be used as the reference automatically and we donot need to remind R. If we want to use factor “B” to be the intercept and not factor “A”, we can use the function “factor” to do so. group<-factor(group, levels = c(“Normal”, “Tumor”)).
45. Create the design matrix.
Design <- model.matrix(~Group) # Here we only have two groups and one possible comparison for differential expression of proteins. We can remove the intercept in the model.matrix by adding 0, i.e., (~0+group). This will allow specific pairwise comparisons without sacrificing one level as a reference. However, since we only have two levels, the result will be the same.
head(Design) # shows the design matrix## (Intercept) Grouppsoriasis
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 1
## 5 1 1
## 6 1 1colnames(Design) # shows how the coefficients are defined## [1] "(Intercept)" "Grouppsoriasis"Design # lists the columns with their contrast coefficients.## (Intercept) Grouppsoriasis
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 1
## 5 1 1
## 6 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$Group
## [1] "contr.treatment"46. Fit a linear model followed by empirical Bayes statistics to measure differences in expression
fit <- lmFit(imputed,Design)
efit <- eBayes(fit) 47. List the top differentially expressed genes
topTable(efit) # Default is 20 genes/proteins.## Removing intercept from test coefficients## logFC
## P31151 5.739511
## P48594;H0Y5H9;C9JZ65 10.473871
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 4.519826
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 -2.393218
## P12236 1.293670
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 4.307138
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 3.611664
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 2.157990
## Q59GN2;P62891 1.498487
## A0A087WW43;Q06033-2;Q06033;E7ET33 -1.474476
## AveExpr
## P31151 28.66029
## P48594;H0Y5H9;C9JZ65 24.78164
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 25.75188
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 26.30979
## P12236 26.17132
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 28.91527
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 25.93944
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 25.61798
## Q59GN2;P62891 27.50380
## A0A087WW43;Q06033-2;Q06033;E7ET33 24.98124
## t
## P31151 13.162589
## P48594;H0Y5H9;C9JZ65 13.096477
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 11.001931
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 -9.635134
## P12236 9.487154
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 9.413344
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 8.889131
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 8.593248
## Q59GN2;P62891 8.564618
## A0A087WW43;Q06033-2;Q06033;E7ET33 -8.381633
## P.Value
## P31151 7.223586e-06
## P48594;H0Y5H9;C9JZ65 7.452247e-06
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 2.176491e-05
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 4.868633e-05
## P12236 5.344239e-05
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 5.601252e-05
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 7.894926e-05
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 9.658239e-05
## Q59GN2;P62891 9.851602e-05
## A0A087WW43;Q06033-2;Q06033;E7ET33 1.119747e-04
## adj.P.Val
## P31151 0.006207722
## P48594;H0Y5H9;C9JZ65 0.006207722
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 0.012086778
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 0.015552811
## P12236 0.015552811
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 0.015552811
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 0.017637780
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 0.017637780
## Q59GN2;P62891 0.017637780
## A0A087WW43;Q06033-2;Q06033;E7ET33 0.017637780
## B
## P31151 4.404941
## P48594;H0Y5H9;C9JZ65 4.379322
## A0A7I2V2P6;A0A3B3ISG5;P14735;A0A7I2V3E3;A0A7I2YQV5;A0A7I2V610;A0A7I2V4Q3;A0A7I2V2S1;A0A7I2V3K9;B3KSB8;P14735-2;A0A7I2YQS6;A0A7I2V612;A0A7I2V373;A0A7I2V4A4;A0A7I2V634 3.457962
## P15502-2;E7ENM0;E7EN65;P15502-4;P15502-1;P15502-9;E7ETP7;P15502;P15502-10;P15502-5;P15502-13;P15502-6;P15502-7;P15502-12;P15502-8;G3V0G6;G5E950;B3KRT8;E7EWS8;P15502-11;E7ENW7;E7EP82;E7EQH8 2.719796
## P12236 2.632107
## Q01469;I6L8B7;A8MUU1;REV__Q5QNW6-2;REV__Q16778;REV__Q99879;REV__Q99877;REV__Q93079;REV__Q8N257;REV__Q5QNW6;REV__U3KQK0;REV__P58876;REV__P57053;REV__P33778;REV__P23527;REV__P06899;REV__O60814;REV__Q99880;REV__P62807 2.587752
## P43490;A0A7P0T9I9;A0A7P0T973;A0A7P0T931;A0A7P0T895;A0A7P0T8L3;A0A7P0TAS5;A0A7P0T862;A0A7P0T8D2;A0A0C4DFS8;A0A7P0TA73;A0A7P0TBB7;A0A7P0T941;A0A7P0Z466;B7Z8W6;A0A7P0Z4N0;A0A7P0T9F0;A0A7P0TB17;A0A7P0T8Z3;A0A7P0T8R4;A0A7P0TAI8;A0A7P0Z437;A0A7P0Z4L4;C9JG65;C9JF35;A0A7P0TAZ2 2.260448
## P42224;P42224-2;A0A669KB53;A0A669KBA4;A0A669KB56;A0A669KB68;A0A669KBI6;J3KPM9;A0A669KB52;A0A669KB75;E7EPD2;A0A669KB17;D2KFR9;E7ENM1;Q67C41;E9PH66;A0A669KBK4 2.065715
## Q59GN2;P62891 2.046471
## A0A087WW43;Q06033-2;Q06033;E7ET33 1.92176348. Look at the outcome of each hypothesis test in the fit object as +1 (up-regulated), 0 (no change), or -1 (down-regulated) using the decideTests function.
DE_proteins <- decideTests(efit, n=Inf) # method="separate" and adjust.method = "BH"' are default49. Summarize the number of up/down DE proteins.
summary(DE_proteins) # lists the number of up-/down-regulated proteins in psoriasis compared to healthy skin.## (Intercept) Grouppsoriasis
## Down 0 96
## NotSig 0 1491
## Up 1666 79- Number of proteins up-regulated in psoriasis skin.
- Number of proteins down-regulated in psoriasis skin.
50. Open DE_proteins.
View (DE_proteins)