Last updated: 2019-07-31

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/QTLlocation.Rmd
    Modified:   analysis/chromHHMQTL.Rmd
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    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
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    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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File Version Author Date Message
Rmd aa1ad85 brimittleman 2019-07-31 add pdf for figrues
html d13025b brimittleman 2019-07-31 Build site.
Rmd e2ff61a brimittleman 2019-07-31 add mayher plot to site
Rmd 64e6257 Mayher 2019-07-31 Added new file for PAS graphs

I first install all of the packages and librarys that will be necessary for the formation of these graphs. Lattice Extra in particular has the “doubleYScale()” function that helps me a lot when making these plots.

The current data frame has two columns - ID, and meanUsage. ID is a very long string containing a lot of currently irrelevant information. In this step, I am trying to solely get the type of the PAS from the ID column in the data frame along with its respective mean usage. To do this, I first read in the data frame. I then split the ID column into 5 columns, each for it’s respective type of information. Though I only need “type” it is easier to access the other information if needed in the future. Then, I create a new data frame containing “type” and “mean usage”.

#reading in the data frame
df <- read.delim("../data/PAS/NuclearPASMeanUsage.txt")

#splitting the ID column
sep <- 
  separate(data = df, col = ID, into = c("chr", "start","end", "thing", "peak"), sep = "\\:|\\_\\+\\_|\\_\\-\\_", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn")
sep <- 
  separate(data = sep, col = thing, into = c("thing", "type"), sep = "\\_", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn")
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
#deleting extraneous information (everything except type and mean usage)
keeps <- c("type","meanUsage")
total <- sep[keeps]

Now that I have the information I want, I have to seperate it based on type. Here, I create 5 data frames that contain only the mean usage for each type, as well as the one for total. I then convert each data frame to a data matrix, which changes the type from a list to a double, allowing it to be put in a plot.

#get the meanUsage per type in a data frame
utr3 <- subset(total, type == "utr3", select = c(meanUsage))
utr5 <- subset(total, type == "utr5", select = c(meanUsage))
end <- subset(total, type == "end", select = c(meanUsage))
cds <- subset(total, type == "cds", select = c(meanUsage))
intron <- subset(total, type == "intron", select = c(meanUsage))

#then, do the same for total
total <- total["meanUsage"]

#convert the data frame to a data matrix so it can be used in a plot
utr3_graph <- data.matrix(utr3)
utr5_graph <- data.matrix(utr5)
end_graph <- data.matrix(end)
cds_graph <- data.matrix(cds)
intron_graph <- data.matrix(intron)
total_graph <- data.matrix(total)

Here, I prepare for the next step by initializing 6 data frames, one for each type of PAS and one for the total data. Because I want meanUsage cutoff (number of values above a specific cutoff), instead of meanUsage itself, I need this extra step.

cutoff_utr3 <- data.frame("cutoff numbers" = c(0,0,0,0,0,0,0,0,0,0))
cutoff_utr5 <- data.frame("cutoff numbers" = c(0,0,0,0,0,0,0,0,0,0))
cutoff_end <- data.frame("cutoff numbers" = c(0,0,0,0,0,0,0,0,0,0))
cutoff_cds <- data.frame("cutoff numbers" = c(0,0,0,0,0,0,0,0,0,0))
cutoff_intron <- data.frame("cutoff numbers" = c(0,0,0,0,0,0,0,0,0,0))
cutoff_total <- data.frame("cutoff" = c(0,0,0,0,0,0,0,0,0,0))

In this step, I take each data matrix, and insert frequencies into the cutoff data frames. For example, if there is a meanUsage greater than 0.4, the cutoff for 0.4, 0.3, 0.2, and 0.1 will increase by one. I do this by using a for loop, and a lot of if statements per for loop to sift the mean usages in their correct “bins”. I do this for all 6 data sets.

for (i in utr3_graph){
  if(i >0.1){
    cutoff_utr3[1,]<- cutoff_utr3[1,] +1
  } 
  if(i>0.2) {
    cutoff_utr3[2,]<- cutoff_utr3[2,] +1
  } 
  if (i>0.3) {
    cutoff_utr3[3,]<- cutoff_utr3[3,] +1
  }
  if(i>0.4) {
    cutoff_utr3[4,]<- cutoff_utr3[4,] +1
  } 
  if(i>0.5) {
    cutoff_utr3[5,]<- cutoff_utr3[5,] +1
  }
  if(i>0.6) {
    cutoff_utr3[6,]<- cutoff_utr3[6,] +1
  } 
  if(i>0.7) {
    cutoff_utr3[7,]<- cutoff_utr3[7,] +1
  }
  if(i>0.8) {
    cutoff_utr3[8,]<- cutoff_utr3[8,] +1
  } 
  if(i>0.9) {
    cutoff_utr3[9,]<- cutoff_utr3[9,] +1
  } 
  if(i>=1.0) {
    cutoff_utr3[10,]<- cutoff_utr3[10,] +1
  }
}  

for (i in utr5_graph){
  if(i >0.1){
    cutoff_utr5[1,]<- cutoff_utr5[1,] +1
  } 
  if(i>0.2) {
    cutoff_utr5[2,]<- cutoff_utr5[2,] +1
  } 
  if (i>0.3) {
    cutoff_utr5[3,]<- cutoff_utr5[3,] +1
  }
  if(i>0.4) {
    cutoff_utr5[4,]<- cutoff_utr5[4,] +1
  } 
  if(i>0.5) {
    cutoff_utr5[5,]<- cutoff_utr5[5,] +1
  }
  if(i>0.6) {
    cutoff_utr5[6,]<- cutoff_utr5[6,] +1
  } 
  if(i>0.7) {
    cutoff_utr5[7,]<- cutoff_utr5[7,] +1
  }
  if(i>0.8) {
    cutoff_utr5[8,]<- cutoff_utr5[8,] +1
  } 
  if(i>0.9) {
    cutoff_utr5[9,]<- cutoff_utr5[9,] +1
  } 
  if(i>=1.0) {
    cutoff_utr5[10,]<- cutoff_utr5[10,] +1
  }
} 


for (i in end_graph){
  if(i >0.1){
    cutoff_end[1,]<- cutoff_end[1,] +1
  } 
  if(i>0.2) {
    cutoff_end[2,]<- cutoff_end[2,] +1
  } 
  if (i>0.3) {
    cutoff_end[3,]<- cutoff_end[3,] +1
  }
  if(i>0.4) {
    cutoff_end[4,]<- cutoff_end[4,] +1
  } 
  if(i>0.5) {
    cutoff_end[5,]<- cutoff_end[5,] +1
  }
  if(i>0.6) {
    cutoff_end[6,]<- cutoff_end[6,] +1
  } 
  if(i>0.7) {
    cutoff_end[7,]<- cutoff_end[7,] +1
  }
  if(i>0.8) {
    cutoff_end[8,]<- cutoff_end[8,] +1
  } 
  if(i>0.9) {
    cutoff_end[9,]<- cutoff_end[9,] +1
  } 
  if(i>=1.0) {
    cutoff_end[10,]<- cutoff_end[10,] +1
  }
}  

for (i in cds_graph){
  if(i >0.1){
    cutoff_cds[1,]<- cutoff_cds[1,] +1
  } 
  if(i>0.2) {
    cutoff_cds[2,]<- cutoff_cds[2,] +1
  } 
  if (i>0.3) {
    cutoff_cds[3,]<- cutoff_cds[3,] +1
  }
  if(i>0.4) {
    cutoff_cds[4,]<- cutoff_cds[4,] +1
  } 
  if(i>0.5) {
    cutoff_cds[5,]<- cutoff_cds[5,] +1
  }
  if(i>0.6) {
    cutoff_cds[6,]<- cutoff_cds[6,] +1
  } 
  if(i>0.7) {
    cutoff_cds[7,]<- cutoff_cds[7,] +1
  }
  if(i>0.8) {
    cutoff_cds[8,]<- cutoff_cds[8,] +1
  } 
  if(i>0.9) {
    cutoff_cds[9,]<- cutoff_cds[9,] +1
  } 
  if(i>=1.0) {
    cutoff_cds[10,]<- cutoff_cds[10,] +1
  }
}  

for (i in intron_graph){
  if(i >0.1){
    cutoff_intron[1,]<- cutoff_intron[1,] +1
  } 
  if(i>0.2) {
    cutoff_intron[2,]<- cutoff_intron[2,] +1
  } 
  if (i>0.3) {
    cutoff_intron[3,]<- cutoff_intron[3,] +1
  }
  if(i>0.4) {
    cutoff_intron[4,]<- cutoff_intron[4,] +1
  } 
  if(i>0.5) {
    cutoff_intron[5,]<- cutoff_intron[5,] +1
  }
  if(i>0.6) {
    cutoff_intron[6,]<- cutoff_intron[6,] +1
  } 
  if(i>0.7) {
    cutoff_intron[7,]<- cutoff_intron[7,] +1
  }
  if(i>0.8) {
    cutoff_intron[8,]<- cutoff_intron[8,] +1
  } 
  if(i>0.9) {
    cutoff_intron[9,]<- cutoff_intron[9,] +1
  } 
  if(i>=1.0) {
    cutoff_intron[10,]<- cutoff_intron[10,] +1
  }
} 

for (i in total_graph){
  if(i >0.1){
    cutoff_total[1,]<- cutoff_total[1,] +1
  } 
  if(i>0.2) {
    cutoff_total[2,]<- cutoff_total[2,] +1
  } 
  if (i>0.3) {
    cutoff_total[3,]<- cutoff_total[3,] +1
  }
  if(i>0.4) {
    cutoff_total[4,]<- cutoff_total[4,] +1
  } 
  if(i>0.5) {
    cutoff_total[5,]<- cutoff_total[5,] +1
  }
  if(i>0.6) {
    cutoff_total[6,]<- cutoff_total[6,] +1
  } 
  if(i>0.7) {
    cutoff_total[7,]<- cutoff_total[7,] +1
  }
  if(i>0.8) {
    cutoff_total[8,]<- cutoff_total[8,] +1
  } 
  if(i>0.9) {
    cutoff_total[9,]<- cutoff_total[9,] +1
  } 
  if(i>=1.0) {
    cutoff_total[10,]<- cutoff_total[10,] +1
  }
}

I now make data frames that contain the proportion of the specific type of PAS with respect to the total. I do this by dividing the frequency of the mean usage per cutoff of the type, by the same in the total.

utr3_prop <- cutoff_utr3/cutoff_total
utr5_prop <- cutoff_utr5/cutoff_total
end_prop <- cutoff_end/cutoff_total
cds_prop <- cutoff_cds/cutoff_total
intron_prop <- cutoff_intron/cutoff_total

Here, I create a data frame called “breaks_new” that has all of the mean usage cutoffs I used for the other data. I then combined this data frame with the proportion to create the data frames needed for all of the plots.

breaks_new <- data.frame(xval = c(0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00))

totalMeanUsageFreq <- data.frame(breaks_new, cutoff_total)
Utr3prop <- data.frame(breaks_new, utr3_prop)
Utr5prop <- data.frame(breaks_new, utr5_prop)
endprop <- data.frame(breaks_new, end_prop)
cdsprop <- data.frame(breaks_new, cds_prop)
intronprop <- data.frame(breaks_new, intron_prop)

Here, I initialixe the objects, which have all of the information in them, such as the type of plots, y axis label, and scale. The first object (obj1), is the one for the total frequency, which will be one side for the double Y axis plot.

obj1 <- xyplot(cutoff ~ xval, totalMeanUsageFreq,
                ylab = "Frequency of Total", xlab = "meanUsage cutoff", col.lab = "black")

obj2 <- xyplot(cutoff.numbers ~ xval, Utr3prop,
               panel = function(...)panel.xyplot(type = "l",lty = 2,grid=TRUE,...), ylab = "Proportion of UTR3", scales = list(y=list(limits=c(0,1))))

obj3 <- xyplot(cutoff.numbers ~ xval, Utr5prop,
               panel = function(...)panel.xyplot(type = "l",lty = 2,grid=TRUE,...), ylab = "Proportion of UTR5", scales = list(y=list(limits=c(0,1))))

obj4 <- xyplot(cutoff.numbers ~ xval, endprop,
               panel = function(...)panel.xyplot(type = "l",lty = 2,grid=TRUE,...), ylab = "Proportion of END", scales = list(y=list(limits=c(0,1))))

obj5 <- xyplot(cutoff.numbers ~ xval, cdsprop,
               panel = function(...)panel.xyplot(type = "l",lty = 2,grid=TRUE,...), ylab = "Proportion of CDS", scales = list(y=list(limits=c(0,1))))

obj6 <- xyplot(cutoff.numbers ~ xval, intronprop,
               panel = function(...)panel.xyplot(type = "l",lty = 2,grid=TRUE,...), ylab = "Proportion of INTRON", scales = list(y=list(limits=c(0,1))))

And lastly, I create the final, double Y axis plots

Version Author Date
d13025b brimittleman 2019-07-31

Version Author Date
d13025b brimittleman 2019-07-31

Version Author Date
d13025b brimittleman 2019-07-31

Version Author Date
d13025b brimittleman 2019-07-31

Version Author Date
d13025b brimittleman 2019-07-31

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

PDF for the figures:


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] latticeExtra_0.6-28 RColorBrewer_1.1-2  lattice_0.20-38    
[4] tidyr_0.8.3         dplyr_0.8.0.1      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       knitr_1.20       whisker_0.3-2    magrittr_1.5    
 [5] workflowr_1.4.0  tidyselect_0.2.5 R6_2.3.0         rlang_0.4.0     
 [9] stringr_1.3.1    highr_0.7        tools_3.5.1      grid_3.5.1      
[13] git2r_0.25.2     htmltools_0.3.6  yaml_2.2.0       rprojroot_1.3-2 
[17] digest_0.6.18    assertthat_0.2.0 tibble_2.1.1     crayon_1.3.4    
[21] purrr_0.3.2      fs_1.3.1         glue_1.3.0       evaluate_0.12   
[25] rmarkdown_1.10   stringi_1.2.4    compiler_3.5.1   pillar_1.3.1    
[29] backports_1.1.2  pkgconfig_2.0.2