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Tuesday, 15 August 2017

Drawing NFkappaB protein schematics with ggplot2..

I've been busy with work and preparing materials for our R for Biochemists 101 online training course for the Biochemical Society. The materials are being tested now and the course should be available in October 2017. Then I had a short holiday...

So, it's taken a little longer than I hoped but here is the code to draw protein schematics with ggplot2. The schematic shows protein schematics of the five human NF-kappaB proteins. Here is the visualisation:




The code uses my drawProteins package which is on Github. This can be installed using the function install_github() from the package devtools. The required syntax is devtools::install_github("brennanpincardiff/drawProteins")

I've gone through the code step by step. It's quite long and I am planning to create some more functions that will shorten the code.

Here is the step by step code that uses drawProteins and ggplot2 to create a protein schematic visualisation.  The ggplots along the way are shown too. To see the plots best requires a plot window of about 900 pixels.

# START
# working with rel proteins as I have been studying these for 10 year
# using ggplot2 to draw the plots.
# proteins are aligned to the N-termini
# alternative alignment options would be worth considering

library(httr)
# devtools::install_github("brennanpincardiff/drawProteins")
library(drawProteins)
library(ggplot2)

# Uniprot accession numbers of five human NF-kappaB family members
proteins_acc <- c("Q04206 Q01201 Q04864 P19838 Q00653")

# accession numbers need to be combined with "%2C" for Uniprot API
proteins_acc_url <- gsub(" ", "%2C", proteins_acc)

# this is the baseurl for a multiple features query
baseurl <- "https://www.ebi.ac.uk/proteins/api/features?offset=0&size=100&accession="

# make url to GET features for multiple accession numbers
url <- paste0(baseurl, proteins_acc_url)

# basic function is GET() which accesses the API
# accept_json() argument gives a JSON object
prots_feat <- GET(url,
                  accept_json())

status_code(prots_feat)  # returns a 200 - good

# extract content() - gives a list with length = number of acc no
prots_feat_red <- httr::content(prots_feat)


################
# loop to work through the API object and convert to data.frame
# probably there is a better way to do this
features_total_plot <- NULL
for(i in 1:length(prots_feat_red)){
  # the extract_feat_acc() function takes features into a data.frame
  features_temp <- drawProteins::extract_feat_acc(prots_feat_red[[i]])
  features_temp$order <- i  # this order is needed for plotting later
  features_total_plot <- rbind(features_total_plot, features_temp)
}

# look at the data.frame
View(features_total_plot)  # 319 observations


### use ggplot2::geom_rect to draw moleucles
### in this script each type of feature is drawn separately.

## step 1: set up the canvas in ggplot (no data added)
# want to put text on the left in the area of negative numbers
plot_start <- -max(features_total_plot$end)*0.2

# need it to be longer than the longest molecule plus a bit...
plot_end <- max(features_total_plot$end) + max(features_total_plot$end)*0.1

p <- ggplot() +
  ylim(0.5, max(features_total_plot$order)+0.5) +
  xlim(plot_start, plot_end)
p # show the object





## step 2: draw the CHAINS in grey (narrower)
p <- p + geom_rect(data= features_total_plot[features_total_plot$type == "CHAIN",],
                   mapping=aes(xmin=begin,
                               xmax=end,
                               ymin=order-0.2,
                               ymax=order+0.2),
                   colour = "black",
                   fill = "grey")
p



## step 3: annotate with names
p <- p +  annotate("text", x = -10, y = features_total_plot$order,
                   label = features_total_plot$entryName,
                   hjust = 1,
                   size = 4)
# check the object
p


## step 4: plot domains fill by description
p <- p + geom_rect(data= features_total_plot[features_total_plot$type == "DOMAIN",],
                   mapping=aes(xmin=begin,
                               xmax=end,
                               ymin=order-0.25,
                               ymax=order+0.25,
                               fill=description))
p


# label domains with descriptions
feat_domain <- features_total_plot[features_total_plot$type == "DOMAIN", ]
p <- p + geom_label(data = feat_domain,
                    aes(x = begin + (end-begin)/2,
                        y = order,
                        label = description))
p
# this works well with geom_label()


## step 5 plot motifs fill by description
p <- p + geom_rect(data= features_total_plot[features_total_plot$type == "MOTIF",],
                   mapping=aes(xmin=begin,
                               xmax=end,
                               ymin=order-0.25,
                               ymax=order+0.25,
                               fill=description))

p
# not going to label these as they're in the description and they are small


## step 6 plot repeats fill by description
p <- p + geom_rect(data= features_total_plot[features_total_plot$type == "REPEAT",],
                   mapping=aes(xmin=begin,
                               xmax=end,
                               ymin=order-0.25,
                               ymax=order+0.25),
                   fill = "lightblue")
p


# label repeats (for this they are ANK but remove digits)
p <- p + geom_text(data = features_total_plot[features_total_plot$type == "REPEAT",],
                    aes(x = begin + (end-begin)/2,
                        y = order,
                        label = gsub("\\d", "", description)),
                   size =2)
p


# add titles
p <- p + labs(x = "Amino acid number",         # label x-axis
              y = "",  # label y-axis
              title = "Schematic of five human NF-kappaB proteins",
              subtitle = "circles = phosphorylation sites\nRHD: Rel Homology Domain\nsource:Uniprot")
p



# white background and remove y-axis
p <- p + theme_bw() +  # white background
         theme(panel.grid.minor=element_blank(),
         panel.grid.major=element_blank()) +
         theme(axis.ticks = element_blank(),
               axis.text.y = element_blank()) +
         theme(panel.border = element_blank())
p


# add phosphorlation sites
# extract info about sites using phospho_site_info() function
phospho_sites <- drawProteins::phospho_site_info(features_total_plot)

# draw phosphorylation sites on the protein with geom_point()
p <- p + geom_point(data = phospho_sites,
               aes(x = begin,
                   y = order+0.25),
               shape = 21,
               colour = "black",
               fill = "yellow", size = 4, stroke = 1)

# to see it properly you need to zoom...

# I have to admit that I am pleased with this diagram.
# The colours are better, the labels seem to work.

p + ggsave("five_human_nfkappab_proteins_20170814.pdf")
# END


The output looks best as a PDF, I think so that's why I've saved it.
There are lots of options for altering text size and the like.
I am still working on visualising protein schematics in R so if you have feedback, it would be most welcome.



Resources

Wednesday, 28 June 2017

Using the Uniprot API to illustrate protein domains...

I'm a member of the Cardiff R User group and we're using R to explore APIs - application programming interfaces which allow R to access data. I chose the explore the API that allows me to download data from Uniprot, "a comprehensive, high-quality and freely accessible resource of protein sequence and functional information".

Tomorrow, we have our show and tell about what we have learned about using APIs with R. So in preparation and inspired by Stef Locke, the lead organiser for the Cardiff R User group, I have now created my first R package. The R package, called drawProteins, uses the API output to create a schematic protein domains.

Here are two examples of the output. The first is a schematic of a single protein using Base R plotting (script below). The second example is a schematic of three proteins generated using ggplot2 (script in a little while).







Here is the script to plot a schematic of the transcription factor p65 (top panel):

SCRIPT START
# OK so I have to write some kind of a script to use the new 
# Uniprot API
# This is R-UserGroup homework for Thursday....

# references: https://www.ebi.ac.uk/proteins/api/doc/#!/proteins/search
# references: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx237

# needs the package httr
# install.packages("httr")
library(httr)

# my package with functions for extracting and graphing
# might require: install.packages("devtools")
devtools::install_github("brennanpincardiff/drawProteins")
library(drawProteins)

# so I want to use the Protein API 
uniprot_acc <- c("Q04206")  # change this for your fav protein

# Get UniProt entry by accession 
acc_uniprot_url <- c("https://www.ebi.ac.uk/proteins/api/proteins?accession=")

comb_acc_api <- paste0(acc_uniprot_url, uniprot_acc)

# basic function is GET() which accesses the API
# requires internet access
protein <- GET(comb_acc_api,
           accept_json())

status_code(protein)  # returns a 200 means it worked

# use content() function from httr to give us a list 
protein_json <- content(protein) # gives a Large list
# with 14 primary parts and lots of bits inside

# function from my package to extract names of protein
names <- drawProteins::extract_names(protein_json)

# I like a visualistion so I want to draw the features of this molecule
# https://www.ebi.ac.uk/proteins/api/features?offset=0&size=100&accession=Q04206

# Get features 
feat_api_url <- c("https://www.ebi.ac.uk/proteins/api/features?offset=0&size=100&accession=")

comb_acc_api <- paste0(feat_api_url, uniprot_acc)

# basic function is GET() which accesses the API
prot_feat <- GET(comb_acc_api,
           accept_json())

status_code(prot_feat)  # returns a 200. 

# so to process this into a data.frame
prot_feat %>%
  content() %>%
  flatten() %>%
  drawProteins::extract_feat_acc() -> 
  features

# clean up for plotting
# focus on those that we want to plot...
features_plot <- features[features$length > 0, ]  
features_plot <- features_plot[complete.cases(features_plot),]
features_plot <- features_plot[features_plot$type!= "CONFLICT",]

# add colours 
library(randomcoloR)   # might need install
colours <- distinctColorPalette(nrow(features_plot)-1)
features_plot$col <- c("white", colours)

# now draw this...  with BaseR
# here is a function that does that....
drawProteins::draw_mol_horiz(names, features_plot)

# add phosphorylation sites
# get sites first. 
features %>%
  drawProteins::phospho_site_info() %>%
  drawProteins::draw_phosphosites(5, "yellow")  # 5 circle radius
text(250,0, "Yellow circles = phosphorylation sites", cex=0.8)

SCRIPT END

Resources

Thursday, 8 June 2017

Making a Namibia choropleth

According to Wikipedia, a choropleth map uses differences in shading, colouring, or the placing of symbols within predefined areas to indicate the average values of a particular quantity in those areas. I thought it would be interesting to make one of these in R for Namibia. I'm going to Namibia on Saturday as part of Project Phoenix, a partnership project between Cardiff University and University of Namibia.

I have used some data about urbanisation in Namibia which I downloaded from the Open Data for Africa website. This data shows some nice variation across the regions of Namibia.

Here is an example of the what can be done:


Here is the code and maps made along the way:

START
## map urbanisation data onto Namibia administrative regions. 

## step 1: download the map data

## download the file...
library(sp) 
link <- "http://biogeo.ucdavis.edu/data/gadm2.8/rds/NAM_adm1.rds"
download.file(url=link, destfile="file.rda", mode="wb")
gadmNab <- readRDS("file.rda")

## step 2: simplify the level of detail...
library(rmapshaper)
gadmNabS <- ms_simplify(gadmNab, keep = 0.01)
plot(gadmNabS)




## step 3: turn it into a ggplot object and plot
library(broom)
library(gpclib)
library(ggplot2)
namMapsimple <- tidy(gadmNabS, region="NAME_1")
ggplot(data = namMapsimple,
       aes(x = long, y = lat, group = group)) + 
       geom_polygon(aes(fill = "red"))



# check region names
unique(namMapsimple$id)
# the exclamation mark before Karas will cause a problem. 
namMapsimple$id <- gsub("!", "", namMapsimple$id)
# check it worked
unique(namMapsimple$id)
# now no exclamation mark


## step 4: let's get our data and bind it...
# download the data about urbanisation
library(RCurl)
x <- getURL("https://raw.githubusercontent.com/brennanpincardiff/learnR/master/data/Namibia_region_urbanRural.csv")
data <- read.csv(text = x)
# exclude whole country
data_regions <- data[!data$region=="Namibia",]
# so extact 2011 data from the data_regions
data_regions2011 <- data_regions[data_regions$Date == 2011, ]
# just extract urban
data_regions2011_urban <- data_regions2011[data_regions2011$residence == "Urban",]
# change colnames so that we can merge by id...
data_col_names <- colnames(data_regions2011_urban)
data_col_names[1] <- c("id")
colnames(data_regions2011_urban) <- data_col_names

# bind the urbanisation data into map with merge
mapNabVals <- merge(namMapsimple, 
                    data_regions2011_urban,
                    by.y = 'id')

p <- ggplot() +     # sometimes don't add any aes...
     geom_polygon(data = mapNabVals,
                  aes(x = long, y = lat,
                  group = group,
                  fill = Value))
p




# plot the outlines of the administrative regions
p <- p + geom_path(data=namMapsimple, 
              aes(x=long, y=lat, group=group))
p




p <- p + scale_fill_gradient("Urban\n pop\n  %",
                      low = "white", high = "red",
                      limits=c(0, 100))
p




p <- p +   coord_map()  # plot with correct mercator projection
p
  


# add titles 
p <- p + labs(x = "",         # label x-axis
                y = "",  # label y-axis
                title = "Urbanisation in Namibia 2011",
                subtitle = "source: http://namibia.opendataforafrica.org")
  
p + theme_bw()




# maybe get rid of long, lat etc..

p <- p + theme_bw() +
  theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank()) + 
  theme(panel.border = element_blank())
p # show the object




## add names of regions
library(rgeos)
# calculate centroids
trueCentroids = gCentroid(gadmNabS,byid=TRUE)
# make a data frame
name_df <- data.frame(gadmNab@data[,'NAME_1'])
centroids_df <- data.frame(trueCentroids)
name_df <- cbind(name_df, centroids_df)
colnames(name_df) <- c("region", "long", "lat")

# label the regions
p <- p + geom_text(data = name_df,
                     aes(label = region,
                         x = long,
                         y = lat), size = 2)
p




# change different colours
p + scale_fill_gradient("Urban\n population\n  %",
                        low = "white", high = "blue")




p + scale_fill_gradient("Urban\n pop\n  %",
                        low = "white", high = "green")




# If you want to save this... export or use the ggave() function
p + ggsave("Namibia_Urbanisation_Map_2011.pdf")

END


Resources:


      

Sunday, 30 April 2017

Three maps of Namibia...

Two colleagues, Dr Mark Kelson and Dr David Gillipse, and I are going to Namibia as part of Project Phoenix. We are planning three days of R training. The materials are being developed and shared on Github as an open source teaching and learning resource.

Before I travel, I'm trying to learn a little more about where I'm going. As a first step, I've been drawing some maps of Namibia. Here are some of the maps:

Colourful map made by library(maps) - Map 1 below.


Map of regions of Namibia made by library(sp) - map 2 below

Map made with library(ggplot2) - map 3 below
I like using ggplot2 but, of course, there is a lot of personal preference. My next step is to add some data to a map... 


## START SCRIPT

# exploring various ways to draw Namibia in R 
# three ways....

## MAP 1 with library(maps)
library(maps)       # Provides functions that let us plot the maps
library(mapdata)    # Contains the hi-resolution points that mark out the countries.

# https://cran.r-project.org/web/packages/maps/maps.pdf
# gives a nice outline
map('worldHires',
    c('Namibia'))
# add some context by adding more countries
map('worldHires',
    c('Namibia', 'South Africa', 'Botswana', 'Angola', 
      'Zambia', 'Zimbabwe', 'Mozambique'))

# show all of Africa
# map the world but limit by longitude and latitude
map('worldHires', 
    xlim=c(-20,60),  # longitude
    ylim=c(-35,40))  # latitude

# focus on countries in Southern Africa
map('worldHires', 
    xlim=c(-10,50), 
    ylim=c(-35,10)) 

# add colour and a title
map('worldHires', 
    xlim=c(-10,50), 
    ylim=c(-35,10),
    fill = TRUE,
    col = c("red", "palegreen", "lightblue", "orange", "pink"))
title(main = "Simple map of Southern Africa")
## MAP 2 with library(sp) and downloaded GADM data 
# this allows us to plot administrative regions inside Namibia
# https://www.students.ncl.ac.uk/keith.newman/r/maps-in-r-using-gadm
# http://biogeo.ucdavis.edu/data/gadm2.8/rds/NAM_adm1.rds

library(sp)

# download the file 
link <- "http://biogeo.ucdavis.edu/data/gadm2.8/rds/NAM_adm1.rds"
download.file(url=link, destfile="file.rda", mode="wb")
gadmNab <- readRDS("file.rda")  # special read file of format RDA

plot(gadmNab)    
# plot with colour filled in, title and label. 
plot(gadmNab, col = 'lightgrey', border = 'darkgrey')
title(main = "Map of admin regions inside Namibia")
points(17.0658, -22.5609,col=2,pch=18)
text(17, -22, "Windhoek")
## MAP 3 with library(ggplot2)
# the advantage is that we can add layers to our plot
# it gives us control
library(ggplot2)
# use map_data()function to create a data.frame containing the outline of Namibia
nab_map<-map_data("worldHires", region = "Namibia")

# create the object m with the map in it. 
# colour Namibia (grey)
m <- ggplot() +
  geom_polygon(data=nab_map,   # geom_polygon draw shape fill
               aes(x=long,     # longitude
                   y=lat,      # latitude
                   group=group),
               fill="gray88") 
m  # show the object

# modify the object to add a thick black line
m <- m + geom_path(data=nab_map,    # geom_path draw shape outline 
              aes(x=long, y=lat, group=group),
              colour="black",  # colour of line
              lwd = 1.5)       # thickness
m    # show it again


# add the points on the plot telling us where the longitude and latitude of Windhoek
# note the data is coming from a different data.frame to the map
location <- data.frame(c(17), c(-22), c("Windhoek"))
colnames(location) <- c("longitude", "latitude", "location")

# add a point with the location
m <- m +  geom_point(data=location, 
                     aes(x=longitude, y=latitude), 
                     colour="red", size = 5) 
m  # show the object

# add text at the same point
m <- m + geom_text(data = location, 
                   aes(x=longitude, 
                       y=latitude, 
                       label = location))  # label has the text

# change the theme to make the map nicer
m <- m + theme_bw()

# add a title
m + ggtitle("Map of Namibia in ggplot")
## END SCRIPT

# see here for more about colours in R
# http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf



Resources: