CoCalc Shared FilesCDS-102 / Lab Week 07 - Tidying your dataset / CDS-102 Lab Week 07 Workbook.ipynb
Author: Helena Gray
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Description: Jupyter notebook CDS-102/Lab Week 07 - Tidying your dataset/CDS-102 Lab Week 07 Workbook.ipynb

# CDS-102: Lab 7 Workbook

## Helena Gray

### March 9, 2017

In [1]:
# Run this code block to load the Tidyverse package
.libPaths(new = "~/Rlibs")
install.packages("GGally", lib = "~/Rlibs")
library(tidyverse)
library(dplyr)
library("GGally")

In [2]:
# Import the dataset

Parsed with column specification: cols( .default = col_double(), GID = col_character(), YORF = col_character(), NAME = col_character(), GWEIGHT = col_integer() ) See spec(...) for full column specifications.
In [11]:
nrow(original_data)
ncol(original_data)


5537
40

The code below splits the values in the NAME column so that each value (separated by ||) is in a separate column and assigns the dataframe to a variable original_data_1 by way of the function separate(). The new columns are named as follows: “name”, “BP”, “MF”, “systematic_name”, and “number”.

In [3]:
original_data_1<-separate(original_data,NAME,c("name","BP","MF","systematic_name","number"),sep="\\|\\|")


The code below removes the whitespace from the five new columns that were created using the function mutate_each() and assigns the dataframe to a variable original_data_1.

In [4]:
original_data_1<-mutate_each(original_data_1, funs(trimws), name:systematic_name)


The code below removes the GID, YORF, and GWEIGHT columns from the dataset but leave all the others. The minus sign indicates to remove the columns specified.

In [5]:
original_data_1 <- subset(original_data_1, select = -c(GID, YORF,GWEIGHT))


The code below uses the gather() function to move variable names in columns G0.05 through U0.3 into a single column. The new column holding the variable names is called “sample” and the new column holding the values underneath the G0.05 through U0.3 is called “expression”.

In [6]:
original_data_1<-gather(original_data_1,"sample","expression",G0.05:U0.3)


The code below splits the "sample" column into two columns "nutrient" and "rate" by using the separate() function.

In [7]:
original_data_1<-separate(original_data_1,sample,c("nutrient","rate"),sep=1,convert=TRUE)


The code below applies a filter() to only look at the rows with a name of “LEU1” in the dataset.

In [8]:
leucine<-filter(original_data_1, name == "LEU1")


A line plot is created using ggplot2. The rate is shown on the horizontal axis, expression on the vertical, and the separate nutrients plotted as different colors.

In [12]:
enzyme.plot<-ggplot(data=leucine,aes(rate, expression, colour=nutrient)) + geom_line(lwd=1.5)
ggsave("enzymePlot.png", plot = enzyme.plot, device="png", scale=1, width=5, height=4)
enzyme.plot



Look closely at that! LEU1 is highly expressed when starved of leucine because the cell has to synthesize its own! And as the amount of leucine in the environment (the growth rate) increases, the cell can worry less about synthesizing leucine, so LEU1 expression goes back down. Consequently the cell can devote more energy into other functions, and we see other genes’ expression very slightly raising.

In [14]:
head(original_data_1)

SFB2 ER to Golgi transport molecular function unknown YNL049C 1082129 G 0.05 -0.24
biological process unknown molecular function unknown YNL095C 1086222 G 0.05 0.28
QRI7 proteolysis and peptidolysis metalloendopeptidase activityYDL104C 1085955 G 0.05 -0.02
CFT2 mRNA polyadenylylation* RNA binding YLR115W 1081958 G 0.05 -0.33
SSO2 vesicle fusion* t-SNARE activity YMR183C 1081214 G 0.05 0.05
PSP2 biological process unknown molecular function unknown YML017W 1083036 G 0.05 -0.69
In [15]:
head(original_data)

GIDYORFNAMEGWEIGHTG0.05G0.1G0.15G0.2G0.25G0.3L0.15L0.2L0.25L0.3U0.05U0.1U0.15U0.2U0.25U0.3
GENE1331X A_06_P5820 SFB2 || ER to Golgi transport || molecular function unknown || YNL049C || 1082129 1 -0.24 -0.13 -0.21 -0.15 -0.05 -0.05 0.13 0.20 0.17 0.11 -0.06 -0.26 -0.05 -0.28 -0.19 0.09
GENE4924X A_06_P5866 || biological process unknown || molecular function unknown || YNL095C || 1086222 1 0.28 0.13 -0.40 -0.48 -0.11 0.17 0.02 0.04 0.03 0.01 -1.02 -0.91 -0.59 -0.61 -0.17 0.18
GENE4690X A_06_P1834 QRI7 || proteolysis and peptidolysis || metalloendopeptidase activity || YDL104C || 10859551 -0.02 -0.27 -0.27 -0.02 0.24 0.25 -0.07 -0.05 -0.13 -0.04 -0.91 -0.94 -0.42 -0.36 -0.49 -0.47
GENE1177X A_06_P4928 CFT2 || mRNA polyadenylylation* || RNA binding || YLR115W || 1081958 1 -0.33 -0.41 -0.24 -0.03 -0.03 0.00 -0.05 0.02 0.00 0.08 -0.53 -0.51 -0.26 0.05 -0.14 -0.01
GENE511X A_06_P5620 SSO2 || vesicle fusion* || t-SNARE activity || YMR183C || 1081214 1 0.05 0.02 0.40 0.34 -0.13 -0.14 0.00 -0.11 0.04 0.01 -0.45 -0.09 -0.13 0.02 -0.09 -0.03
GENE2133X A_06_P5307 PSP2 || biological process unknown || molecular function unknown || YML017W || 1083036 1 -0.69 -0.03 0.23 0.20 0.00 -0.27 0.25 -0.21 0.12 -0.11 NA -0.65 0.09 0.06 -0.07 -0.10
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