In yavorska/AchillesPackage: Achilles Application Functions

Introduction

This package was designed to analyse the Achilles v2.0 and v2.4 datasets from the Broad Institute, however, any other shRNA screens can also be analysed in a similar fashion through building an "RNAi" object. Namely :

library(YavAch)

# The data
data("Achilles2.4_data") # Achilles v2.4 dataset
data("Achilles2.4_annotation") # The p53 annotation for the Achilles v2.4 dataset

# Read in the shRNA data with a row for each hairpin and columns corresponding to cell lines.
head(Achilles2.4_Data[,1:5])

# Create a new RNAi object by filling in the corresponding slots
RNAiObject <- new(Class = "RNAi",
                  genes = Achilles2.4_Data$Gene,
                  sequences = Achilles2.4_Data$Name,
                  cancers = colnames(Achilles2.4_Data)[-(1:2)],
                  cancersP53 = Achilles2.4_annotation,
                  values = Achilles2.4_Data[,-(1:2)])

Most of the functions within the package require an "RNAi" object.

General Functions

Variation of shRNAs

# object        an RNAi object
# cancerID      a character string indicating the cell line of interest (partial string matching is sufficient)
# geneName      character string specifying the name of the gene of interest (case sensitive)   
# type          can be either "all" or "median"

variation.within.cell.line(object = RNAiObject, cancerID = "SLR24", geneName = "MDM4", type = "all")

# object        an RNAi object
# entityName    can either be "all" or the name of one of the entity in question (partial string matching is sufficient)
# geneName      character string specifying the name of the gene of interest (case sensitive) 

variation.within.entity(object = RNAiObject, entityName = "BONE", geneName = "MDM4")

# object        an RNAi object
# geneName      character string specifying the name of the gene of interest (case sensitive) 
# type          one of "value" or "rank"
# statistic     one of "mean" or "median"

variation.within.entities(object = RNAiObject, geneName = "MDM4", type = "value", statistic = "mean")

Consistency of phenotypes

# object        an RNAi object
# entityName    can either be "all" or the name of one of the entity in question (partial string matching is sufficient)
# geneName      character string specifying the name of the gene of interest (case sensitive) 
# type          one of "line" or "box""

consistency.of.phenotypes(object = RNAiObject, entityName = "BONE", geneName = "MDM4", type = "line")

p53 dependency

# object        an RNAi object
# entityName    can either be "all" or the name of one of the entity in question (partial string matching is sufficient)
# geneName      character string specifying the name of the gene of interest (case sensitive) 
# type          one of "line" or "box""

p53.dependency.by.entity(object = RNAiObject, entityName = "Lung", geneName = "MDM4", p53 = "all", type = "line")

Riger Functions

The RIGER functions require a separate data structure. In this case a simple data.frame is used (the files used for analysing both Achilles datasets are included in the package).

data("Achilles2.0") # RIGER data for the Achilles v2.0 dataset
data("Achilles2.4") # RIGER data for the Achilles v2.4 dataset

head(Achilles2.4[,1:5])

Evaluation of RIGER gene score

# object        must be an object of the above type
# geneName      character string specifying the name of the gene of interest (case sensitive)
# entityName    can either be "all" or the name of one of the entity in question (partial string matching is sufficient)

riger.gene.comparison(object = Achilles2.4, geneName = "MDM4", entityName = "All")

Evaluation of RIGER gene score across datasets

# object1        must be an object of the above type
# object2        must be an object of the above type
# object1Name    character vector giving the name of the first dataset used in the plot
# object2Name    character vector giving the name of the second dataset used in the plot
# geneName       character string specifying the name of the gene of interest (case sensitive)
# entityName     can either be "all" or the name of one of the entity in question (partial string matching is sufficient)
# percentage     top and bottom quantiles which should be coloured (number between 1 and 100)
# plotName       character string giving the name of the plot

riger.gene.comparison.datasets(object1 = Achilles2.0, object2 = Achilles2.4,
                               object1Name = "Ach1", object2Name = "Ach2",
                               geneName = "MDM4",
                               entityName = "Colon",
                               percentage = 1,
                               plotName = "Comparing RIGER scores")