Package 'ScreenR'

Title: Package to Perform High Throughput Biological Screening
Description: ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis.
Authors: Emanuel Michele Soda [aut, cre] (0000-0002-2301-6465), Elena Ceccacci [aut] (0000-0002-2285-8994), Saverio Minucci [fnd, ths] (0000-0001-5678-536X)
Maintainer: Emanuel Michele Soda <[email protected]>
License: MIT + file LICENSE
Version: 1.9.0
Built: 2024-11-30 04:34:28 UTC
Source: https://github.com/bioc/ScreenR

Help Index

Table for the annotation of Barcode

Description

Table for the annotation of Barcode

Usage

data(annotation_table)

Format

A data frame with 5320 rows and 2 columns obtained from a loss-of-function genetic screening. This table is used to store information about the shRNAs:

Gene

It Contains the gene name

Barcode

It contains an ID that identify each barcode (it is an unique identifier for an shRNA). I t can be use to merge the annotation table with t he count table

Gene_ID

It Contains a unique Gene ID

Sequence

It contains the cDNA sequence of the shRNA associated to the barcode

Library

It contains the library from which the shRNA come from. In this case is a pooled from https://cellecta.com/cellecta

Count number of barcode lost

Description

This function counts the number of barcodes lost during the sequencing. A barcode is lost if its associated shRNA has zero mapped read in a sample.

Usage

barcode_lost(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

Return a tibble containing the number of barcode lost for each sample

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

# In order to count the number of barcodes lost just the ScreenR object is
# needed
head(barcode_lost(object))

Compute data Table

Description

This function computes the data table that will be used for the analysis. The data_table is a tidy and normalized version of the original count_table and will be used throughout the analysis.

Usage

compute_data_table(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

ScreenR_Object with the data_table filed containing the table.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
object <- compute_data_table(object)
head(slot(object, "data_table"))

Compute Metrics

Description

This function computes the metrics that will be then used to compute the z-score using the function find_zscore_hit starting from the screenr object for a given treatment in a given day. More information about the z-score and other metrics used in genetic screening can be found at this paper z-score

Usage

compute_metrics(screenR_Object, control, treatment, day)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
control

A string specifying the sample that as to be used as control in the analysis. This string has to be equal to the interested sample in the Treatment column of the data_table slot
treatment

A string specifying the sample that as to be used as treatment in the analysis. This string has to be equal to the interested sample in the Treatment column of the data_table slot.
day

A string containing the day (time point) to consider in the metrics computation. This string has to be equal to the interested sample in the Day column of the data_table slot.

Value

Return a tibble with all the measure computed.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
metrics <- compute_metrics(object,
    control = "TRT",
    treatment = "Time3", day = "Time3"
)
head(metrics)

Compute Slope of a Gene

Description

This function is used to compute the slope of the gene passed as input

Usage

compute_slope(screenR_Object, genes, group_var)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
genes

The genes for which the slope as to be computed. Those genes are the result of the three statistical methods selection
group_var

The variable to use as independent variable (x) for the linear model

Value

A tibble containing in each row the gene and the corresponding Slope

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

compute_slope(object,
    genes = c("Gene_42", "Gene_24"),
    group_var = c("T1", "T2", "TRT")
)

Count the number of mapped read

Description

This function counts the number of reads for each barcode in each sample. It is a quality control function (QC) to see if the biological protocol went as planned. If a sample has very low mapped compared to the other means that is has a lower quality.

Usage

count_mapped_reads(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

Return a tibble containing the number of mapped read for sample

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
head(count_mapped_reads(object))

Table of the count table

Description

Table of the count table

Usage

data(count_table)

Format

A data frame with 5323 rows and 15 variables obtained from barcode alignment to the reference library. It is generated from a [Cellecta](https://cellecta.com/) protocol. The samples generated are then sequenced using an RNA-seq protocol. Due to the fact that different shRNAs are sequenced for a gene each barcode has its associated reads. This reads were aligned to the reference library using [bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) and then sorted with [samtools](https://github.com/samtools/samtools). Since this dataset comes from a Chemical Synthetic Lethality experiments the samples treated and combined with the shRNAs knockdown should present a decreased number of reads compared to the controls.

Barcode

It contains an ID that identify each barcode. It can be use to marge the annotation table with the count table. A Barcode is a unique identifier of an shRNA. In a genetic screening multiple slightly different shRNAs perform a knockout of a gene each with its efficacy. For this reason it is important to keep track of each shRNA using a unique barcode.

Time_1

It contains the counts at time zero. This is the first time point at which cells are not treated and not infected.

Time_2

It contains the counts after the cell were washed. At this time point the cells are infected and following the Cellecta protocol are washed with the puromycin.

Time_3_TRT_rep1

It contains the counts for the first replicate of the treated at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_3_TRT_rep2

It contains the counts for the second replicate of the treated at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_3_TRT_rep3

It contains the counts for the third replicate of the treated at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_3_rep1

It contains the counts for the first replicate of the control at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_3_rep2

It contains the counts for the second replicate of the control at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_3_rep3

It contains the counts for the third replicate of the control at the first time point. Usually the first time point is 7 day after the puromycin wash.

Time_4_TRT_rep1

It contains the counts for the first replicate of the treated at the second time point. Usually the first time point is 14 day after the puromycin wash.

Time_4_TRT_rep2

It contains the counts for the second replicate of the treated at the second time point. Usually the first time point is 14 day after the puromycin wash.

Time_4_TRT_rep3

It contains the counts for the third replicate of the treated at the second time point. Usually the first time point is 14 day after the puromycin wash.

Time_4_rep1

It contains the counts for the first replicate of the control at the second time point. Usually the first time point is 14 day after the puromycin wash.

Time_4_rep2

It contains the counts for the second replicate of the control at the second time point. Usually the first time point is 14 day after the puromycin wash.

Time_4_rep3

It contains the counts for the third replicate of the control at the second time point. Usually the first time point is 14 day after the puromycin wash.

Create edgeR Object

Description

Utility function that using the screenr-class object create the corresponding edgeR object. This function and other utility function enables the user to not worry abut the implementation and just focus on the analysis. The ScreenR package will take care of the rest.

Usage

create_edger_obj(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

The edgeR object will all the needed information for the analysis.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
create_edger_obj(object)

Create the ScreenR Object

Description

Initial function to create the Screen Object.

Usage

create_screenr_object(
  table = NULL,
  annotation = NULL,
  groups = NULL,
  replicates = c("")
)

Arguments

table

The count table obtained from the read alignment that contains the Barcodes as rows and samples as columns.
annotation

The annotation table containing the information for each Barcode and the association to the corresponding Gene
groups

A factor containing the experimental design label
replicates

A vector containing the replicates label

Value

An object containing all the needed information for the analysis.

Examples

count_table <-
    data.frame(
        Barcode = c("Code_1", "Code_2", "Code_3", "Code_3"),
        Time_3_rep1 = c("3520", "3020", "1507", "1400"),
        Time_3_rep2 = c("3500", "3000", "1457", "1490"),
        Time_3_TRT_rep1 = c("1200", "1100", "1300", "1350"),
        Time_3_TRT_rep2 = c("1250", "1000", "1400", "1375")
    )
annotation_table <-
    data.frame(
        Gene = c("Gene_1", "Gene_1", "Code_2", "Code_2"),
        Barcode = c("Code_1", "Code_2", "Code_3", "Code_3"),
        Gene_ID = rep(NA, 4), Sequence = rep(NA, 4),
        Library = rep(NA, 4)
    )

groups <- factor(c("Control", "Control", "Treated", "Treated"))
obj <- create_screenr_object(
    table = count_table,
    annotation = annotation_table,
    groups = groups, replicates = c("")
)
obj

Filter using the slope filter

Description

This function is used to improve the quality of the hits found. It computes a regression line in the different samples ad uses the slope of this line to see the trend

Usage

filter_by_slope(
  screenR_Object,
  genes,
  group_var_treatment,
  group_var_control,
  slope_control,
  slope_treatment
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
genes

The genes for which the slope as to be computed. Those genes are the result of the three statistical methods selection
group_var_treatment

The variable to use as independent variable (x) for the linear model of the treatment
group_var_control

The variable to use as independent variable (x) for the linear model of the the control
slope_control

A value used as threshold for the control slope
slope_treatment

A value used as threshold for the treatment slope

Value

A data frame with the slope for the treatment and the control for each gene

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

filter_by_slope(
    screenR_Object = object, genes = c("Gene_1", "Gene_2"),
    group_var_treatment = c("T1", "T2", "TRT"),
    group_var_control = c("T1", "T2", "Time3", "Time4"),
    slope_control = 0.5, slope_treatment = 1
)

Filter using the variance filter

Description

This function is used to improve the quality of the hits. It compute the variance among the hits and filter the one with a value greater than the threshold set

Usage

filter_by_variance(
  screenR_Object,
  genes,
  matrix_model,
  variance = 0.5,
  contrast
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
genes

The genes for which the variance as to be computed. Those genes are the result of the three statistical methods selection
matrix_model

a matrix created using model.matrix
variance

The maximum value of variance accepted
contrast

The variable to use as X for the linear model for the Treatment

Value

A data frame with the variance for the treatment and the control for each gene

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
matrix_model <- model.matrix(~ slot(object, "groups"))
colnames(matrix_model) <- c("Control", "T1_T2", "Treated")
contrast <- limma::makeContrasts(Treated - Control, levels = matrix_model)

data <- filter_by_variance(
    screenR_Object = object, genes = c("Gene_42"),
    matrix_model = matrix_model, contrast = contrast
)
head(data)

Find Camera Hit

Description

This function implements the method by proposed by Wu and Smyth (2012). The original camera method is a gene set test, here is applied in the contest of a genetic screening and so it erforms a competitive barcode set test. The paper can be found here CAMERA

Usage

find_camera_hit(
  screenR_Object,
  matrix_model,
  contrast,
  number_barcode = 3,
  thresh = 1e-04,
  lfc = 1,
  direction = "Down"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
matrix_model

The matrix that will be used to perform the linear model analysis created using model.matrix
contrast

A vector or a single value indicating the index or the name of the column the model_matrix with which perform the analysis
number_barcode

Number of barcode that as to be differentially expressed (DE)in order to consider the gene associated DE. Example a gene is associated with 10 shRNA we consider a gene DE if it has at least number_barcode = 5 shRNA DE.
thresh

The threshold for the False Discovery Rate (FDR) that has to be used to select the statistically significant hits.
lfc

The Log2FC threshold.
direction

String containing the direction of the variation, "Down" for the down regulation "Up" for the up regulation.

Value

The data frame containing the hit found using the camera method

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

matrix <- model.matrix(~ slot(object, "groups"))
colnames(matrix) <- c("Control", "T1/T2", "Treated")

result <- find_camera_hit(
    screenR_Object = object,
    matrix_model = matrix, contrast = "Treated"
)
head(result)

Find common hit

Description

This method find the hit in common between the three methods

Usage

find_common_hit(hit_zscore, hit_camera, hit_roast, common_in = 3)

Arguments

hit_zscore

The matrix obtained by the find_zscore_hit method
hit_camera

The matrix obtained by the find_camera_hit method
hit_roast

The matrix obtained by the find_roast_hit method
common_in

Number of methods in which the hit has to be in common in order to be considered a candidate hit. The default value is 3, which means that has to be present in the result of all the three methods.

Value

A vector containing the common hit

Examples

hit_zscore <- data.frame(Gene = c("A", "B", "C", "D", "E"))
hit_camera <- data.frame(Gene = c("A", "B", "C", "F", "H", "G"))
hit_roast <- data.frame(Gene = c("A", "L", "N"))

# common among all the three methods
find_common_hit(hit_zscore, hit_camera, hit_roast)

# common among at least two of the three methods
find_common_hit(hit_zscore, hit_camera, hit_roast, common_in = 2)

Find Roast Hit

Description

Find the hit using the roast method. Roast is a competitive gene set test which uses rotation instead of permutation. Here is applied in a contest of a genetic screening so it perform a barcode competitive test testing for barcode which are differentially expressed within a gene. More information can be found in Roast

Usage

find_roast_hit(
  screenR_Object,
  matrix_model,
  contrast,
  nrot = 9999,
  number_barcode = 3,
  direction = "Down",
  p_val = 0.05
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
matrix_model

The matrix that will be used to perform the linear model analysis. Created using model.matrix
contrast

A vector or a single value indicating the index or the name of the column the model_matrix to which perform the analysis
nrot

Number of rotation to perform the test. Higher number of rotation leads to more statistically significant result.
number_barcode

Number of barcode that as to be differentially expressed (DE)in order to consider the gene associated DE. Example a gene is associated with 10 shRNA we consider a gene DE if it has at least number_barcode = 5 shRNA DE.
direction

Direction of variation
p_val

The value that as to be used as p-value cut off

Value

The hits found by ROAST method

Examples

set.seed(42)
object <- get0("object", envir = asNamespace("ScreenR"))
matrix_model <- model.matrix(~ slot(object, "groups"))
colnames(matrix_model) <- c("Control", "T1_T2", "Treated")

result <- find_roast_hit(object,
    matrix_model = matrix_model,
    contrast = "Treated", nrot = 100
)
head(result)

Title Find robust Z-score Hit

Description

Title Find robust Z-score Hit

Usage

find_robust_zscore_hit(table_treate_vs_control, number_barcode)

Arguments

table_treate_vs_control

A table computed with the function compute_data_table. It contain for each barcode the associated Gene the counts in the treated and control and the value for the Log2FC, Zscore, ZscoreRobust in each day.
number_barcode

Number of barcode that as to be differentially expressed (DE)in order to consider the gene associated DE. Example a gene is associated with 10 shRNA we consider a gene DE if it has at least number_barcode = 5 shRNA DE.

Value

Return a tibble containing the hit for the robust Z-score

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
table <- compute_metrics(object,
    control = "TRT", treatment = "Time3",
    day = "Time3"
)
result <- find_robust_zscore_hit(table, number_barcode = 6)
head(result)

Title Find Z-score Hit

Description

Title Find Z-score Hit

Usage

find_zscore_hit(table_treate_vs_control, number_barcode = 6, metric = "median")

Arguments

table_treate_vs_control

table computed with the function compute_data_table
number_barcode

Number of barcode that as to be differentially expressed (DE)in order to consider the gene associated DE. Example a gene is associated with 10 shRNA we consider a gene DE if it has at least number_barcode = 5 shRNA DE.
metric

A string containing the metric to use. The value allowed are "median" or "mean".

Value

Return a tibble containing the hit for the Z-score

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
table <- compute_metrics(object,
    control = "TRT", treatment = "Time3",
    day = "Time3"
)

# For the the median
result <- find_zscore_hit(table, number_barcode = 6)
head(result)

# For the mean
result <- find_zscore_hit(table, number_barcode = 6, metric = "mean")
head(result)

Get ScreenR annotation table

Description

Get function for the annotation table of the ScreenR object

Usage

get_annotation_table(object)

## S4 method for signature 'screenr_object'
get_annotation_table(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The annotation table of the ScreenR object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
annotation_table <- get_annotation_table(object)
head(annotation_table)

Get ScreenR count table

Description

Get function for the count table of the ScreenR object

Usage

get_count_table(object)

## S4 method for signature 'screenr_object'
get_count_table(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The count table of the ScreenR object

Slots

count_table

It is used to store the count table to perform the analysis

annotation_table

It is used to store the annotation of the shRNA

groups

It is used to store the vector of treated and untreated

replicates

It is used to store information about the replicates

normalized_count_table

It is used to store a normalized version of the count table

data_table

It is used to store a tidy format of the count table

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
count_table <- get_count_table(object)
head(count_table)
data("count_table", package = "ScreenR")
data("annotation_table", package = "ScreenR")

groups <- factor(c(
    "T1/T2", "T1/T2", "Treated", "Treated", "Treated",
    "Control", "Control", "Control", "Treated", "Treated",
    "Treated", "Control", "Control", "Control"
))

obj <- create_screenr_object(
    table = count_table,
    annotation = annotation_table,
    groups = groups,
    replicates = c("")
)

Get ScreenR data_table

Description

Get function for the data_table of the ScreenR object

Usage

get_data_table(object)

## S4 method for signature 'screenr_object'
get_data_table(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The data_table of the ScreenR object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
data_table <- get_data_table(object)
data_table

Get ScreenR groups

Description

Get function for the groups of the ScreenR object

Usage

get_groups(object)

## S4 method for signature 'screenr_object'
get_groups(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The groups of the ScreenR object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
groups <- get_groups(object)
groups

Get ScreenR normalized_count_table

Description

Get function for the normalized_count_table of the ScreenR object

Usage

get_normalized_count_table(object)

## S4 method for signature 'screenr_object'
get_normalized_count_table(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The normalized_count_table of the ScreenR object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
normalized_count_table <- get_normalized_count_table(object)
normalized_count_table

Get ScreenR replicates

Description

Get function for the replicates of the ScreenR object

Usage

get_replicates(object)

## S4 method for signature 'screenr_object'
get_replicates(object)

Arguments

object

The ScreenR object obtained using the create_screenr_object

Value

The replicates of the ScreenR object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
replicates <- get_replicates(object)
replicates

Mapped Reads

Description

This function returns the number of mapped reads inside the ScreenR object

Usage

mapped_reads(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

Return a tibble containing the number of mapped read for sample

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
mapped_reads(object)

Normalize data

Description

This function perform a normalization on the data considering the fact that each shRNA has a defined length so this will not influence the data. Basically is computed the sum for each row and then multiply by 1e6. At the end the data obtained will be Count Per Million.

Usage

normalize_data(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

Return the ScreenR object with the normalize data

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
object <- normalize_data(object)

slot(object, "normalized_count_table")

Plot barcode hit

Description

Create a barcode plot for a hit. A barcode plot displays if the hit is differentially up or down regulated. If most of the vertical line are on the left side the gene associated to the barcodes is down regulated otherwise is up regulated.

Usage

plot_barcode_hit(
  screenR_Object,
  matrix_model,
  contrast,
  number_barcode = 3,
  gene,
  quantile = c(-0.5, 0.5),
  labels = c("Negative logFC", "Positive logFC")
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
matrix_model

The matrix that will be used to perform the linear model analysis. It is created using model.matrix.
contrast

An object created with makeContrasts function.
number_barcode

Number of barcode that as to be differentially expressed (DE) in order to consider the associated gene DE. Example a gene is associated with 10 shRNA we consider a gene DE if it has at least number_barcode = 5 shRNA DE.
gene

The name of the gene that has to be plot
quantile

Quantile to display on the plot
labels

The label to be displayed on the quantile side

Value

The barcode plot

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
matrix_model <- model.matrix(~ slot(object, "groups"))
colnames(matrix_model) <- c("Control", "T1_T2", "Treated")
contrast <- limma::makeContrasts(Treated - Control, levels = matrix_model)

plot_barcode_hit(object, matrix_model,
    contrast = contrast,
    gene = "Gene_300"
)

Plot number of barcode lost

Description

This function plots the number of barcode lost in each sample. Usually lots of barcodes lost mean that the sample has low quality.

Usage

plot_barcode_lost(
  screenR_Object,
  palette = NULL,
  alpha = 1,
  legende_position = "none"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
palette

A vector of colors to be used to fill the barplot.
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
legende_position

Where to positioning the legend of the plot. Allowed values are in the "top", "bottom", "right", "left", "none".

Value

Returns the plot displaying the number of barcode lost in each sample

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_barcode_lost(object)

Plot number of barcode lost for gene

Description

This function plots the number of barcodes lost in each sample for each gene. Usually in a genetic screening each gene is is associated with multiple shRNAs and so barcodes. For this reason a reasonable number of barcodes associated with the gene has to be retrieved in order to have a robust result. Visualizing the number of genes that have lost lot's of barcode is a Quality Check procedure in order to be aware of the number of barcode for the hit identified.

Usage

plot_barcode_lost_for_gene(screenR_Object, facet = TRUE, samples)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
facet

A boolean to use the facet.
samples

A vector of samples that as to be visualize

Value

Return the plot displaying the number of barcode lost for each gene in each sample.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_barcode_lost_for_gene(object,
    samples = c("Time3_A", "Time3_B")
)
plot_barcode_lost_for_gene(object,
    samples = c("Time3_A", "Time3_B"),
    facet = FALSE
)

Plot the trend over time of the barcodes

Description

Plot the log2FC over time of the barcodes in the different time point. This plot is useful to check we efficacy of each shRNA. Good shRNAs should have consistent trend trend over time.

Usage

plot_barcode_trend(
  list_data_measure,
  genes,
  n_col = 1,
  size_line = 1,
  color = NULL
)

Arguments

list_data_measure

A list containing the measure table of the different time point. Generated using the compute_metrics function.
genes

The vector of genes name.
n_col

The number of column to use in the facet wrap.
size_line

The thickness of the line.
color

The vector of colors. One color for each barcode.

Value

The trend plot for the genes in input.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

metrics <- dplyr::bind_rows(
    compute_metrics(object,
        control = "TRT", treatment = "Time3",
        day = "Time3"
    ),
    compute_metrics(object,
        control = "TRT", treatment = "Time4",
        day = "Time4"
    )
)
# Multiple Genes
plot_barcode_trend(metrics,
    genes = c("Gene_1", "Gene_50"),
    n_col = 2
)
# Single Gene
plot_barcode_trend(metrics, genes = "Gene_300")

Plot Barcodes Hit

Description

This function plots a boxplot for each sample for the genes passed as input. It can be used to see the overall trend of a gene and so to visualize if the gene is up or down regulated.

Usage

plot_boxplot(
  screenR_Object,
  genes,
  group_var,
  alpha = 0.5,
  nrow = 1,
  ncol = 1,
  fill_var = "Sample",
  type = "boxplot",
  scales = "free"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
genes

The vector of genes that will be displayed
group_var

The variable that as to be used to filter the data, for example the different treatment
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
nrow

The number of rows in case multiple genes are plotted
ncol

The number of columns in case multiple genes are plotted
fill_var

The variable used to fill the boxplot
type

The type of plot to use "boxplot" or "violinplot"
scales

The scales used for the facet. Possible values can be "free", "fixed" and "free_y"

Value

A boxplot

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_boxplot(object,
    genes = c("Gene_34"),
    group_var = c("T1", "T2", "TRT"), nrow = 1, ncol = 2,
    fill_var = "Day", type = "violinplot"
)

Plot common hit

Description

This method plot the hits in common among the three methods is a wrapper for the ggvenn function.

Usage

plot_common_hit(
  hit_zscore,
  hit_camera,
  hit_roast,
  alpha = 0.5,
  stroke_size = 0.5,
  set_name_size = 4,
  text_color = "black",
  text_size = 4,
  show_percentage = TRUE,
  title = "",
  color = c("#1B9E77", "#D95F02", "#7570B3"),
  show_elements = TRUE
)

Arguments

hit_zscore

The list of hits of the find_zscore_hit
hit_camera

The list of hits of the find_camera_hit
hit_roast

The list of hits of the find_roast_hit
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
stroke_size

Stroke size for drawing circles
set_name_size

Text size for set names
text_color

Text color for intersect contents
text_size

Text size for intersect contents
show_percentage

Show percentage for each set
title

The title to display above the plot
color

The three vector color for the venn
show_elements

Show set elements instead of count/percentage.

Value

A vector containing the common hit

Examples

hit_zscore <- data.frame(Gene = c("A", "B", "C", "D", "E"))
hit_camera <- data.frame(Gene = c("A", "B", "C", "F", "H", "G"))
hit_roast <- data.frame(Gene = c("A", "L", "N"))
plot_common_hit(hit_zscore, hit_camera, hit_roast)

Plot the explained variance by the PC

Description

This function plot the explained variance by the Principal Component analysis.

Usage

plot_explained_variance(
  screenR_Object,
  cumulative = FALSE,
  color = "steelblue"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
cumulative

A boolean value which indicates whether or not to plot the cumulative variance. The default value is FALSE.
color

The color to fill the barplot the default value is steelblue

Value

The explained variance plot

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_explained_variance(object)

# For the cumulative plot
plot_explained_variance(object, cumulative = TRUE)

Plot mapped reads

Description

This function plots the number of reads mapped for each sample. It internally call the count_mapped_reads function, to compute the number of mapped reads.

Usage

plot_mapped_reads(
  screenR_Object,
  palette = NULL,
  alpha = 1,
  legende_position = "none"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
palette

A vector of color that as to be used to fill the barplot.
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
legende_position

Where to positioning the legend of the plot ("none", "left", "right", "bottom", "top")

Value

return a ggplot object

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_mapped_reads(object)

Plot the distribution of the mapped reads

Description

This function creates a boxplot or a densityplot to show the distribution of the mapped reads in different samples. This function can be used to assess the quality of the samples. Samples which show roughly the same distribution have good quality.

Usage

plot_mapped_reads_distribution(
  screenR_Object,
  palette = NULL,
  alpha = 1,
  type = "boxplot"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object.
palette

The color vector that as to be used for the plot.
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
type

The type of plot. The default is "boxplot" the other option is "density."

Value

Return a tibble containing the number of mapped read for each sample

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

# Boxplot
plot_mapped_reads_distribution(object)

# Density
plot_mapped_reads_distribution(object, type = "density")

plot_mapped_reads_distribution(object, type = "density", alpha = 0.2)

Multidimensional Scaling Plot

Description

Plot samples on a two-dimensional scatterplot so that distances on the plot approximate the typical log2 fold changes between the samples.

Usage

plot_mds(
  screenR_Object,
  groups = NULL,
  alpha = 0.8,
  size = 2.5,
  color = "black"
)

Arguments

screenR_Object

The Object of the package create_screenr_object
groups

The vector that has to be used to fill the plot if NULL the function will use the default groups slot in the object passed as input.
alpha

The opacity of the labels. Possible value are in a range from 0 to 1.
size

The dimension of the labels. The default value is 2.5
color

The color of the labels. The default value is black

Value

The MDS Plot

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_mds(object)

Plot the trend hit gene

Description

This function plot the trend of a gene resulted as hit

Usage

plot_trend(
  screenR_Object,
  genes,
  group_var,
  alpha = 0.5,
  se = FALSE,
  point_size = 1,
  line_size = 1,
  nrow = 1,
  ncol = 1,
  scales = "free"
)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object
genes

The vector of genes to use
group_var

The variable that as to be used to filter the data, for example the different treatment
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1
se

A boolean to indicate where or not to plot the standard error
point_size

The dimension of each dot
line_size

The dimension of the line
nrow

The number of rows in case multiple genes are plotted
ncol

The number of columns in case multiple genes are plotted
scales

The scales to be used in the facette

Value

The plot of the trend over time for a specific treatment.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_trend(object, genes = "Gene_42", group_var = c("T1", "T2", "TRT"))

plot_trend(object,
    genes = c("Gene_42", "Gene_100"),
    group_var = c("T1", "T2", "TRT"),
    nrow = 2
)

Plot distribution Z-score

Description

This function plots the Log2FC Z-score distribution of the treated vs control in the different time points.

Usage

plot_zscore_distribution(time_point_measure, alpha = 1)

Arguments

time_point_measure

A list containing the table for each time point. Each table contains for each barcode the counts for the treated and control the Log2FC, Zscore, ZscoreRobust, Day.
alpha

A value for the opacity of the plot. Allowed values are in the range 0 to 1

Value

return the density plot of the distribution of the Z-score

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

table1 <- compute_metrics(object,
    control = "TRT", treatment = "Time3",
    day = "Time3"
)

table2 <- compute_metrics(object,
    control = "TRT", treatment = "Time4",
    day = "Time4"
)

plot_zscore_distribution(list(table1, table2), alpha = 0.5)

Remove rows that have zero count in all samples

Description

This function removes the rows that have zero count in all samples. It takes care of updating both count_table and annotation_table. Very_Important: It has to be performed before the data normalization.

Usage

remove_all_zero_row(screenR_Object)

Arguments

screenR_Object

The ScreenR object obtained using the create_screenr_object

Value

The ScreenR object with the count_table and the annotation_table filtered.

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
counts <- get_count_table(object)
nrow(counts)
object <- remove_all_zero_row(object)
counts <- get_count_table(object)
nrow(counts)

Set ScreenR annotation table

Description

Set function for the annotation table of the ScreenR object

Usage

set_annotation_table(object, annotation_table)

## S4 method for signature 'screenr_object'
set_annotation_table(object, annotation_table)

Arguments

object

The ScreenR object obtained using the create_screenr_object
annotation_table

a table containing the annotation for each shRNA

Value

The ScreenR object with the annotation table

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
annotation <- get_annotation_table(object)
set_annotation_table(object, annotation)

Set ScreenR count table

Description

Set function for the count table of the ScreenR object

Usage

set_count_table(object, count_table)

## S4 method for signature 'screenr_object'
set_count_table(object, count_table)

Arguments

object

The ScreenR object obtained using the create_screenr_object
count_table

A count table containing in each row an shRNA and in each column a sample

Value

The ScreenR object with the count table

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
counts <- get_count_table(object)
set_count_table(object, counts)

Set ScreenR data_table

Description

Set function for the data_table of the ScreenR object

Usage

set_data_table(object, data_table)

## S4 method for signature 'screenr_object'
set_data_table(object, data_table)

Arguments

object

The ScreenR object obtained using the create_screenr_object
data_table

A count table in a tidy format

Value

The ScreenR object with the set data_table

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
data_table <- get_data_table(object)
set_data_table(object, data_table)

Set ScreenR groups

Description

Set function for the groups of the ScreenR object

Usage

set_groups(object, groups)

## S4 method for signature 'screenr_object'
set_groups(object, groups)

Arguments

object

The ScreenR object obtained using the create_screenr_object
groups

The treatment and control groups

Value

The ScreenR object containing the group field

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
groups <- get_groups(object)
set_groups(object, groups)

Set ScreenR normalized_count_table

Description

Set function for the normalized_count_table of the ScreenR object

Usage

set_normalized_count_table(object, normalized_count_table)

## S4 method for signature 'screenr_object'
set_normalized_count_table(object, normalized_count_table)

Arguments

object

The ScreenR object obtained using the create_screenr_object
normalized_count_table

A table of the normalized count table

Value

The ScreenR object with the set normalized_count_table

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
normalized_count_table <- get_normalized_count_table(object)
normalized_count_table
set_normalized_count_table(object, normalized_count_table)

Set ScreenR replicates

Description

Set function for the replicates of the ScreenR object

Usage

set_replicates(object, replicates)

## S4 method for signature 'screenr_object'
set_replicates(object, replicates)

Arguments

object

The ScreenR object obtained using the create_screenr_object
replicates

The vector containing the replicates name

Value

The ScreenR object with the specific replicates

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
replicates <- get_replicates(object)
set_replicates(object, replicates)