Package 'CRISPRball' reference manual

Title:	Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering
Description:	A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds.
Authors:	Jared Andrews [aut, cre] , Jacob Steele [ctb]
Maintainer:	Jared Andrews <[email protected]>
License:	MIT + file LICENSE
Version:	1.3.1
Built:	2024-11-02 03:29:23 UTC
Source:	https://github.com/bioc/CRISPRball

Build SQLite database of DepMap data

Description

Build SQLite database of DepMap data

Usage

build_depmap_db(
  retrieve = c("rnai", "crispr", "dependency", "cn", "ccle_tpm", "meta", "drug",
    "gene.summary", "release"),
  file = "depmap_db.sqlite"
)
build_depmap_db(
  retrieve = c("rnai", "crispr", "dependency", "cn", "ccle_tpm", "meta", "drug",
    "gene.summary", "release"),
  file = "depmap_db.sqlite"
)

Arguments

retrieve

Character vector of data to retrieve from DepMap. Options include:

rnai: RNAi screen data from depmap_rnai.
crispr: CRISPR screen data from depmap_crispr.
cn: Copy number data from depmap_copyNumber.
ccle_tpm: TPM data from depmap_TPM.
meta: Cell line metadata from depmap_metadata.
drug: Drug sensitivity data from depmap_drug_sensitivity.
gene.summary: Gene summary data from depmap_gene_summary.
release: DepMap release information from from depmap_release.

file

Name of SQLite database file to create.

Value

Name of SQLite database containing DepMap data.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
build_depmap_db(retrieve = "release")
library(CRISPRball)
build_depmap_db(retrieve = "release")

Create an interactive Shiny app for visualization & exploration of CRISPR analyses

Description

This package was designed to make the visualization, interpretation, and exploration of CRISPR screen analyses accessible for both analysts and bench scientists. It makes high-quality, publication-ready figures simple to create and customize. It also provides a simple interface to DepMap data for comparison and filtering.

Usage

CRISPRball(
  gene.data = NULL,
  sgrna.data = NULL,
  count.summary = NULL,
  norm.counts = NULL,
  h.id = "mag1",
  positive.ctrl.genes = NULL,
  essential.genes = NULL,
  depmap.db = NULL,
  genesets = NULL,
  return.app = TRUE
)
CRISPRball(
  gene.data = NULL,
  sgrna.data = NULL,
  count.summary = NULL,
  norm.counts = NULL,
  h.id = "mag1",
  positive.ctrl.genes = NULL,
  essential.genes = NULL,
  depmap.db = NULL,
  genesets = NULL,
  return.app = TRUE
)

Arguments

`gene.data`	A named list containing `gene_summary.txt` tables as data.frames. Multiple data.frames may be provided, one per element of the list. Users will be able to swap between them within the app. List element names should match names of `sgrna.data` list elements.
`sgrna.data`	A named list containing `sgrna_summary.txt` tables as data.frames. Multiple data.frames may be provided, one per element of the list. Users will be able to swap between them within the app. List element names should match names of `gene.data` list elements.
`count.summary`	Matrix or dataframe containing count summary (`countsummary.txt`) as generated by `mageck count`.
`norm.counts`	Matrix or dataframe containing normalized counts (`count_normalized.txt`) as generated by `mageck count`.
`h.id`	String indicating unique ID for interactive plots. Required if multiple apps are run within the same Rmd file.
`positive.ctrl.genes`	Optional character vector of gene identifiers for positive control genes from the screen so that they can be easily filtered.
`essential.genes`	Optional character vector of gene identifiers of common essential genes (i.e. pan-lethal) so that they can be easily filtered. If provided, overrides the depmap essential genes.
`depmap.db`	Optional character scalar for name of SQLite database returned by `build_depmap_db`.
`genesets`	Optional named list containing genesets that can be interactively highlighted on the plots. The elements of the list should each be a geneset with gene identifiers matching those used in the results.
`return.app`	Optional boolean indicating whether a Shiny app should be returned. `TRUE` by default. If `FALSE`, a named list of app elements (ui and server) will be returned instead. Useful for deploying as a standalone shiny app.

Details

Features with no variation will be removed prior to pca being run for the PCA visualization. Gene labels can be added to the MAplot and volcano plot by clicking a point. The labels can also be dragged around, though adding labels will reset the positions, so it's recommended to add all labels prior to re-positioning them.

Most users will be interested in the main function to create a Shiny application (CRISPRball), though there are certain plotting functions that may be of interest to developers (plot_volcano, plot_rank, plot_lawn, etc). Most included plotting functions produce a ggplot object by default and have few required arguments. Many additional arguments are available for customization to generate complex, publication-ready figures.

This package supplements the MAGeCKFlute package, adding additional functionality, visualizations, and a Shiny interface.

To report bugs, suggest new features, or ask for help, the best method is to create an issue on the github, here, or the bioconductor support site (be sure to tag 'CRISPRball' so that I get a notification!), here

Value

A Shiny app containing interactive visualizations of CRISPR analysis results.

Author(s)

Jared Andrews, Jacob Steele

Examples

library(CRISPRball)
# Create app with no data loaded.
app <- CRISPRball()
if (interactive()) {
    shiny::runApp(app)
}

# Create app with data loaded.
# Create lists of results summaries for each dataset.
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.genes <- read.delim(system.file("extdata", "plasmid.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

d1.sgrnas <- read.delim(system.file("extdata", "esc1.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.sgrnas <- read.delim(system.file("extdata", "plasmid.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
    package = "CRISPRball"
), check.names = FALSE)
norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)

genes <- list(ESC = d1.genes, plasmid = d2.genes)
sgrnas <- list(ESC = d1.sgrnas, plasmid = d2.sgrnas)

app <- CRISPRball(
    gene.data = genes, sgrna.data = sgrnas,
    count.summary = count.summ, norm.counts = norm.counts
)
if (interactive()) {
    shiny::runApp(app)
}
library(CRISPRball)
# Create app with no data loaded.
app <- CRISPRball()
if (interactive()) {
    shiny::runApp(app)
}

# Create app with data loaded.
# Create lists of results summaries for each dataset.
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.genes <- read.delim(system.file("extdata", "plasmid.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

d1.sgrnas <- read.delim(system.file("extdata", "esc1.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
d2.sgrnas <- read.delim(system.file("extdata", "plasmid.sgrna_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)

count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
    package = "CRISPRball"
), check.names = FALSE)
norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)

genes <- list(ESC = d1.genes, plasmid = d2.genes)
sgrnas <- list(ESC = d1.sgrnas, plasmid = d2.sgrnas)

app <- CRISPRball(
    gene.data = genes, sgrna.data = sgrnas,
    count.summary = count.summ, norm.counts = norm.counts
)
if (interactive()) {
    shiny::runApp(app)
}

DepMap copy number data

Description

The DepMap copy number data for CDK2.

Usage

data(depmap_22q1_cn)
data(depmap_22q1_cn)

Format

`depmap_22q1_TPM`

A dataframe with 1754 rows and 9 columns:

depmap_id: Depmap cell line ID
log_copy_number: log2 copy number
entrez_id: Numeric entrez gene ID
gene_name: Gene symbol
dataset: Screen type, either CRISPR or RNAi
cell_line_name: Cell line name
primary_disease: Cell line disease origin
lineage: Cell line lineage
lineage_subtype: Cell line lineage subtype, usually a malignancy
hover.string: String for hover text when plotting

Value

A dataframe containing DepMap 22Q1 copy number data.

Source

https://depmap.org/portal/download/all/

https://bioconductor.org/packages/release/data/experiment/html/depmap.html

DepMap CRISPR screen data

Description

The DepMap CRISPR screen data for CDK2.

Usage

data(depmap_22q1_crispr)
data(depmap_22q1_crispr)

Format

`depmap_22q1_crispr`

A dataframe with 1070 rows and 9 columns:

depmap_id: Depmap cell line ID
dependency: Depmap dependency score
entrez_id: Numeric entrez gene ID
gene_name: Gene symbol
cell_line_name: Cell line name
primary_disease: Cell line disease origin
lineage: Cell line lineage
lineage_subtype: Cell line lineage subtype, usually a malignancy
hover.string: String for hover text when plotting

Value

A dataframe containing DepMap 22Q1 CRISPR screen data.

Source

https://depmap.org/portal/download/all/

https://bioconductor.org/packages/release/data/experiment/html/depmap.html

DepMap CRISPR & RNAi screen data

Description

The DepMap CRISPR & RNAi screen data for CDK2.

Usage

data(depmap_22q1_crispr_rnai)
data(depmap_22q1_crispr_rnai)

Format

`depmap_22q1_crispr_rnai`

A dataframe with 1782 rows and 9 columns:

depmap_id: Depmap cell line ID
dependency: Depmap dependency score
entrez_id: Numeric entrez gene ID
gene_name: Gene symbol
dataset: Screen type, either CRISPR or RNAi
cell_line_name: Cell line name
primary_disease: Cell line disease origin
lineage: Cell line lineage
lineage_subtype: Cell line lineage subtype, usually a malignancy
hover.string: String for hover text when plotting

Value

A dataframe containing DepMap 22Q1 CRISPR and RNAi data.

Source

https://depmap.org/portal/download/all/

https://bioconductor.org/packages/release/data/experiment/html/depmap.html

DepMap RNAi screen data

Description

The DepMap RNAi screen data for CDK2.

Usage

data(depmap_22q1_rnai)
data(depmap_22q1_rnai)

Format

`depmap_22q1_rnai`

A dataframe with 712 rows and 9 columns:

dependency: Depmap dependency score
entrez_id: Numeric entrez gene ID
gene_name: Gene symbol
depmap_id: Depmap cell line ID
cell_line_name: Cell line name
primary_disease: Cell line disease origin
lineage: Cell line lineage
lineage_subtype: Cell line lineage subtype, usually a malignancy
hover.string: String for hover text when plotting

Value

A dataframe containing DepMap 22Q1 RNAi screen data.

Source

https://depmap.org/portal/download/all/

https://bioconductor.org/packages/release/data/experiment/html/depmap.html

DepMap expression data

Description

The DepMap expression data for CDK2.

Usage

data(depmap_22q1_TPM)
data(depmap_22q1_TPM)

Format

`depmap_22q1_TPM`

A dataframe with 1393 rows and 9 columns:

depmap_id: Depmap cell line ID
rna_expression: log2(TPM+1) expression
entrez_id: Numeric entrez gene ID
gene_name: Gene symbol
dataset: Screen type, either CRISPR or RNAi
cell_line_name: Cell line name
primary_disease: Cell line disease origin
lineage: Cell line lineage
lineage_subtype: Cell line lineage subtype, usually a malignancy
hover.string: String for hover text when plotting

Value

A dataframe containing DepMap 22Q1 expression data.

Source

https://depmap.org/portal/download/all/

https://bioconductor.org/packages/release/data/experiment/html/depmap.html

Parse gene summary data for easier plotting and display

Description

Parse gene summary data for easier plotting and display

Usage

gene_ingress(
  df,
  sig.thresh,
  es.thresh,
  es.col,
  sig.col,
  positive.ctrl.genes = NULL,
  essential.genes = NULL,
  depmap.genes = NULL
)
gene_ingress(
  df,
  sig.thresh,
  es.thresh,
  es.col,
  sig.col,
  positive.ctrl.genes = NULL,
  essential.genes = NULL,
  depmap.genes = NULL
)

Arguments

`df`	data.frame of gene summary data. Gene IDs should be in the first column.
`sig.thresh`	Numeric scalar for significance threshold to consider a gene a hit.
`es.thresh`	Numeric scalar for absolute log fold change threshold to consider a gene a hit.
`es.col`	Character scalar for the column name of the effect size value.
`sig.col`	Character scalar for the column name of the significance value.
`positive.ctrl.genes`	Character vector of gene identifiers to label as positive controls.
`essential.genes`	Character vector of gene identifiers to label as essential genes.
`depmap.genes`	data.frame of DepMap gene summary data.

Value

A data.frame of gene summary with additional, easier to plot, columns added.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
out.df <- gene_ingress(d1.genes, 0.05, 0.5, es.col = "LFC", sig.col = "fdr")
library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
out.df <- gene_ingress(d1.genes, 0.05, 0.5, es.col = "LFC", sig.col = "fdr")

Get essential/selective gene information from DepMap summary table.

Description

Get essential/selective gene information from DepMap summary table.

Usage

get_depmap_essentiality(gene, depmap.summary)
get_depmap_essentiality(gene, depmap.summary)

Arguments

`gene`	Character scalar for gene symbol.
`depmap.summary`	data.frame containing DepMap gene summary information.

Value

Named list containing RNAi and CRISPR named lists containing dataset information for the provided gene, if available. If the gene is not found in the summary data.frame, the avail element for the RNAi and CRISPR lists will be set to FALSE.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
build_depmap_db(retrieve = "gene.summary")
pool <- pool::dbPool(RSQLite::SQLite(), dbname = "depmap_db.sqlite")
depmap.gene <- pool::dbGetQuery(pool, "SELECT * FROM 'gene.summary'")

essentials <- get_depmap_essentiality(gene = "CDK2", depmap.summary = depmap.gene)
library(CRISPRball)
build_depmap_db(retrieve = "gene.summary")
pool <- pool::dbPool(RSQLite::SQLite(), dbname = "depmap_db.sqlite")
depmap.gene <- pool::dbGetQuery(pool, "SELECT * FROM 'gene.summary'")

essentials <- get_depmap_essentiality(gene = "CDK2", depmap.summary = depmap.gene)

Create DepMap dataframe for plotting

Description

Based on the requested data type, this function will create a dataframe from the DepMap database that can be used for plotting.

Usage

get_depmap_plot_data(gene, data.type, depmap.meta, depmap.pool)
get_depmap_plot_data(gene, data.type, depmap.meta, depmap.pool)

Arguments

`gene`	Character scalar of gene name.
`data.type`	Character scalar of data type to retrieve. One of "dependency", "crispr", "rnai", "cn", or "ccle_tpm".
`depmap.meta`	data.frame of DepMap cell line metadata, as stored in the 'meta' table of the SQLite database built by `build_depmap_db`.
`depmap.pool`	pool connection to DepMap SQLite database built with `build_depmap_db`.

Value

data.frame containing appropriate DepMap data for plotting.

Author(s)

Jared Andrews

Examples

## Not run: 
library(CRISPRball)
build_depmap_db(retrieve = c("meta", "crispr"))
pool <- pool::dbPool(RSQLite::SQLite(), dbname = "depmap_db.sqlite")
depmap.meta <- pool::dbGetQuery(pool, "SELECT * FROM 'meta'")

df <- get_depmap_plot_data(
    gene = "CDK2", data.type = "crispr",
    depmap.meta = depmap.meta, depmap.pool = pool
)

## End(Not run)
## Not run: 
library(CRISPRball)
build_depmap_db(retrieve = c("meta", "crispr"))
pool <- pool::dbPool(RSQLite::SQLite(), dbname = "depmap_db.sqlite")
depmap.meta <- pool::dbGetQuery(pool, "SELECT * FROM 'meta'")

df <- get_depmap_plot_data(
    gene = "CDK2", data.type = "crispr",
    depmap.meta = depmap.meta, depmap.pool = pool
)

## End(Not run)

Create interactive bar plot

Description

Create an interactive bar plot for specific summary information for each sample in the dataset.

Usage

plot_bar(
  count.summary,
  x = "Label",
  y = "GiniIndex",
  title = "sgRNA Read Distribution",
  xlab = NULL,
  ylab = "Gini Index",
  fill = "#E69F00",
  yaxis.addition = 0.05
)
plot_bar(
  count.summary,
  x = "Label",
  y = "GiniIndex",
  title = "sgRNA Read Distribution",
  xlab = NULL,
  ylab = "Gini Index",
  fill = "#E69F00",
  yaxis.addition = 0.05
)

Arguments

`count.summary`	data.frame containing the count summary information for each sample.
`x`	Character scalar for column of count.summary to plot along the x-axis.
`y`	Character scalar for column of count.summary to plot along the y-axis.
`title`	Character scalar for title of the plot.
`xlab`	Character scalar for label of the x-axis.
`ylab`	Character scalar for label of the y-axis.
`fill`	Character scalar for bar fill color in hex.
`yaxis.addition`	Numeric scalar for additional space to add to the y-axis.

Value

An interactive bar chart showing the specified summary information for each sample. The axis and plot title are editable.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
    package = "CRISPRball"
), check.names = FALSE)
# Gini Index plot
plot_bar(count.summ)

# Zero count sgRNAs plot
plot_bar(count.summ,
    x = "Label", y = "Zerocounts", title = "Fully Depleted sgRNAs",
    fill = "#394E80", ylab = "Zero Count sgRNAs", yaxis.addition = 10
)
library(CRISPRball)
count.summ <- read.delim(system.file("extdata", "escneg.countsummary.txt",
    package = "CRISPRball"
), check.names = FALSE)
# Gini Index plot
plot_bar(count.summ)

# Zero count sgRNAs plot
plot_bar(count.summ,
    x = "Label", y = "Zerocounts", title = "Fully Depleted sgRNAs",
    fill = "#394E80", ylab = "Zero Count sgRNAs", yaxis.addition = 10
)

Plot a Correlation Heatmap

Description

This function creates a heatmap using ComplexHeatmap to display the correlation values in a matrix. The color of each cell in the heatmap is determined by the corresponding correlation value, using a color ramp that ranges from the minimum value color to a maximum value color.

Usage

plot_correlation_heatmap(
  mat,
  min.color = "#FF0000",
  max.color = "#0000FF",
  legend.title = "Pearson Corr.",
  plot.title = "Correlation Matrix"
)
plot_correlation_heatmap(
  mat,
  min.color = "#FF0000",
  max.color = "#0000FF",
  legend.title = "Pearson Corr.",
  plot.title = "Correlation Matrix"
)

Arguments

`mat`	A matrix containing the correlation values.
`min.color`	Character scalar for the hexadecimal color code for the minimum values in the heatmap.
`max.color`	Character scalar for the hexadecimal color code for the maximum values in the heatmap.
`legend.title`	Character scalar for title of the legend.
`plot.title`	Character scalar for title of the plot.

Value

A Heatmap object.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)
norm.counts <- as.matrix(norm.counts[, c(-1, -2)])
norm.counts.log <- log2(norm.counts + 1)
cor.mat <- cor(norm.counts.log)
plot_correlation_heatmap(cor.mat)
library(CRISPRball)
norm.counts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)
norm.counts <- as.matrix(norm.counts[, c(-1, -2)])
norm.counts.log <- log2(norm.counts + 1)
cor.mat <- cor(norm.counts.log)
plot_correlation_heatmap(cor.mat)

Plot gene copy number information from DepMap

Description

Plot gene copy number information from DepMap

Usage

plot_depmap_cn(df, color = "#CEA3CB", plot.grid = FALSE)
plot_depmap_cn(df, color = "#CEA3CB", plot.grid = FALSE)

Arguments

`df`	data.frame containing information for a single gene as returned by `get_depmap_plot_data`.
`color`	Character scalar for trace color.
`plot.grid`	Boolean indicating whether to plot gridlines.

Value

plotly object

Author(s)

Jared Andrews

Examples

library(CRISPRball)
data(depmap_22q1_cn)
plot_depmap_cn(depmap_22q1_cn)
library(CRISPRball)
data(depmap_22q1_cn)
plot_depmap_cn(depmap_22q1_cn)

Plot gene dependency information from DepMap CRISPR and RNAi tables

Description

Plot gene dependency information from DepMap CRISPR and RNAi tables

Usage

plot_depmap_dependency(
  df,
  crispr.color = "#3584B5",
  rnai.color = "#52288E",
  depline = TRUE,
  plot.grid = FALSE
)
plot_depmap_dependency(
  df,
  crispr.color = "#3584B5",
  rnai.color = "#52288E",
  depline = TRUE,
  plot.grid = FALSE
)

Arguments

`df`	data.frame containing information for a single gene as returned by `get_depmap_plot_data`.
`crispr.color`	Character scalar for CRISPR trace color as hexcode.
`rnai.color`	Character scalar for RNAi trace color as hexcode.
`depline`	Boolean indicating whether to show the dependency threshold line.
`plot.grid`	Boolean indicating whether to plot gridlines.

Value

plotly object

Author(s)

Jared Andrews

Examples

library(CRISPRball)
data(depmap_22q1_crispr_rnai)
plot_depmap_dependency(depmap_22q1_crispr_rnai)
library(CRISPRball)
data(depmap_22q1_crispr_rnai)
plot_depmap_dependency(depmap_22q1_crispr_rnai)

Plot gene expression information from DepMap, mostly from CCLE

Description

Plot gene expression information from DepMap, mostly from CCLE

Usage

plot_depmap_expression(df, color = "#7B8CB2", plot.grid = FALSE)
plot_depmap_expression(df, color = "#7B8CB2", plot.grid = FALSE)

Arguments

`df`	data.frame containing information for a single gene as returned by `get_depmap_plot_data`.
`color`	Character scalar for trace color.
`plot.grid`	Boolean indicating whether to plot gridlines.

Value

plotly object

Author(s)

Jared Andrews

Examples

library(CRISPRball)
data(depmap_22q1_TPM)
plot_depmap_expression(depmap_22q1_TPM)
library(CRISPRball)
data(depmap_22q1_TPM)
plot_depmap_expression(depmap_22q1_TPM)

Plot selected information across lineages from DepMap.

Description

Plot selected information across lineages from DepMap.

Usage

plot_depmap_lineages(
  df,
  plot.val,
  group.by,
  lineage = NULL,
  depline = TRUE,
  label.size = 12,
  pt.size = 5,
  pt.color = "#56B4E9",
  boxplot.fill = "#E2E2E2",
  boxplot.line.color = "#000000"
)
plot_depmap_lineages(
  df,
  plot.val,
  group.by,
  lineage = NULL,
  depline = TRUE,
  label.size = 12,
  pt.size = 5,
  pt.color = "#56B4E9",
  boxplot.fill = "#E2E2E2",
  boxplot.line.color = "#000000"
)

Arguments

`df`	data.frame containing information for a single gene as returned by `get_depmap_plot_data`.
`plot.val`	Character scalar of column name to plot values from.
`group.by`	Character scalar of column name to group by.
`lineage`	Character scalar of lineage for which to plot sub-lineage data.
`depline`	Boolean indicating whether to show the dependency threshold line.
`label.size`	Numeric scaler for axis label size.
`pt.size`	Numeric scalar for point size.
`pt.color`	Character scalar for point color.
`boxplot.fill`	Character scalar for boxplot fill color.
`boxplot.line.color`	Character scalar for boxplot line color.

Value

plotly object

Author(s)

Jared Andrews

Examples

library(CRISPRball)
data("depmap_22q1_rnai")
plot_depmap_lineages(df = depmap_22q1_rnai, plot.val = "dependency", group.by = "lineage")
library(CRISPRball)
data("depmap_22q1_rnai")
plot_depmap_lineages(df = depmap_22q1_rnai, plot.val = "dependency", group.by = "lineage")

Create a plotly plot from a distribution of values

Description

This function creates a plotly plot with the distribution of values for each column in the mat matrix, using different colors for each column. The legend will show the column names from mat, and the plot will have the title "Distribution of read counts".

Usage

plot_hist(
  mat,
  title = NULL,
  xlab = "Values",
  ylab = "Frequency",
  show.grid = FALSE
)
plot_hist(
  mat,
  title = NULL,
  xlab = "Values",
  ylab = "Frequency",
  show.grid = FALSE
)

Arguments

`mat`	A matrix with the data to plot.
`title`	A character scalar for the title of the plot.
`xlab`	Character scalar for label of the x-axis.
`ylab`	Character scalar for label of the y-axis.
`show.grid`	A boolean for whether to show the grid lines.

Value

A plotly plot with the distribution of read counts.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
cts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)
cts.log <- as.matrix(log2(cts[, c(-1, -2)] + 1))
colnames(cts.log) <- colnames(cts)[c(-1, -2)]

plot_hist(cts.log,
    title = "Distribution of read counts",
    xlab = "log2(counts + 1)", ylab = "Frequency"
)
library(CRISPRball)
cts <- read.delim(system.file("extdata", "escneg.count_normalized.txt",
    package = "CRISPRball"
), check.names = FALSE)
cts.log <- as.matrix(log2(cts[, c(-1, -2)] + 1))
colnames(cts.log) <- colnames(cts)[c(-1, -2)]

plot_hist(cts.log,
    title = "Distribution of read counts",
    xlab = "log2(counts + 1)", ylab = "Frequency"
)

Create an interactive lawn plot

Description

Create an interactive lawn plot for data with significance values. Typically, this plot is randomly ordered along the x-axis, but the user is free to order it by any term in res that they'd like.

Usage

plot_lawn(
  res,
  ylim = 5,
  fc.thresh = 0.5,
  hover.info = NULL,
  sig.line = TRUE,
  h.id = "crispr",
  feat.term = "rows",
  x.term = "RandomIndex",
  sig.term = "fdr",
  lfc.term = "LFC",
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = TRUE,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_lawn"
)
plot_lawn(
  res,
  ylim = 5,
  fc.thresh = 0.5,
  hover.info = NULL,
  sig.line = TRUE,
  h.id = "crispr",
  feat.term = "rows",
  x.term = "RandomIndex",
  sig.term = "fdr",
  lfc.term = "LFC",
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = TRUE,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_lawn"
)

Arguments

`res`	Dataframe containing, at minimum, significance values and a term by which the x-axis can be ordered.
`ylim`	Positive numeric scalar indicating y-axis limits. The negative value will be used for the lower limit.
`fc.thresh`	Numeric scalar indicating the fold change threshold for coloring significant features.
`hover.info`	Character vector indicating which additional columns from `res` to include in the hover info.
`sig.line`	Logical indicating whether to add a significance threshold line to the plot.
`h.id`	Character scalar indicating the unique ID of the plotly object. Can usually be ignored, but should be used if multiple plots are being created in the same R session (e.g. Shiny app).
`feat.term`	Character scalar indicating the column name of the feature IDs in `res`.
`x.term`	Character scalar for the x-axis term from `res` to be plotted.
`sig.term`	Character scalar indicating the column name of the significance values in `res`.
`lfc.term`	Character scalar indicating the column name of the log fold change values in `res`.
`down.color`	Character scalar indicating the color of down-regulated features.
`up.color`	Character scalar indicating the color of up-regulated features.
`insig.color`	Character scalar indicating the color of insignificant features.
`sig.thresh`	Numeric scalar indicating the significance threshold for coloring significant features.
`fs`	Dataframe containing coordinates and label information for points that should be labeled. Columns should be: `x` - x coordinate of the point `y` - y coordinate of the point `customdata` - label to be displayed
`sig.size`	Numeric scalar indicating the size of significant feature points.
`insig.size`	Numeric scalar indicating the size of insignificant feature points.
`sig.opacity`	Numeric scalar indicating the opacity of significant feature points.
`insig.opacity`	Numeric scalar indicating the opacity of insignificant feature points.
`label.size`	Numeric scalar indicating the size of feature labels.
`webgl`	Logical indicating whether to use WebGL for rendering the plot.
`webgl.ratio`	Numeric scalar indicating the ratio of WebGL to HTML5 canvas rendering, increases resolution of saved plot when WebGL plotting is not used.
`show.counts`	Logical indicating whether to show annotations for the number of features in the plot.
`show.hl.counts`	Logical indicating whether to show annotations for the number of highlighted features in the plot.
`counts.size`	Numeric scalar indicating the size of the feature counts labels.
`highlight.featsets`	Character vector indicating which feature sets should be highlighted.
`highlight.feats`	Character vector indicating which features should be highlighted.
`featsets`	Named list of feature sets to be used for highlighting.
`highlight.feats.color`	Character scalar indicating the color of highlighted features.
`highlight.feats.size`	Numeric scalar indicating the size of highlighted features.
`highlight.feats.opac`	Numeric scalar indicating the opacity of highlighted features.
`highlight.feats.linecolor`	Character scalar indicating the line color of highlighted features.
`highlight.feats.linewidth`	Numeric scalar indicating the line width of highlighted features.
`highlight.feats.label`	Logical indicating whether to label highlighted features.
`highlight.featsets.color`	Character scalar indicating the color of highlighted feature sets.
`highlight.featsets.size`	Numeric scalar indicating the point size of highlighted feature sets.
`highlight.featsets.opac`	Numeric scalar indicating the opacity of highlighted feature sets.
`highlight.featsets.linecolor`	Character scalar indicating the line color of highlighted feature sets.
`highlight.featsets.linewidth`	Numeric scalar indicating the line width of highlighted feature sets.
`highlight.featsets.label`	Logical indicating whether to label highlighted feature sets.
`h.id.suffix`	Character scalar indicating the suffix to be added to the plotly object ID.

Value

An interactive plotly rank plot.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_lawn(plot.df, feat.term = "id")
library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_lawn(plot.df, feat.term = "id")

Plot a biplot from a PCAtools `pca` object

Description

This function plots a biplot from a PCAtools pca object.

Usage

plot_pca_biplot(
  pca.res,
  dim.x = "PC1",
  dim.y = "PC2",
  dim.z = NULL,
  plot.title = "PCA Biplot",
  color.by = NULL,
  shape.by = NULL,
  hover.info = NULL,
  show.loadings = FALSE,
  n.loadings = 3,
  pt.size = 12
)
plot_pca_biplot(
  pca.res,
  dim.x = "PC1",
  dim.y = "PC2",
  dim.z = NULL,
  plot.title = "PCA Biplot",
  color.by = NULL,
  shape.by = NULL,
  hover.info = NULL,
  show.loadings = FALSE,
  n.loadings = 3,
  pt.size = 12
)

Arguments

`pca.res`	A `pca` generated by the PCAtools package.
`dim.x`	Character scalar for the principal component to plot on the x-axis.
`dim.y`	Character scalar for the principal component to plot on the y-axis.
`dim.z`	Character scalar for the principal component to plot on the z-axis.
`plot.title`	Character scalar for the title of the plot.
`color.by`	Character scalar for the column name to use for coloring points.
`shape.by`	Character scalar for the column name to use for shaping points.
`hover.info`	Character vector of column name(s) to include in the hover info for each point.
`show.loadings`	Boolean indicating whether to show component loadings on the plot.
`n.loadings`	Integer for number of loadings to show.
`pt.size`	Numeric size of the plotted points.

Value

A plotly plot of the PCA biplot, or a text grob indicating no PCA was provided.

Author(s)

Jared Andrews

Examples

library("PCAtools")
library("CRISPRball")
col <- 10
row <- 2000
mat <- matrix(
    rexp(col * row, rate = 0.1),
    ncol = col
)
rownames(mat) <- paste0("gene", seq_len(nrow(mat)))
colnames(mat) <- paste0("sample", seq_len(ncol(mat)))

metadata <- data.frame(row.names = colnames(mat))
metadata$Group <- rep(NA, ncol(mat))
metadata$Group[seq(1, 10, 2)] <- "A"
metadata$Group[seq(2, 10, 2)] <- "B"

p <- pca(mat, metadata = metadata, removeVar = 0.1)
plot_pca_biplot(p, color.by = "Group")
library("PCAtools")
library("CRISPRball")
col <- 10
row <- 2000
mat <- matrix(
    rexp(col * row, rate = 0.1),
    ncol = col
)
rownames(mat) <- paste0("gene", seq_len(nrow(mat)))
colnames(mat) <- paste0("sample", seq_len(ncol(mat)))

metadata <- data.frame(row.names = colnames(mat))
metadata$Group <- rep(NA, ncol(mat))
metadata$Group[seq(1, 10, 2)] <- "A"
metadata$Group[seq(2, 10, 2)] <- "B"

p <- pca(mat, metadata = metadata, removeVar = 0.1)
plot_pca_biplot(p, color.by = "Group")

Create an interactive rank plot

Description

Create an interactive rank plot for data with fold change, significance terms, and rank.

Usage

plot_rank(
  res,
  ylim = c(-10, 10),
  y.thresh = 0.5,
  y.lines = TRUE,
  hover.info = NULL,
  h.id = "crispr",
  feat.term = "rows",
  sig.term = "fdr",
  rank.term = "LFC",
  rank.ascending = TRUE,
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = TRUE,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_volc"
)
plot_rank(
  res,
  ylim = c(-10, 10),
  y.thresh = 0.5,
  y.lines = TRUE,
  hover.info = NULL,
  h.id = "crispr",
  feat.term = "rows",
  sig.term = "fdr",
  rank.term = "LFC",
  rank.ascending = TRUE,
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = TRUE,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_volc"
)

Arguments

`res`	Dataframe containing, at minimum, significance values and something to rank by (LFC, RRA score, betas, etc).
`ylim`	Numeric vector of length two for the y-axis limits.
`y.thresh`	Numeric scalar used as the y-axis threshold for point coloring. The negative of this value is also used as the threshold.
`y.lines`	Logical as for whether or not to show horizontal lines at `y.thresh`.
`hover.info`	Character vector indicating which additional columns from `res` to include in the hover info.
`h.id`	Character scalar indicating the unique ID of the plotly object. Can usually be ignored, but should be used if multiple plots are being created in the same R session (e.g. Shiny app).
`feat.term`	Character scalar indicating the column name of the feature IDs in `res`.
`sig.term`	Character scalar indicating the column name of the significance values in `res`.
`rank.term`	Character scalar for the term to rank by from `res`. This will be used as the y-axis.
`rank.ascending`	Boolean indicating whether or not the rank should be ascending.
`down.color`	Character scalar indicating the color of down-regulated features.
`up.color`	Character scalar indicating the color of up-regulated features.
`insig.color`	Character scalar indicating the color of insignificant features.
`sig.thresh`	Numeric scalar indicating the significance threshold for coloring significant features.
`fs`	Dataframe containing coordinates and label information for points that should be labeled. Columns should be: `x` - x coordinate of the point `y` - y coordinate of the point `customdata` - label to be displayed
`sig.size`	Numeric scalar indicating the size of significant feature points.
`insig.size`	Numeric scalar indicating the size of insignificant feature points.
`sig.opacity`	Numeric scalar indicating the opacity of significant feature points.
`insig.opacity`	Numeric scalar indicating the opacity of insignificant feature points.
`label.size`	Numeric scalar indicating the size of feature labels.
`webgl`	Logical indicating whether to use WebGL for rendering the plot.
`webgl.ratio`	Numeric scalar indicating the ratio of WebGL to HTML5 canvas rendering, increases resolution of saved plot when WebGL plotting is not used.
`show.counts`	Logical indicating whether to show annotations for the number of features in the plot.
`show.hl.counts`	Logical indicating whether to show annotations for the number of highlighted features in the plot.
`counts.size`	Numeric scalar indicating the size of the feature counts labels.
`highlight.featsets`	Character vector indicating which feature sets should be highlighted.
`highlight.feats`	Character vector indicating which features should be highlighted.
`featsets`	Named list of feature sets to be used for highlighting.
`highlight.feats.color`	Character scalar indicating the color of highlighted features.
`highlight.feats.size`	Numeric scalar indicating the size of highlighted features.
`highlight.feats.opac`	Numeric scalar indicating the opacity of highlighted features.
`highlight.feats.linecolor`	Character scalar indicating the line color of highlighted features.
`highlight.feats.linewidth`	Numeric scalar indicating the line width of highlighted features.
`highlight.feats.label`	Logical indicating whether to label highlighted features.
`highlight.featsets.color`	Character scalar indicating the color of highlighted feature sets.
`highlight.featsets.size`	Numeric scalar indicating the point size of highlighted feature sets.
`highlight.featsets.opac`	Numeric scalar indicating the opacity of highlighted feature sets.
`highlight.featsets.linecolor`	Character scalar indicating the line color of highlighted feature sets.
`highlight.featsets.linewidth`	Numeric scalar indicating the line width of highlighted feature sets.
`highlight.featsets.label`	Logical indicating whether to label highlighted feature sets.
`h.id.suffix`	Character scalar indicating the suffix to be added to the plotly object ID.

Value

An interactive plotly rank plot.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_rank(plot.df, feat.term = "id")
library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_rank(plot.df, feat.term = "id")

Create an interactive volcano plot

Description

Create an interactive volcano plot for data with fold change and significance terms.

Usage

plot_volcano(
  res,
  xlim = 5,
  ylim = 5,
  fc.thresh = 0.5,
  fc.lines = TRUE,
  hover.info = NULL,
  sig.line = TRUE,
  h.id = "crispr",
  feat.term = "rows",
  sig.term = "fdr",
  lfc.term = "LFC",
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = 8,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_volc"
)
plot_volcano(
  res,
  xlim = 5,
  ylim = 5,
  fc.thresh = 0.5,
  fc.lines = TRUE,
  hover.info = NULL,
  sig.line = TRUE,
  h.id = "crispr",
  feat.term = "rows",
  sig.term = "fdr",
  lfc.term = "LFC",
  down.color = "#0026ff",
  up.color = "#ff0000",
  insig.color = "#A6A6A6",
  sig.thresh = 0.05,
  fs = NULL,
  sig.size = 6,
  insig.size = 5,
  sig.opacity = 1,
  insig.opacity = 0.5,
  label.size = 10,
  webgl = TRUE,
  webgl.ratio = 7,
  show.counts = TRUE,
  show.hl.counts = TRUE,
  counts.size = 8,
  highlight.featsets = NULL,
  highlight.feats = NULL,
  featsets = NULL,
  highlight.feats.color = "#E69F00",
  highlight.feats.size = 7,
  highlight.feats.opac = 1,
  highlight.feats.linecolor = "#000000",
  highlight.feats.linewidth = 1,
  highlight.feats.label = TRUE,
  highlight.featsets.color = "#009E73",
  highlight.featsets.size = 7,
  highlight.featsets.opac = 1,
  highlight.featsets.linecolor = "#000000",
  highlight.featsets.linewidth = 1,
  highlight.featsets.label = FALSE,
  h.id.suffix = "_volc"
)

Arguments

`res`	Dataframe containing, at minimum, fold change and significance values.
`xlim`	Positive numeric scalar indicating x-axis limits. The negative value will be used for the lower limit.
`ylim`	Positive numeric scalar indicating y-axis limits. The negative value will be used for the lower limit.
`fc.thresh`	Numeric scalar indicating the fold change threshold for coloring significant features.
`fc.lines`	Logical indicating whether to add fold change threshold lines to the plot.
`hover.info`	Character vector indicating which additional columns from `res` to include in the hover info.
`sig.line`	Logical indicating whether to add a significance threshold line to the plot.
`h.id`	Character scalar indicating the unique ID of the plotly object. Can usually be ignored, but should be used if multiple plots are being created in the same R session (e.g. Shiny app).
`feat.term`	Character scalar indicating the column name of the feature IDs in `res`.
`sig.term`	Character scalar indicating the column name of the significance values in `res`.
`lfc.term`	Character scalar indicating the column name of the log fold change values in `res`.
`down.color`	Character scalar indicating the color of down-regulated features.
`up.color`	Character scalar indicating the color of up-regulated features.
`insig.color`	Character scalar indicating the color of insignificant features.
`sig.thresh`	Numeric scalar indicating the significance threshold for coloring significant features.
`fs`	Dataframe containing coordinates and label information for points that should be labeled. Columns should be: `x` - x coordinate of the point `y` - y coordinate of the point `customdata` - label to be displayed
`sig.size`	Numeric scalar indicating the size of significant feature points.
`insig.size`	Numeric scalar indicating the size of insignificant feature points.
`sig.opacity`	Numeric scalar indicating the opacity of significant feature points.
`insig.opacity`	Numeric scalar indicating the opacity of insignificant feature points.
`label.size`	Numeric scalar indicating the size of feature labels.
`webgl`	Logical indicating whether to use WebGL for rendering the plot.
`webgl.ratio`	Numeric scalar indicating the ratio of WebGL to HTML5 canvas rendering, increases resolution of saved plot when WebGL plotting is not used.
`show.counts`	Logical indicating whether to show annotations for the number of features in the plot.
`show.hl.counts`	Logical indicating whether to show annotations for the number of highlighted features in the plot.
`counts.size`	Numeric scalar indicating the size of the feature counts labels.
`highlight.featsets`	Character vector indicating which feature sets should be highlighted.
`highlight.feats`	Character vector indicating which features should be highlighted.
`featsets`	Named list of feature sets to be used for highlighting.
`highlight.feats.color`	Character scalar indicating the color of highlighted features.
`highlight.feats.size`	Numeric scalar indicating the size of highlighted features.
`highlight.feats.opac`	Numeric scalar indicating the opacity of highlighted features.
`highlight.feats.linecolor`	Character scalar indicating the line color of highlighted features.
`highlight.feats.linewidth`	Numeric scalar indicating the line width of highlighted features.
`highlight.feats.label`	Logical indicating whether to label highlighted features.
`highlight.featsets.color`	Character scalar indicating the color of highlighted feature sets.
`highlight.featsets.size`	Numeric scalar indicating the point size of highlighted feature sets.
`highlight.featsets.opac`	Numeric scalar indicating the opacity of highlighted feature sets.
`highlight.featsets.linecolor`	Character scalar indicating the line color of highlighted feature sets.
`highlight.featsets.linewidth`	Numeric scalar indicating the line width of highlighted feature sets.
`highlight.featsets.label`	Logical indicating whether to label highlighted feature sets.
`h.id.suffix`	Character scalar indicating the suffix to be added to the plotly object ID.

Value

An interactive plotly volcano plot.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_volcano(plot.df, feat.term = "id")
library(CRISPRball)
d1.genes <- read.delim(system.file("extdata", "esc1.gene_summary.txt",
    package = "CRISPRball"
), check.names = FALSE)
plot.df <- gene_ingress(d1.genes,
    sig.thresh = 0.05, es.thresh = 0.5,
    es.col = "LFC", sig.col = "fdr"
)
plot_volcano(plot.df, feat.term = "id")

Read and parse MAGeCK MLE output gene summary file

Description

This function reads the gene summary file output by mageck mle and parses it into a list of data.frames, one for each sample. The sample names are extracted from the column names of the input file and used as the names of the list elements.

Usage

read_mle_gene_summary(filepath)
read_mle_gene_summary(filepath)

Arguments

filepath

Path to the gene summary file output by mageck mle.

Value

A named list of data.frames containing MAGeCK MLE output, one for each sample contained in the file.

Author(s)

Jared Andrews

Examples

library(CRISPRball)
mle_gene_summary <- file.path(system.file("extdata", "beta_leukemia.gene_summary.txt",
    package = "CRISPRball"
))
gene_data <- read_mle_gene_summary(mle_gene_summary)
library(CRISPRball)
mle_gene_summary <- file.path(system.file("extdata", "beta_leukemia.gene_summary.txt",
    package = "CRISPRball"
))
gene_data <- read_mle_gene_summary(mle_gene_summary)

Package 'CRISPRball'

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