Package 'RiboCrypt' reference manual

Title:	Interactive visualization in genomics
Description:	R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development.
Authors:	Michal Swirski [aut, cre, cph], Haakon Tjeldnes [aut, ctb], Kornel Labun [ctb]
Maintainer:	Michal Swirski <[email protected]>
License:	MIT + file LICENSE
Version:	1.11.0
Built:	2024-09-15 05:20:09 UTC
Source:	https://github.com/bioc/RiboCrypt

Differential expression plots (1D or 2D)

Description

Gives you interactive 1D or 2D DE plots

Usage

DEG_plot(
  dt,
  draw_non_regulated = FALSE,
  xlim = ifelse(two_dimensions, "bidir.max", "auto"),
  ylim = "bidir.max",
  xlab = ifelse(two_dimensions, "RNA fold change (log2)", "Mean counts (log2)"),
  ylab = ifelse(two_dimensions, "RFP fold change (log2)", "Fold change (log2)"),
  two_dimensions = ifelse("LFC" %in% colnames(dt), FALSE, TRUE),
  color.values = c(`No change` = "black", Significant = "red", Buffering = "purple",
    `mRNA abundance` = "darkgreen", Expression = "blue", Forwarded = "yellow", Inverse =
    "aquamarine", Translation = "orange4")
)
DEG_plot(
  dt,
  draw_non_regulated = FALSE,
  xlim = ifelse(two_dimensions, "bidir.max", "auto"),
  ylim = "bidir.max",
  xlab = ifelse(two_dimensions, "RNA fold change (log2)", "Mean counts (log2)"),
  ylab = ifelse(two_dimensions, "RFP fold change (log2)", "Fold change (log2)"),
  two_dimensions = ifelse("LFC" %in% colnames(dt), FALSE, TRUE),
  color.values = c(`No change` = "black", Significant = "red", Buffering = "purple",
    `mRNA abundance` = "darkgreen", Expression = "blue", Forwarded = "yellow", Inverse =
    "aquamarine", Translation = "orange4")
)

Arguments

`dt`	a data.table with results from a differential expression run. Normally from: `ORFik::DTEG.analysis(df1, df2)`
`draw_non_regulated`	logical, default FALSE. Should non-regulated rows be included in the plot? Will make the plot faster to render if skipped (FALSE)
`xlim`	numeric vector or character preset, default: `ifelse(two_dimensions, "bidir.max", "auto")` (Equal in both + / - direction, using max value + 0.5 of meanCounts(in 1d) / rna(in 2d) column of dt). If you want ggplot to decide limit, set to "auto". For numeric vector, specify min and max x limit: like c(-5, 5)
`ylim`	numeric vector or character preset, default: "bidir.max" (Equal in both + / - direction, using max value + 0.5 of LFC(in 1d) / rfp(in 2d) column of dt). If you want ggplot to decide limit, set to "auto". For numeric vector, specify min and max x limit: like c(-5, 5)
`xlab`	character, default: `ifelse(two_dimensions, "RNA fold change (log2)", "Mean counts (log2)")`
`ylab`	character, default: `ifelse(two_dimensions, "RFP fold change (log2)", "Fold change (log2)")`
`two_dimensions`	logical, default: `ifelse("LFC" %in% colnames(dt), FALSE, TRUE)` Is this two dimensional, like: Ribo-seq vs RNA-seq. Alternative, FALSE: Then Log fold change vs mean counts
`color.values`	named character vector, default: `c("No change" = "black", "Significant" = "red", "Buffering" = "purple", "mRNA abundance" = "darkgreen", "Expression" = "blue", "Forwarded" = "yellow", "Inverse" = "aquamarine", "Translation" = "orange4")`

Value

plotly object

Examples

# Load experiment
df <- ORFik.template.experiment()
# 1 Dimensional analysis
dt <- DEG.analysis(df[df$libtype == "RNA",])
dt$Regulation[1] <- "Significant" # Fake sig level
DEG_plot(dt, draw_non_regulated = TRUE)
# 2 Dimensional analysis
dt_2d <- DTEG.analysis(df[df$libtype == "RFP",], df[df$libtype == "RNA",],
                    output.dir = NULL)
dt_2d$Regulation[4] <- "Translation" # Fake sig level
dt_2d$Regulation[5] <- "Buffering" # Fake sig level
DEG_plot(dt_2d, draw_non_regulated = TRUE)
# Load experiment
df <- ORFik.template.experiment()
# 1 Dimensional analysis
dt <- DEG.analysis(df[df$libtype == "RNA",])
dt$Regulation[1] <- "Significant" # Fake sig level
DEG_plot(dt, draw_non_regulated = TRUE)
# 2 Dimensional analysis
dt_2d <- DTEG.analysis(df[df$libtype == "RFP",], df[df$libtype == "RNA",],
                    output.dir = NULL)
dt_2d$Regulation[4] <- "Translation" # Fake sig level
dt_2d$Regulation[5] <- "Buffering" # Fake sig level
DEG_plot(dt_2d, draw_non_regulated = TRUE)

Distance to following range

Description

Distance to following range

Usage

distanceToFollowing(grl, grl2 = grl, ignore.strand = FALSE)
distanceToFollowing(grl, grl2 = grl, ignore.strand = FALSE)

Arguments

`grl`	a GRangesList
`grl2`	a GRangesList, default 'grl'
`ignore.strand`	logical, default FALSE

Value

numeric vector of distance

Fetch Javascript sequence

Description

Fetch Javascript sequence

Usage

fetch_JS_seq(
  target_seq,
  nplots,
  distance = 50,
  display_dist,
  aa_letter_code = "one_letter"
)
fetch_JS_seq(
  target_seq,
  nplots,
  distance = 50,
  display_dist,
  aa_letter_code = "one_letter"
)

Arguments

`target_seq`	the target sequence
`nplots`	number of plots
`distance`	numeric, default 50.
`display_dist`	display distance
`aa_letter_code`	"one_letter"

Value

a list of 2 lists, the nt list (per frame, total 3) and AA list (per frame, total 3)

Fetch summary of uniprot id

Description

Fetch summary of uniprot id

Usage

fetch_summary(qualifier, provider = "alphafold")
fetch_summary(qualifier, provider = "alphafold")

Arguments

`qualifier`	uniprot ids
`provider`	"pdbe", alternatives: "alphafold", "all"

Value

a character of json

Multi-omics animation using list input

Description

The animation will move with a play butten, there is 1 transition per library given.

Usage

multiOmicsPlot_animate(
  display_range,
  annotation = display_range,
  reference_sequence,
  reads,
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = NULL,
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = NULL,
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  BPPARAM = BiocParallel::SerialParam()
)
multiOmicsPlot_animate(
  display_range,
  annotation = display_range,
  reference_sequence,
  reads,
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = NULL,
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = NULL,
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  BPPARAM = BiocParallel::SerialParam()
)

Arguments

`display_range`	the whole region to visualize, a `GRangesList` or `GRanges` object
`annotation`	the whole annotation which your target region is a subset, a `GRangesList` or `GRanges` object
`reference_sequence`	the genome reference, a `FaFile` or `FaFile` convertible object
`reads`	the NGS libraries, as a list of `GRanges` with or without score column for replicates.
`viewMode`	character, default "tx" (transcript coordinates, first position is 1, exons are merged into a single sequence) Alternative: "genomic" (genomic coordinates, first position is first position in `display_range` argument. Introns are displayed).
`custom_regions`	a GRangesList or NULL, default: NULL. The alternative annotation, like self defined uORFs etc. The vertical annotation bars will have a different color.
`leader_extension`	integer, default 0. (How much to extend view upstream)
`trailer_extension`	integer, default 0. (How much to extend view downstream)
`withFrames`	a logical vector, default NULL. Alternative: a length 1 or same length as list length of "reads" argument.
`frames_type`	character, default "lines". Alternative: - columns - stacks - area
`colors`	character, default NULL (automatic colouring). If "withFrames" argument is TRUE, colors are set to to c("red", "green", "blue") for the 3 frames. Alternative: Character vector of length 1 or length of "reads" list argument.
`kmers`	numeric (integer), bin positions into kmers.
`kmers_type`	character, function used for kmers sliding window. default: "mean", alternative: "sum"
`ylabels`	character, default NULL. Name of libraries in "reads" list arugment.
`lib_to_annotation_proportions`	numeric vector of length 2. relative sizes of profiles and annotation.
`lib_proportions`	numeric vector of length equal to displayed libs. Relative sizes of profiles displayed
`annotation_proportions`	numeric vector of length 3 (seq displayed), or 2 (seq not displayed). Relative sizes of annotation tracks.
`width`	numeric, default NULL. Width of plot.
`height`	numeric, default NULL. Height of plot.
`plot_name`	= character, default "default" (will create name from display_range name). Alternative: custom name for region.
`plot_title`	character, default NULL. A title for plot.
`display_sequence`	character/logical, default `c("both","nt", "aa", "none")[1]`. If TRUE or "both", display nucleotide and aa sequence in plot.
`seq_render_dist`	integer, default 100. The sequences will appear after zooming below this threshold.
`aa_letter_code`	character, when set to "three_letters", three letter amino acid code is used. One letter by default.
`annotation_names`	character, default NULL. Alternative naming for annotation.
`start_codons`	character vector, default "ATG"
`stop_codons`	character vector, default c("TAA", "TAG", "TGA")
`custom_motif`	character vector, default NULL.
`BPPARAM`	how many cores/threads to use? default: `BiocParallel::SerialParam()`. To see number of threads used for multicores, do `BiocParallel::bpparam()$workers`. You can also add a time remaining bar, for a more detailed pipeline.

Value

the plot object

Examples

library(RiboCrypt)
df <- ORFik.template.experiment()[9:10,]
cds <- loadRegion(df, "cds")
mrna <- loadRegion(df, "mrna")
# multiOmicsPlot_animate(mrna[1], annotation = cds[1], reference_sequence = findFa(df),
#                     frames_type = "columns", leader_extension = 30, trailer_extension = 30,
#                     reads = outputLibs(df, type = "pshifted", output.mode = "envirlist",
#                                   naming = "full", BPPARAM = BiocParallel::SerialParam()))
library(RiboCrypt)
df <- ORFik.template.experiment()[9:10,]
cds <- loadRegion(df, "cds")
mrna <- loadRegion(df, "mrna")
# multiOmicsPlot_animate(mrna[1], annotation = cds[1], reference_sequence = findFa(df),
#                     frames_type = "columns", leader_extension = 30, trailer_extension = 30,
#                     reads = outputLibs(df, type = "pshifted", output.mode = "envirlist",
#                                   naming = "full", BPPARAM = BiocParallel::SerialParam()))

Multi-omics plot using list input

Description

Customizable html plots for visualizing genomic data.

Usage

multiOmicsPlot_list(
  display_range,
  annotation = display_range,
  reference_sequence,
  reads,
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = NULL,
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = NULL,
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  AA_code = Biostrings::GENETIC_CODE,
  BPPARAM = BiocParallel::SerialParam(),
  summary_track = FALSE,
  summary_track_type = frames_type,
  export.format = "svg"
)
multiOmicsPlot_list(
  display_range,
  annotation = display_range,
  reference_sequence,
  reads,
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = NULL,
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = NULL,
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  AA_code = Biostrings::GENETIC_CODE,
  BPPARAM = BiocParallel::SerialParam(),
  summary_track = FALSE,
  summary_track_type = frames_type,
  export.format = "svg"
)

Arguments

`display_range`	the whole region to visualize, a `GRangesList` or `GRanges` object
`annotation`	the whole annotation which your target region is a subset, a `GRangesList` or `GRanges` object
`reference_sequence`	the genome reference, a `FaFile` or `FaFile` convertible object
`reads`	the NGS libraries, as a list of `GRanges` with or without score column for replicates.
`viewMode`	character, default "tx" (transcript coordinates, first position is 1, exons are merged into a single sequence) Alternative: "genomic" (genomic coordinates, first position is first position in `display_range` argument. Introns are displayed).
`custom_regions`	a GRangesList or NULL, default: NULL. The alternative annotation, like self defined uORFs etc. The vertical annotation bars will have a different color.
`leader_extension`	integer, default 0. (How much to extend view upstream)
`trailer_extension`	integer, default 0. (How much to extend view downstream)
`withFrames`	a logical vector, default NULL. Alternative: a length 1 or same length as list length of "reads" argument.
`frames_type`	character, default "lines". Alternative: - columns - stacks - area
`colors`	character, default NULL (automatic colouring). If "withFrames" argument is TRUE, colors are set to to c("red", "green", "blue") for the 3 frames. Alternative: Character vector of length 1 or length of "reads" list argument.
`kmers`	numeric (integer), bin positions into kmers.
`kmers_type`	character, function used for kmers sliding window. default: "mean", alternative: "sum"
`ylabels`	character, default NULL. Name of libraries in "reads" list arugment.
`lib_to_annotation_proportions`	numeric vector of length 2. relative sizes of profiles and annotation.
`lib_proportions`	numeric vector of length equal to displayed libs. Relative sizes of profiles displayed
`annotation_proportions`	numeric vector of length 3 (seq displayed), or 2 (seq not displayed). Relative sizes of annotation tracks.
`width`	numeric, default NULL. Width of plot.
`height`	numeric, default NULL. Height of plot.
`plot_name`	= character, default "default" (will create name from display_range name). Alternative: custom name for region.
`plot_title`	character, default NULL. A title for plot.
`display_sequence`	character/logical, default `c("both","nt", "aa", "none")[1]`. If TRUE or "both", display nucleotide and aa sequence in plot.
`seq_render_dist`	integer, default 100. The sequences will appear after zooming below this threshold.
`aa_letter_code`	character, when set to "three_letters", three letter amino acid code is used. One letter by default.
`annotation_names`	character, default NULL. Alternative naming for annotation.
`start_codons`	character vector, default "ATG"
`stop_codons`	character vector, default c("TAA", "TAG", "TGA")
`custom_motif`	character vector, default NULL.
`AA_code`	Genetic code for amino acid display. Default is SGC0 (standard: Vertebrate). See `Biostrings::GENETIC_CODE_TABLE` for options. To change to bacterial, do: `Biostrings::getGeneticCode("11")`
`BPPARAM`	how many cores/threads to use? default: `BiocParallel::SerialParam()`. To see number of threads used for multicores, do `BiocParallel::bpparam()$workers`. You can also add a time remaining bar, for a more detailed pipeline.
`summary_track`	logical, default FALSE. Display a top track, that is the sum of all tracks.
`summary_track_type`	character, default is same as 'frames_type' argument
`export.format`	character, default: "svg". alternative: "png". when you click the top right image button export, what should it export as?

Value

the plot object

Examples

library(RiboCrypt)
df <- ORFik.template.experiment()[9:10,]
cds <- loadRegion(df, "cds")
mrna <- loadRegion(df, "mrna")
multiOmicsPlot_list(mrna[1], annotation = cds[1], reference_sequence = findFa(df),
                    frames_type = "columns", leader_extension = 30, trailer_extension = 30,
                    reads = outputLibs(df, type = "pshifted", output.mode = "envirlist",
                                  naming = "full", BPPARAM = BiocParallel::SerialParam()))
library(RiboCrypt)
df <- ORFik.template.experiment()[9:10,]
cds <- loadRegion(df, "cds")
mrna <- loadRegion(df, "mrna")
multiOmicsPlot_list(mrna[1], annotation = cds[1], reference_sequence = findFa(df),
                    frames_type = "columns", leader_extension = 30, trailer_extension = 30,
                    reads = outputLibs(df, type = "pshifted", output.mode = "envirlist",
                                  naming = "full", BPPARAM = BiocParallel::SerialParam()))

Multi-omics plot using ORFik experiment input

Description

Customizable html plots for visualizing genomic data.

Usage

multiOmicsPlot_ORFikExp(
  display_range,
  df,
  annotation = "cds",
  reference_sequence = findFa(df),
  reads = outputLibs(df, type = "pshifted", output.mode = "envirlist", naming = "full",
    BPPARAM = BiocParallel::SerialParam()),
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = libraryTypes(df, uniqueTypes = FALSE) %in% c("RFP", "RPF", "LSU"),
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = bamVarName(df),
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  BPPARAM = BiocParallel::SerialParam(),
  input_id = "",
  summary_track = FALSE,
  summary_track_type = frames_type,
  export.format = "svg"
)
multiOmicsPlot_ORFikExp(
  display_range,
  df,
  annotation = "cds",
  reference_sequence = findFa(df),
  reads = outputLibs(df, type = "pshifted", output.mode = "envirlist", naming = "full",
    BPPARAM = BiocParallel::SerialParam()),
  viewMode = c("tx", "genomic")[1],
  custom_regions = NULL,
  leader_extension = 0,
  trailer_extension = 0,
  withFrames = libraryTypes(df, uniqueTypes = FALSE) %in% c("RFP", "RPF", "LSU"),
  frames_type = "lines",
  colors = NULL,
  kmers = NULL,
  kmers_type = c("mean", "sum")[1],
  ylabels = bamVarName(df),
  lib_to_annotation_proportions = c(0.8, 0.2),
  lib_proportions = NULL,
  annotation_proportions = NULL,
  width = NULL,
  height = NULL,
  plot_name = "default",
  plot_title = NULL,
  display_sequence = c("both", "nt", "aa", "none")[1],
  seq_render_dist = 100,
  aa_letter_code = c("one_letter", "three_letters")[1],
  annotation_names = NULL,
  start_codons = "ATG",
  stop_codons = c("TAA", "TAG", "TGA"),
  custom_motif = NULL,
  BPPARAM = BiocParallel::SerialParam(),
  input_id = "",
  summary_track = FALSE,
  summary_track_type = frames_type,
  export.format = "svg"
)

Arguments

`display_range`	the whole region to visualize, a `GRangesList` or `GRanges` object
`df`	an ORFik `experiment` or a list containing ORFik experiments. Usually a list when you have split Ribo-seq and RNA-seq etc.
`annotation`	the whole annotation which your target region is a subset, a `GRangesList` or `GRanges` object
`reference_sequence`	the genome reference, default ORFik::findFa(df)
`reads`	the NGS libraries, as a list of `GRanges` with or without 'score' column for replicates. Can also be a covRle object of precomputed coverage. Default: `outputLibs(df, type = "pshifted", output.mode = "envirlist", naming = "full", BPPARAM = BiocParallel::SerialParam())`
`viewMode`	character, default "tx" (transcript coordinates, first position is 1, exons are merged into a single sequence) Alternative: "genomic" (genomic coordinates, first position is first position in `display_range` argument. Introns are displayed).
`custom_regions`	a GRangesList or NULL, default: NULL. The alternative annotation, like self defined uORFs etc. The vertical annotation bars will have a different color.
`leader_extension`	integer, default 0. (How much to extend view upstream)
`trailer_extension`	integer, default 0. (How much to extend view downstream)
`withFrames`	a logical vector, default `libraryTypes(df, uniqueTypes = FALSE) %in% c("RFP", "RPF", "LSU")` Alternative: a length 1 or same length as list length of "reads" argument.
`frames_type`	character, default "lines". Alternative: - columns - stacks - area
`colors`	character, default NULL (automatic colouring). If "withFrames" argument is TRUE, colors are set to to c("red", "green", "blue") for the 3 frames. Alternative: Character vector of length 1 or length of "reads" list argument.
`kmers`	numeric (integer), bin positions into kmers.
`kmers_type`	character, function used for kmers sliding window. default: "mean", alternative: "sum"
`ylabels`	character, default `bamVarName(df)`. Name of libraries in "reads" list argument.
`lib_to_annotation_proportions`	numeric vector of length 2. relative sizes of profiles and annotation.
`lib_proportions`	numeric vector of length equal to displayed libs. Relative sizes of profiles displayed
`annotation_proportions`	numeric vector of length 3 (seq displayed), or 2 (seq not displayed). Relative sizes of annotation tracks.
`width`	numeric, default NULL. Width of plot.
`height`	numeric, default NULL. Height of plot.
`plot_name`	character, default "default" (will create name from display_range name).
`plot_title`	character, default NULL. A title for plot.
`display_sequence`	character/logical, default `c("both","nt", "aa", "none")[1]`. If TRUE or "both", display nucleotide and aa sequence in plot.
`seq_render_dist`	integer, default 100. The sequences will appear after zooming below this threshold.
`aa_letter_code`	character, when set to "three_letters", three letter amino acid code is used. One letter by default.
`annotation_names`	character, default NULL. Alternative naming for annotation.
`start_codons`	character vector, default "ATG"
`stop_codons`	character vector, default c("TAA", "TAG", "TGA")
`custom_motif`	character vector, default NULL.
`BPPARAM`	how many cores/threads to use? default: `BiocParallel::SerialParam()`. To see number of threads used for multicores, do `BiocParallel::bpparam()$workers`. You can also add a time remaining bar, for a more detailed pipeline.
`input_id`	character path, default: "", id for shiny to disply structures, should be "" for local users.
`summary_track`	logical, default FALSE. Display a top track, that is the sum of all tracks.
`summary_track_type`	character, default is same as 'frames_type' argument
`export.format`	character, default: "svg". alternative: "png". when you click the top right image button export, what should it export as?

Value

the plot object

Examples

library(RiboCrypt)
df <- ORFik.template.experiment()[9,] #Use third library in experiment only
cds <- loadRegion(df, "cds")
multiOmicsPlot_ORFikExp(extendLeaders(extendTrailers(cds[1], 30), 30), df = df,
                        frames_type = "columns")
library(RiboCrypt)
df <- ORFik.template.experiment()[9,] #Use third library in experiment only
cds <- loadRegion(df, "cds")
multiOmicsPlot_ORFikExp(extendLeaders(extendTrailers(cds[1], 30), 30), df = df,
                        frames_type = "columns")

Select box for organism

Description

Select box for organism

Usage

organism_input_select(genomes, ns)
organism_input_select(genomes, ns)

Arguments

`genomes`	name of genomes, returned from list.experiments()
`ns`	the ID, for shiny session

Value

selectizeInput object

Create RiboCrypt app

Description

Create RiboCrypt app

Usage

RiboCrypt_app(
  validate.experiments = TRUE,
  options = list(launch.browser = ifelse(interactive(), TRUE, FALSE)),
  all_exp = list.experiments(validate = validate.experiments),
  browser_options = c(),
  init_tab_focus = "browser"
)
RiboCrypt_app(
  validate.experiments = TRUE,
  options = list(launch.browser = ifelse(interactive(), TRUE, FALSE)),
  all_exp = list.experiments(validate = validate.experiments),
  browser_options = c(),
  init_tab_focus = "browser"
)

Arguments

`validate.experiments`	logical, default TRUE, set to FALSE to allow starting the app with malformed experiments, be careful will crash if you try to load that experiment!
`options`	list of arguments, default `list("launch.browser" = ifelse(interactive(), TRUE, FALSE))`
`all_exp`	a data.table, default: `list.experiments(validate = validate.experiments)`. Which experiments do you want to allow your app to see, default is all in your system config path.
`browser_options`	named character vector of browser specific arguments: - default_experiment : Which experiment to select, default: first one - default_gene : Which genes to select, default: first one - default_libs : Which libraries to select: first one, else a single string, where libs are seperated by "\|", like "RFP_WT_r1\|RFP_WT_r2". - default_kmer : K-mer windowing size, default: 1 - default_frame_type : Ribo-seq line type, default: "lines" - plot_on_start : Plot when starting, default: "FALSE"
`init_tab_focus`	character, default "browser". Which tab to open on init.

Value

RiboCrypt shiny app

Examples

## Default run
# RiboCrypt_app()
## Plot on start
# RiboCrypt_app(browser_options = c(plot_on_start = "TRUE"))
## Init with an experiment and gene (you must of course have the experiment)

#RiboCrypt_app(validate.experiments = FALSE,
#       browser_options = c(plot_on_start = "TRUE",
#                           default_experiment = "human_all_merged_l50",
#                           default_gene = "ATF4-ENSG00000128272"))
## Default run
# RiboCrypt_app()
## Plot on start
# RiboCrypt_app(browser_options = c(plot_on_start = "TRUE"))
## Init with an experiment and gene (you must of course have the experiment)

#RiboCrypt_app(validate.experiments = FALSE,
#       browser_options = c(plot_on_start = "TRUE",
#                           default_experiment = "human_all_merged_l50",
#                           default_gene = "ATF4-ENSG00000128272"))

Package 'RiboCrypt'

Help Index

Differential expression plots (1D or 2D)

Description

Usage

Arguments

Value

Examples

Distance to following range

Description

Usage

Arguments

Value

Fetch Javascript sequence

Description

Usage

Arguments

Value

Fetch summary of uniprot id

Description

Usage

Arguments

Value

Multi-omics animation using list input

Description

Usage

Arguments

Value

Examples

Multi-omics plot using list input

Description

Usage

Arguments

Value

Examples

Multi-omics plot using ORFik experiment input

Description

Usage

Arguments

Value

Examples

Select box for organism

Description

Usage

Arguments

Value

Create RiboCrypt app

Description

Usage

Arguments

Value

Examples