| Title: | Create Copy-Number Plots using karyoploteR functionality |
|---|---|
| Description: | CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. |
| Authors: | Bernat Gel <[email protected]> and Miriam Magallon <[email protected]> |
| Maintainer: | Bernat Gel <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.23.0 |
| Built: | 2024-11-29 05:30:56 UTC |
| Source: | https://github.com/bioc/CopyNumberPlots |
Assign a color to each data element according to the copy number status of the its genomic location
colByCopyNumber(data, cn.calls, cn.column="cn", cn.colors=NULL)colByCopyNumber(data, cn.calls, cn.column="cn", cn.colors=NULL)
data |
The data elements. Can be a GRanges or anything accepted by |
cn.calls |
The CN calls to use to select the color for the data elements |
cn.column |
The name of the column with the copy number information. (defaults to "cn") |
cn.colors |
The specification of the colors. Internally, it used to call the function getCopyNumberColors. Detailed documentation on color specification can be found there. (defaults to NULL) |
Given a set of data elements positioned on the genome (either a GRanges
or any other format accepted by regioneR's toGRanges)
and a set of copy number calls, assign a color to each element depending on
the copy number status of the genomic region where it's located. The colors
can be customized using the cn.colors parameter as documented in
getCopyNumberColors.
Return a vector of colors with the same length as the number of elements in data
toGRanges, getCopyNumberColors, plotBAF
pos <- sort(floor(runif(1000, 1, 10000))) baf.data <- toGRanges("chr1", pos, pos) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) breakpoint <- start(baf.data)[400] cn.calls <- toGRanges(data.frame(chr=c("chr1", "chr1"), start=c(1, breakpoint+1), end=c(breakpoint, 10000), cn=c(3,2))) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data, points.col = colByCopyNumber(baf.data, cn.calls))pos <- sort(floor(runif(1000, 1, 10000))) baf.data <- toGRanges("chr1", pos, pos) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) breakpoint <- start(baf.data)[400] cn.calls <- toGRanges(data.frame(chr=c("chr1", "chr1"), start=c(1, breakpoint+1), end=c(breakpoint, 10000), cn=c(3,2))) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data, points.col = colByCopyNumber(baf.data, cn.calls))
Given the merge matrix of an hclust object, compute a valid ordering of the samples so a dendogram can be plotted with no line crossings.
Note: It's been implemented from scratch but in our experience reproduces exactly the ordering produced by R own ordering algorithm used by hclust.
computeHclustPlotOrder(m)computeHclustPlotOrder(m)
m |
(matrix) A merge matrix with 2 columns representing the merges bewteen elements and clusters. The format is described in the documentation of hclust. |
A vector with the ordering of the nodes.
ztree <- matrix(c(0,3,0.1,2, 1,4,0.2,2, 5,2,0.3,2, 6,7,0.4,2), nrow=4) hc.tree <- Ztree2Hclust(ztree) plot(hc.tree) #Set the ordering to a non-correct ordering hc.tree$order <- c(1:5) #When we plot, we see line crossings plot(hc.tree) #Compute a correct ordering and replot element.order <- computeHclustPlotOrder(hc.tree$merge) hc.tree$order <- element.order plot(hc.tree)ztree <- matrix(c(0,3,0.1,2, 1,4,0.2,2, 5,2,0.3,2, 6,7,0.4,2), nrow=4) hc.tree <- Ztree2Hclust(ztree) plot(hc.tree) #Set the ordering to a non-correct ordering hc.tree$order <- c(1:5) #When we plot, we see line crossings plot(hc.tree) #Compute a correct ordering and replot element.order <- computeHclustPlotOrder(hc.tree$merge) hc.tree$order <- element.order plot(hc.tree)
Set the style of the chromosome names to Ensembl ("chr1" instead of "1")
EnsemblStyle(x)EnsemblStyle(x)
x |
(GRanges, GRangesList or list of GRanges) The object to transform to Ensembl style |
The same x object with the styles of the seqlevels set to Ensembl
#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- EnsemblStyle(seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- EnsemblStyle(seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- EnsemblStyle(seg.data)#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- EnsemblStyle(seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- EnsemblStyle(seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- EnsemblStyle(seg.data)
Identify the column in a data.frame or equivalent with the B-allele frecuency information
getBAFColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getBAFColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the bi-allelic frecuency information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getBAFColumn(df = df) col.num <- getBAFColumn(df = df, col = "BAF")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getBAFColumn(df = df) col.num <- getBAFColumn(df = df, col = "BAF")
Identify the column in a data.frame or equivalent with the chromosome information
getChrColumn(df, col=NULL, avoid.pattern = NULL, needed=TRUE, verbose = TRUE)getChrColumn(df, col=NULL, avoid.pattern = NULL, needed=TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the chromosome information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getChrColumn(df = df) col.num <- getChrColumn(df = df, col = "chromosome")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getChrColumn(df = df) col.num <- getChrColumn(df = df, col = "chromosome")
Use simple pattern matching to try to identify a column in a data.frame or equivalent by its name
getColumn(df, col=NULL, pattern="", avoid.pattern = NULL, msg.col.name="", needed=TRUE, verbose = TRUE)getColumn(df, col=NULL, pattern="", avoid.pattern = NULL, msg.col.name="", needed=TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
pattern |
(character) The pattern to match the column name. If more than one column matches the pattern, the first one (leftmost) will be returned. The pattern may be any valid regular expression. (defaults to "") |
avoid.pattern |
(character) The pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
msg.col.name |
(character) Only used in the error message to make the message clearer. The name of the column we are searching for. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will use pattern matching to try to identify which column of
a data.frame contains a certain data. It will return its number. If
col is specified it will not use pattern matching but identify
if a column with that exact name exists and return its position.
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234", "endogenous" = FALSE, "chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getColumn(df = df, pattern = "Chr|chr", msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, col = "chromosome", msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, col = 3, msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, pattern = "end", msg.col.name = "End", needed = TRUE) col.num <- getColumn(df = df, pattern = "end", avoid.pattern = "endogenous", msg.col.name = "End", needed = TRUE)df <- data.frame("id"= "rs1234", "endogenous" = FALSE, "chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getColumn(df = df, pattern = "Chr|chr", msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, col = "chromosome", msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, col = 3, msg.col.name = "Chromosome", needed = TRUE) col.num <- getColumn(df = df, pattern = "end", msg.col.name = "End", needed = TRUE) col.num <- getColumn(df = df, pattern = "end", avoid.pattern = "endogenous", msg.col.name = "End", needed = TRUE)
Get the colors representing the copy number levels
getCopyNumberColors(colors=NULL)getCopyNumberColors(colors=NULL)
colors |
(character) Either a palette name (i.e. "green_orange_red") or a vector of colors. If NULL the default palette ("green_orange_red") will be returned. (defaults to NULL) |
This function returns a vector of colors with names from 0 to the length of the vector. The colors will be used to represent copy numbers from 0 (homozygous deletion) to the copy number equal to the length of the vector (typically 7 or 8). The function has a single parameter 'colors', which can be either the name of one of the available palettes or the a vector of valid color specifications (i.e. c("red", "#FFAAAA", "gray", "#AAAAFF", "#5555FF", "#0000FF")). If 'colors' is a palette name, the palette will be returned. If color is a color vector, it will be returned with the names set as needed.
Available palettes are: "gree_orange_red" and "red_blue"
A vector of colors with the names set to the positions in the vector.
getCopyNumberColors() getCopyNumberColors("red_blue") getCopyNumberColors(c("red", "#FFAAAA", "gray", "#AAAAFF", "#5555FF", "#0000FF"))getCopyNumberColors() getCopyNumberColors("red_blue") getCopyNumberColors(c("red", "#FFAAAA", "gray", "#AAAAFF", "#5555FF", "#0000FF"))
Identify the column in a data.frame or equivalent with the copy number information
getCopyNumberColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getCopyNumberColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the copy number information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getCopyNumberColumn(df = df) col.num <- getCopyNumberColumn(df = df, col = "copy.number.level")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getCopyNumberColumn(df = df) col.num <- getCopyNumberColumn(df = df, col = "copy.number.level")
Identify the column in a data.frame or equivalent with the end position information
getEndColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getEndColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the position information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getEndColumn(df = df) col.num <- getEndColumn(df = df, col = "end.position")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getEndColumn(df = df) col.num <- getEndColumn(df = df, col = "end.position")
Identify the column in a data.frame or equivalent with the ID information
getIDColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getIDColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains ID information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getIDColumn(df = df) col.num <- getIDColumn(df = df, col = "id")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getIDColumn(df = df) col.num <- getIDColumn(df = df, col = "id")
Identify the column in a data.frame or equivalent with LOH information
getLOHColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getLOHColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the LOH information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getLOHColumn(df = df) col.num <- getLOHColumn(df = df, col = "LOH")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getLOHColumn(df = df) col.num <- getLOHColumn(df = df, col = "LOH")
Identify the column in a data.frame or equivalent with the Log Ratio information
getLRRColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getLRRColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the Log Ratio information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getLRRColumn(df = df) col.num <- getLRRColumn(df = df, col = "Log.Ratio")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getLRRColumn(df = df) col.num <- getLRRColumn(df = df, col = "Log.Ratio")
Identify the column in a data.frame or equivalent with the position information
getPosColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getPosColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the position information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getPosColumn(df = df) col.num <- getPosColumn(df = df, col = "strange.name")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getPosColumn(df = df) col.num <- getPosColumn(df = df, col = "strange.name")
Identify the column in a data.frame or equivalent with the position of the segment value information.
getSegmentValueColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getSegmentValueColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame or equivalent that contains the segment information and return its position.
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getSegmentValueColumn(df = df) col.num <- getSegmentValueColumn(df = df, col = "median.value.per.segment")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getSegmentValueColumn(df = df) col.num <- getSegmentValueColumn(df = df, col = "median.value.per.segment")
Identify the column in a data.frame with the start position information
getStartColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)getStartColumn(df, col = NULL, avoid.pattern = NULL, needed = TRUE, verbose = TRUE)
df |
(data.frame or equivalent) The object were columns are searched. It must be either a data.frame or an object where names(df) works. |
col |
(number or character) The column to identify. If NULL an heuristic will be used to automatically identify the column. It can also a number or a character to define the exact column to return.(defaults to NULL) |
avoid.pattern |
(character) An optional pattern to avoid on the column name. The pattern may be any valid regular expression. (defaults to "") |
needed |
(logical) Whether the column is needed or not. If TRUE, an error will be raised if the column is not found. (defaults to TRUE) |
verbose |
Whether to show information messages. (defaults to TRUE) |
Identify the column of a data.frame that contains the position information and return its position
The number of the column matching the specification or NULL if no column was found.
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getStartColumn(df = df ) col.num <- getStartColumn(df = df, col = "Start")df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") col.num <- getStartColumn(df = df ) col.num <- getStartColumn(df = df, col = "Start")
Loads copy number calls from a tabular format
loadCopyNumberCalls(cnv.data, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, loh.col = NULL, segment.value.col = NULL, genome = NULL, zero.based = FALSE, verbose = TRUE)loadCopyNumberCalls(cnv.data, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, loh.col = NULL, segment.value.col = NULL, genome = NULL, zero.based = FALSE, verbose = TRUE)
cnv.data |
Either the name of the file with the data or a variable containing the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to NULL) |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to NULL) |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
loh.col |
(number or character) The name or number of the column with LOH information. If NULL, it is automatically identified. (defaults to NULL) |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome (defaults to NULL) |
zero.based |
(logical) Whether the data is zero-based and half open (i.e. ranges are defined by (start:end] so chr1:10-20 represents nine bases long features spanning from base 11 to 20). (defaults to FALSE) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from any "bed-like" data structure
in R or file. Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment
df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") cnv.call <- loadCopyNumberCalls(cnv.data = df)df <- data.frame("id"= "rs1234","chromosome"="chr1", "Start"=0, "end.position"=100, "copy.number.level"=3, "LOH"=0, "median.value.per.segment"=1.2, "BAF"=0.2, "Log Ratio"=1.5, "strange.name"="strange.value") cnv.call <- loadCopyNumberCalls(cnv.data = df)
Loads copy number calls from either cn.mops result class or in a tabular format
loadCopyNumberCallsCnmops(cn.mops.res, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = "CN", segment.value.col = "median", genome = NULL, verbose = TRUE)loadCopyNumberCallsCnmops(cn.mops.res, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = "CN", segment.value.col = "median", genome = NULL, verbose = TRUE)
cn.mops.res |
The name of the file with the data or the name of the variable with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to NULL) |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to NULL) |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to "CN") |
segment.value.col |
(number or character) The name or number of the column with segment value. It can be mean or median. If NULL, it is automatically identified. (defaults to "median") |
genome |
(character) The name of the genome (defaults to NULL) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from either cn.mops result class or
in a tabular format from a file. Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
Column names of the format loaded are specified as default but,
if no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment or a list of GRanges with a GRanges per sample.
# loadCopyNumberCallsCnmops from cn.mops result class: #NOT RUN - Example loading data from a results object created by cn.mops # require(cn.mops) # data(cn.mops, package = "cn.mops") # cn.mops.res <- cn.mops(XRanges) # cn.mops.res <- calcIntegerCopyNumbers(cn.mops.res) # cnv.call <- loadCopyNumberCallsCnmops(cn.mops.res = cn.mops.res) # loadCopyNumberCallsCnmops from a file where cn.mops result was saved: cn.mops.res <- system.file("extdata", "cn.mops.segmentation.csv", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCnmops(cn.mops.res = cn.mops.res)# loadCopyNumberCallsCnmops from cn.mops result class: #NOT RUN - Example loading data from a results object created by cn.mops # require(cn.mops) # data(cn.mops, package = "cn.mops") # cn.mops.res <- cn.mops(XRanges) # cn.mops.res <- calcIntegerCopyNumbers(cn.mops.res) # cnv.call <- loadCopyNumberCallsCnmops(cn.mops.res = cn.mops.res) # loadCopyNumberCallsCnmops from a file where cn.mops result was saved: cn.mops.res <- system.file("extdata", "cn.mops.segmentation.csv", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCnmops(cn.mops.res = cn.mops.res)
Loads copy number calls from CNVkit.cns file format
loadCopyNumberCallsCNVkit(cnvkit.file, chr.col = "chromosome", start.col = "start", end.col = "end", segment.value.col = "log2", file.type = "auto", cn.col = NULL, zero.based = TRUE, genome = NULL, verbose = TRUE)loadCopyNumberCallsCNVkit(cnvkit.file, chr.col = "chromosome", start.col = "start", end.col = "end", segment.value.col = "log2", file.type = "auto", cn.col = NULL, zero.based = TRUE, genome = NULL, verbose = TRUE)
cnvkit.file |
The name of the file with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to "chromosome") |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to "start") |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to "end") |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to "log2") |
file.type |
(character) wheter to load ".cns", ".cnr" or "auto" if the file type is automatically recognised. (defaults to "auto) |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
zero.based |
(logical) Whether the data is zero-based and half open (i.e. ranges are defined by (start:end] so chr1:10-20 represents nine bases long features spanning from base 11 to 20). (defaults to FALSE) |
genome |
(character) The name of the genome (defaults to NULL) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from CNVkit.cns file format and from CNVkit.cnr.
Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment.
## loadCopyNumberCallsCNVkit from .cns file format: ## An example of .cns file format is found at https://github.com/etal/cnvkit/blob/master/test/formats/cl_seq.cns. # Loading a CNVkit.cns file cnvkit.file <- system.file("extdata", "CNVkit_output.cns", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCNVkit(cnvkit.file) # Loading a CNVkit.cnr file cnvkit.file <- system.file("extdata", "CNVkit_output.cnr", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCNVkit(cnvkit.file)## loadCopyNumberCallsCNVkit from .cns file format: ## An example of .cns file format is found at https://github.com/etal/cnvkit/blob/master/test/formats/cl_seq.cns. # Loading a CNVkit.cns file cnvkit.file <- system.file("extdata", "CNVkit_output.cns", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCNVkit(cnvkit.file) # Loading a CNVkit.cnr file cnvkit.file <- system.file("extdata", "CNVkit_output.cnr", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsCNVkit(cnvkit.file)
Loads copy number calls from DECoN output file
loadCopyNumberCallsDECoN(decon.file, chr.col = NULL, start.col = "Start", end.col = "End", cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose = TRUE)loadCopyNumberCallsDECoN(decon.file, chr.col = NULL, start.col = "Start", end.col = "End", cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose = TRUE)
decon.file |
The name of the file with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to "Start") |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to "End") |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NA) |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome. (defaults to NULL) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from DECoN output file.
Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment or a list of GRanges with a GRanges per sample.
decon.file <- system.file("extdata", "DECoN_output.txt", package = "CopyNumberPlots", mustWork = TRUE) cn.calls <- loadCopyNumberCallsDECoN(decon.file = decon.file)decon.file <- system.file("extdata", "DECoN_output.txt", package = "CopyNumberPlots", mustWork = TRUE) cn.calls <- loadCopyNumberCallsDECoN(decon.file = decon.file)
Loads copy number calls from DNACopy results.
loadCopyNumberCallsDNAcopy(DNAcopy.data, chr.col = "chrom", start.col = "loc.start", end.col = "loc.end", segment.value.col = "seg.mean", cn.col = NULL, chr.transformation = "23:X,24:Y,25:MT", genome = NULL, verbose = TRUE)loadCopyNumberCallsDNAcopy(DNAcopy.data, chr.col = "chrom", start.col = "loc.start", end.col = "loc.end", segment.value.col = "seg.mean", cn.col = NULL, chr.transformation = "23:X,24:Y,25:MT", genome = NULL, verbose = TRUE)
DNAcopy.data |
The name of the file with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to "chrom") |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to "loc.start") |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to "loc.end") |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to "seg.mean") |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
chr.transformation |
(character) The transformation of the chromosome names in a comma separated "key:value" format.(defaults to "23:X,24:Y,25:MT") |
genome |
(character) The name of the genome (defaults to NULL) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from DNAcopy data structure
in R. Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment or a list of GRanges with a GRanges per sample.
library(DNAcopy) data(coriell) CNA.object <- suppressWarnings(CNA(cbind(coriell$Coriell.05296), coriell$Chromosome, coriell$Position, data.type="logratio",sampleid="c05296")) smoothed.CNA.object <- smooth.CNA(CNA.object) DNAcopy.data <- segment(smoothed.CNA.object, verbose=1) cnv.call <- loadCopyNumberCallsDNAcopy(DNAcopy.data = DNAcopy.data) # more than 1 sample CNA.object <- CNA(genomdat = cbind(coriell$Coriell.05296, coriell$Coriell.13330), chrom = coriell$Chromosome, maploc = coriell$Position, data.type = "logratio", sampleid = c("c05296", "c13330")) smoothed.CNA.object <- smooth.CNA(CNA.object) DNAcopy.data <- segment(smoothed.CNA.object, verbose=1) cnv.call <- loadCopyNumberCallsDNAcopy(DNAcopy.data = DNAcopy.data)library(DNAcopy) data(coriell) CNA.object <- suppressWarnings(CNA(cbind(coriell$Coriell.05296), coriell$Chromosome, coriell$Position, data.type="logratio",sampleid="c05296")) smoothed.CNA.object <- smooth.CNA(CNA.object) DNAcopy.data <- segment(smoothed.CNA.object, verbose=1) cnv.call <- loadCopyNumberCallsDNAcopy(DNAcopy.data = DNAcopy.data) # more than 1 sample CNA.object <- CNA(genomdat = cbind(coriell$Coriell.05296, coriell$Coriell.13330), chrom = coriell$Chromosome, maploc = coriell$Position, data.type = "logratio", sampleid = c("c05296", "c13330")) smoothed.CNA.object <- smooth.CNA(CNA.object) DNAcopy.data <- segment(smoothed.CNA.object, verbose=1) cnv.call <- loadCopyNumberCallsDNAcopy(DNAcopy.data = DNAcopy.data)
Loads copy number calls from either cn.mops result class or in a tabular format
loadCopyNumberCallsPanelcnmops(panelcn.mops.res, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose = TRUE)loadCopyNumberCallsPanelcnmops(panelcn.mops.res, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose = TRUE)
panelcn.mops.res |
The name of the file with the data or the name of the variable with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to NULL) |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to NULL) |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome (defaults to NULL) |
verbose |
(character) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from panelcn.mops resulttable or
in a tabular format or file. Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment or a list of GRanges with a GRanges per sample.
## loadCopyNumberCallsPanelcnmops from panelcn.mops resulttable: library(panelcn.mops) data(panelcn.mops, package = "panelcn.mops") XandCB <- test sampleNames <- colnames(elementMetadata(XandCB)) elementMetadata(XandCB) <- cbind(elementMetadata(XandCB), elementMetadata(control)) resulttable <- createResultTable(resultlist = resultlist, XandCB = XandCB, countWindows = countWindows, sampleNames = sampleNames) panelcn.mops.res <- resulttable[[1]] cnv.call <- loadCopyNumberCallsPanelcnmops(panelcn.mops.res = panelcn.mops.res)## loadCopyNumberCallsPanelcnmops from panelcn.mops resulttable: library(panelcn.mops) data(panelcn.mops, package = "panelcn.mops") XandCB <- test sampleNames <- colnames(elementMetadata(XandCB)) elementMetadata(XandCB) <- cbind(elementMetadata(XandCB), elementMetadata(control)) resulttable <- createResultTable(resultlist = resultlist, XandCB = XandCB, countWindows = countWindows, sampleNames = sampleNames) panelcn.mops.res <- resulttable[[1]] cnv.call <- loadCopyNumberCallsPanelcnmops(panelcn.mops.res = panelcn.mops.res)
Loads copy number calls from pennCNV.rawcnv file format
loadCopyNumberCallspennCNV (pennCNV.file, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose=TRUE)loadCopyNumberCallspennCNV (pennCNV.file, chr.col = NULL, start.col = NULL, end.col = NULL, cn.col = NULL, segment.value.col = NULL, genome = NULL, verbose=TRUE)
pennCNV.file |
The name of the file with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to NULL) |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to NULL) |
cn.col |
(number or character) The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome (defaults to NULL) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load copy number calls from pennCNV.rawcnv file format.
Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment or a list of GRanges with a GRanges per sample.
## loadCopyNumberCallspennCNV from .rawcnv file format: ## The file to run the example can be found in: http://penncnv.openbioinformatics.org/en/latest/user-guide/test/ pennCNV.file <- system.file("extdata", "pennCNV.rawcnv", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallspennCNV(pennCNV.file = pennCNV.file)## loadCopyNumberCallspennCNV from .rawcnv file format: ## The file to run the example can be found in: http://penncnv.openbioinformatics.org/en/latest/user-guide/test/ pennCNV.file <- system.file("extdata", "pennCNV.rawcnv", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallspennCNV(pennCNV.file = pennCNV.file)
Loads copy number calls from .seg file format
loadCopyNumberCallsSeg (seg.file, chr.col = "chrom", start.col = "loc.start", end.col = "loc.end", segment.value.col = "seg.mean", cn.col = NULL, genome = NULL, chr.transformation = "23:X,24:Y,25:MT", verbose = TRUE)loadCopyNumberCallsSeg (seg.file, chr.col = "chrom", start.col = "loc.start", end.col = "loc.end", segment.value.col = "seg.mean", cn.col = NULL, genome = NULL, chr.transformation = "23:X,24:Y,25:MT", verbose = TRUE)
seg.file |
The name of the file with the data |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to "chrom") |
start.col |
(number or character) The name or number of the column with start position information. If NULL, it is automatically identified. (defaults to "loc.start") |
end.col |
(number or character) The name or number of the column with end position information. If NULL, it is automatically identified. (defaults to "loc.end") |
segment.value.col |
(number or character) The name or number of the column with segment value. If NULL, it is automatically identified. (defaults to "seg.mean") |
cn.col |
(number or character)The name or number of the column with CN information. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome (defaults to NULL) |
chr.transformation |
(character)(character) The transformation of the chromosome names in a comma separated "key:value" format as detailed at https://cnvkit.readthedocs.io/en/stable/importexport.html#import-seg.(defaults to "23:X,24:Y,25:MT") |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will load segments data from .seg file format.
Internally it uses the toGRanges function from regioneR
package and can work with any format accepted by it, including R objects
and local or remote files.
If no column names are specified, it will use simple heuristics to try to
identify the relevant data columns.
A GRanges with a range per copy number segment
## loadCopyNumberCallsSeg from .seg file format: ## the file to run in the example can be found in: ## https://software.broadinstitute.org/software/igv/SEG ## under example.seg file name. seg.file <- system.file("extdata", "DNACopy_output.seg", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsSeg(seg.file = seg.file)## loadCopyNumberCallsSeg from .seg file format: ## the file to run in the example can be found in: ## https://software.broadinstitute.org/software/igv/SEG ## under example.seg file name. seg.file <- system.file("extdata", "DNACopy_output.seg", package = "CopyNumberPlots", mustWork = TRUE) cnv.call <- loadCopyNumberCallsSeg(seg.file = seg.file)
Loads SNP array data in a tabular format
loadSNPData(snp.data, chr.col = NULL, start.col = NULL, end.col = NULL, pos.col = NULL, baf.col = NULL, lrr.col = NULL, id.col = NULL, genome = NULL, verbose = TRUE)loadSNPData(snp.data, chr.col = NULL, start.col = NULL, end.col = NULL, pos.col = NULL, baf.col = NULL, lrr.col = NULL, id.col = NULL, genome = NULL, verbose = TRUE)
snp.data |
Either the name of the file with the data or a variable containing the data. |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) the name or number of the column with start position |
end.col |
(number or character) the name of number of the column with end position |
pos.col |
(number or character) The name or number of the column with position information. If NULL, it is automatically identified. (defaults to NULL) |
baf.col |
(number or character) The name or number of the column with BAF information. If NULL, it is automatically identified. (defaults to NULL) |
lrr.col |
(number or character) The name or number of the column with LRR information. If NULL, it is automatically identified. (defaults to NULL) |
id.col |
(number or character) The name or number of the column with SNP identifier information. If NULL, it is automatically identified. (defaults to NULL) |
genome |
(character) The name of the genome (defaults to "hg19") |
verbose |
Wether information messages should be generated. (defaults to TRUE) |
Given a file name or data in a tabular format, the function loads SNP array data in a tabular format. It will try to identify the columns with the relevant information (chr, position, BAF, LRR, etc...) or will use the column number or name supplied by the user, if any. It will convert the tabular data into a GRanges, with one range per SNP in the table.
A GRanges with a range per SNP
# There are two examples of posible files to load. snp.data1 <- system.file("extdata", "snp.data_test.csv", package = "CopyNumberPlots", mustWork=TRUE) snps <- loadSNPData(snp.data = snp.data1) snp.data2 <- system.file("extdata", "snp.data_test2.csv", package = "CopyNumberPlots", mustWork=TRUE) snps <- loadSNPData(snp.data = snp.data2)# There are two examples of posible files to load. snp.data1 <- system.file("extdata", "snp.data_test.csv", package = "CopyNumberPlots", mustWork=TRUE) snps <- loadSNPData(snp.data = snp.data1) snp.data2 <- system.file("extdata", "snp.data_test2.csv", package = "CopyNumberPlots", mustWork=TRUE) snps <- loadSNPData(snp.data = snp.data2)
Loads SNP array data in a tabular format using data from DNAcopy rawdata or DNAcopy results
loadSNPDataDNAcopy (snp.data, chr.col = "chrom", start.col = NULL, end.col = NULL, pos.col = "maploc", lrr.col = NULL, chr.transformation = "23:X,24:Y,25:MT", genome = NULL, na.rm = FALSE, verbose = TRUE)loadSNPDataDNAcopy (snp.data, chr.col = "chrom", start.col = NULL, end.col = NULL, pos.col = "maploc", lrr.col = NULL, chr.transformation = "23:X,24:Y,25:MT", genome = NULL, na.rm = FALSE, verbose = TRUE)
snp.data |
Either the name of the file with the data or a variable containing the data. |
chr.col |
(number or character) The name or number of the column with chromosome information. If NULL, it is automatically identified. (defaults to NULL) |
start.col |
(number or character) the name or number of the column with start position |
end.col |
(number or character) the name of number of the column with end position |
pos.col |
(number or character) The name or number of the column with position information. If NULL, it is automatically identified. (defaults to NULL) |
lrr.col |
(number or character) The name or number of the column with LRR information. If NULL, it is automatically identified. (defaults to NULL) |
chr.transformation |
(character) The transformation of the chromosome names in a comma separated "key:value" format.(defaults to "23:X,24:Y,25:MT") |
genome |
(character) The name of the genome (defaults to "hg19") |
na.rm |
(logical) Whether to remove NA values. (defaults to FALSE) |
verbose |
Wether information messages should be generated. (defaults to TRUE) |
Given a DNAcopy rawdata or DNAcopy results object, the function load SNP array data in a tabular format. It will try to identify the columns with the relevant information (chr, position, BAF, LRR, etc...) or will use the column number or name supplied by the user, if any. It will convert the tabular data into a GRanges, with one range per SNP in the table.
A list of GRanges with a range per SNP and one GRanges per sample.
library(DNAcopy) data(coriell) #one sample CNA.object <- CNA(cbind(coriell$Coriell.05296), coriell$Chromosome, coriell$Position, data.type="logratio") CNA.object <- smooth.CNA(CNA.object) snps <- loadSNPDataDNAcopy(snp.data = CNA.object, na.rm = TRUE) # more than 1 sample CNA.object <- CNA(genomdat = cbind(coriell$Coriell.05296, coriell$Coriell.13330), chrom = coriell$Chromosome, maploc = coriell$Position, data.type = "logratio", sampleid = c("c05296", "c13330")) CNA.object <- smooth.CNA(CNA.object) snps <- loadSNPDataDNAcopy(snp.data = CNA.object, na.rm = TRUE) #If the data come from DNAcopy results DNAcopy.object <- segment(CNA.object, verbose=1) snps <- loadSNPDataDNAcopy(snp.data = DNAcopy.object)library(DNAcopy) data(coriell) #one sample CNA.object <- CNA(cbind(coriell$Coriell.05296), coriell$Chromosome, coriell$Position, data.type="logratio") CNA.object <- smooth.CNA(CNA.object) snps <- loadSNPDataDNAcopy(snp.data = CNA.object, na.rm = TRUE) # more than 1 sample CNA.object <- CNA(genomdat = cbind(coriell$Coriell.05296, coriell$Coriell.13330), chrom = coriell$Chromosome, maploc = coriell$Position, data.type = "logratio", sampleid = c("c05296", "c13330")) CNA.object <- smooth.CNA(CNA.object) snps <- loadSNPDataDNAcopy(snp.data = CNA.object, na.rm = TRUE) #If the data come from DNAcopy results DNAcopy.object <- segment(CNA.object, verbose=1) snps <- loadSNPDataDNAcopy(snp.data = DNAcopy.object)
Load BAF-like data from a VCF file based on the variant allele frequency
loadSNPDataFromVCF(vcf.file, regions=NULL, genome="hg19", mirror.baf=TRUE, verbose=TRUE)loadSNPDataFromVCF(vcf.file, regions=NULL, genome="hg19", mirror.baf=TRUE, verbose=TRUE)
vcf.file |
The name of the VCF file with the data |
regions |
The regions to which we will limit the import (defaults to NULL) |
genome |
(a character)The name of the genome (defaults to "hg19") |
mirror.baf |
Flip the baf of about half the snps (the ones in odd positions in the genome) to achieve a mirror-like effect as in SNP arrays (defaults to TRUE) |
verbose |
Wether information messages should be generated. (defaults to TRUE) |
Given a VCF file the function will compute BAF-like values based on the variant allele frequency contained in it and return a GRanges object with a column named "baf".
A GRanges object with a range per variant and a column named baf
vcf.file <- system.file("extdata", "example.vcf.gz", package = "CopyNumberPlots", mustWork = TRUE) # These examples fail in windows. Commenting them out temporarily # snps <- loadSNPDataFromVCF(vcf.file) #kp <- plotKaryotype(plot.type = 4) #plotBAF(kp, snps = snps, labels = names(snps))vcf.file <- system.file("extdata", "example.vcf.gz", package = "CopyNumberPlots", mustWork = TRUE) # These examples fail in windows. Commenting them out temporarily # snps <- loadSNPDataFromVCF(vcf.file) #kp <- plotKaryotype(plot.type = 4) #plotBAF(kp, snps = snps, labels = names(snps))
Plot the B-allele frequency (BAF) data
plotBAF(karyoplot, snps, baf.column="baf", labels=NULL, points.cex=0.3, points.col="#333333", points.pch=16, label.cex=1.5, label.srt=90, label.margin=0.03, add.axis=TRUE, axis.cex=1.2, r0=0, r1=1, track.margin=0.1, data.panel=1, verbose=FALSE, ...)plotBAF(karyoplot, snps, baf.column="baf", labels=NULL, points.cex=0.3, points.col="#333333", points.pch=16, label.cex=1.5, label.srt=90, label.margin=0.03, add.axis=TRUE, axis.cex=1.2, r0=0, r1=1, track.margin=0.1, data.panel=1, verbose=FALSE, ...)
karyoplot |
(a KaryoPlot object) The object returned by the |
snps |
(a GRanges, a list of GRanges or a GRangesList) An object with the positions of the SNPs and a column with the BAF values. Other columns are ignored. If it's a list of GRanges with different samples, all samples will be plotted, splitting the total plot space between them. |
baf.column |
(a character) The name of the column in |
labels |
(a character) The text of the label to identify the data. If NA, no label will be plotted. If NULL, if snps is a single sample GRanges it will default to "BAF", if it's a list of samples it will default to the names in the list or consecutive numbers if names(snps) is NULL. (defaults to NULL) |
points.cex |
(numeric) The size of the points. (defaults to 0.3) |
points.col |
(a color) The color of the points (defaults to "#333333") |
points.pch |
(numeric) The shape of the points. (defaults to 16, a filled circle) |
label.cex |
(numeric) The size of the label (defaults to 1.5) |
label.srt |
(numeric) The rotation of the label (defaults to 90, vertical text) |
label.margin |
(numeric) The margin between the label and the origin of the plot in plot coordinates (the width of the plot is 1). (defaults to 0.03) |
add.axis |
(logical) Whether to add an axis (defaults to TRUE) |
axis.cex |
(numeric) The size of the axis labels. (defaults to 1.2) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
track.margin |
(numeric) If snps is a list object, this is the margin between the samples BAF. (deafults to 0.1) |
data.panel |
(numeric) (karyoploteR parameter) The identifier of the data panel where the data is to be plotted. The available data panels depend on the plot type selected in the call to |
verbose |
(logical) Wether messages with information on the processing should be generated (defaults to FALSE) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
This function plot the B-allele frequency (BAF) values on the
genome. BAF values represent the frequency of one of the alleles (NOT always
the minor allele) in the sample and usually come from SNP-array. However,
it's possible to get similar values from NGS variant calling data
(loadSNPDataFromVCF for example, will load a standard VCF and
generate BAF-like values).
The function plots the data points, an optional axis and an optional label
identifying the data. The function expects a GRanges object with the position
of the SNPs with a column (usually named 'baf') with the BAF values or any
object valid to toGRanges. This object can be created
with loadSNPData.
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions
pos <- sort(floor(runif(1000, 1, 10000))) baf.data <- toGRanges("chr1", pos, pos) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data) names(mcols(baf.data)) <- "values" kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data, baf.column="values", r0=0, r1=0.5, points.col="red", labels="Values") plotBAF(kp, baf.data, baf.column=1, r0=0.5, r1=1, points.col="orange", labels="First", label.cex=0.8, points.cex=1.4) #Plotting a list baf.data2 <- baf.data baf.data2$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) baf.list <- list(Tumor=baf.data, Normal=baf.data2) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.list, labels=NULL) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.list, labels=NULL, r1=0.8, label.cex=0.8, axis.cex=0.4) kpRect(kp, chr="chr1", x0=0, x1=pos[400], y0=0, y1=0.8, border="red") kpPlotNames(kp, chr="chr1", x0=0, x1=pos[400], y0=0, y1=0.8, labels = "Alteration", position = "top", col="red")pos <- sort(floor(runif(1000, 1, 10000))) baf.data <- toGRanges("chr1", pos, pos) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data) names(mcols(baf.data)) <- "values" kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.data, baf.column="values", r0=0, r1=0.5, points.col="red", labels="Values") plotBAF(kp, baf.data, baf.column=1, r0=0.5, r1=1, points.col="orange", labels="First", label.cex=0.8, points.cex=1.4) #Plotting a list baf.data2 <- baf.data baf.data2$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf <- rnorm(1000, mean = 0.5, sd = 0.05) baf.data$baf[1:400] <- baf.data$baf[1:400] + c(0.2, -0.2) baf.list <- list(Tumor=baf.data, Normal=baf.data2) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.list, labels=NULL) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotBAF(kp, baf.list, labels=NULL, r1=0.8, label.cex=0.8, axis.cex=0.4) kpRect(kp, chr="chr1", x0=0, x1=pos[400], y0=0, y1=0.8, border="red") kpPlotNames(kp, chr="chr1", x0=0, x1=pos[400], y0=0, y1=0.8, labels = "Alteration", position = "top", col="red")
Plot the segments representing the copy number calls by any algorithm
plotCopyNumberCalls(karyoplot, cn.calls, cn.values=NULL, cn.column="cn", cn.colors=NULL, loh.values=NULL, loh.column="loh", loh.color="#1E90FF", loh.height=0.3, labels=NULL, label.cex=1, label2.cex=NULL,track.margin=0.01, r0=0, r1=1, ...)plotCopyNumberCalls(karyoplot, cn.calls, cn.values=NULL, cn.column="cn", cn.colors=NULL, loh.values=NULL, loh.column="loh", loh.color="#1E90FF", loh.height=0.3, labels=NULL, label.cex=1, label2.cex=NULL,track.margin=0.01, r0=0, r1=1, ...)
karyoplot |
A karyoplote object |
cn.calls |
(a GRanges, a list of GRanges or a GRangesList) An object with the positions of the CN calls and a column with the CN values. Other columns are ignored. If it's a list of GRanges with different samples, all samples will be plotted, splitting the total plot space between them. |
cn.values |
(integer vector) The CN values. If NULL, they will be extracted from the cn.calls (defaults to NULL) |
cn.column |
(integer or character vector) The name or number of the column with CN information.(defaults to "cn") |
cn.colors |
(colors) The colors assigned to gains and losses (defaults to NULL) |
loh.values |
(logical vector) A logical vector that indicates whether there is or not LOH or a vector that can be coerced as logical. (defaults to NULL) |
loh.column |
(number or character) The name or number of the column with LRR information. (defaults to "loh") |
loh.color |
(a color) The color assigned to LOH values. (defaults to "#1E90FF") |
loh.height |
(numeric) The proportion of r0 and r1 of the vertical space over each chromosome dedicated to loh. It is dedicated the 30% of the vertical space by default.(defaults to 0.3) |
labels |
(character) The text of the label to identify the data. If NA, no label will be plotted. If NULL, if snps is a single sample GRanges it will default to "CN", if it's a list of samples it will default to the names in the list or consecutive numbers if names(snps) is NULL. (defaults to NULL) |
label.cex |
(numeric) The size of tthe label (defaults to 1) |
label2.cex |
(numeric) The size of the label 2. If NULL label2.cex will be lable.cex. (defaults to NULL) |
track.margin |
(numeric) If cn.calls is a list object, this is the margin between the samples CN. (deafults to 0.01) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 0) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 1) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
Plots the copy number calls as colored rectangles in the karyoplot. Each copy number status has its own color (including 2n, normal genome) and these colors may be specified by cn.colors. If LOH data is available it will be plotted below the copy number data, with a colored rectangle for every LOH region. The input is simply a GRanges object with an additional column containing the copy number status (as integers) and optionally another column containing the LOH status of each region.
If there's only one label, it will be used to label both Copy Number and LOH. If there are at least two labels, the first one is used to label the Copy Number and the second one to labvel the LOH.
If the function is called with a list of GRanges it plots every GRanges independently as different tracks one below the other. Track positioning is based on 'autotrack' and the margin between track is controlled by 'track.margin'.
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions
s1.calls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) s1.calls <- loadCopyNumberCalls(s1.calls.file) s2.calls.file <- system.file("extdata", "S2.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) s2.calls <- loadCopyNumberCalls(s2.calls.file) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, s1.calls) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, s1.calls, cn.colors="red_blue") #List of GRanges cn.calls <- list(s1=s1.calls, s2 =s2.calls) kp <-plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, cn.calls, cn.colors="red_blue")s1.calls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) s1.calls <- loadCopyNumberCalls(s1.calls.file) s2.calls.file <- system.file("extdata", "S2.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) s2.calls <- loadCopyNumberCalls(s2.calls.file) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, s1.calls) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, s1.calls, cn.colors="red_blue") #List of GRanges cn.calls <- list(s1=s1.calls, s2 =s2.calls) kp <-plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, cn.calls, cn.colors="red_blue")
Plot the segments representing the copy number calls by any algorithm
plotCopyNumberCallsAsLines(karyoplot, cn.calls, style="line", cn.column="cn", labels=NULL, label.cex=1, add.axis=TRUE, axis.cex=1, numticks=NULL, col="black", ymin=NULL, ymax=NULL, r0=0, r1=1, ...)plotCopyNumberCallsAsLines(karyoplot, cn.calls, style="line", cn.column="cn", labels=NULL, label.cex=1, add.axis=TRUE, axis.cex=1, numticks=NULL, col="black", ymin=NULL, ymax=NULL, r0=0, r1=1, ...)
karyoplot |
(a KaryoPlot object) The object returned by the |
cn.calls |
(a GRanges, a list of GRanges or a GRangesList) An object with the positions of the CN calls and a column with the CN values. Other columns are ignored. If it's a list of GRanges with different samples, all samples will be plotted, splitting the total plot space between them. |
style |
(character) The style in which the lines can be plot. It colud be as lines or segments. (defaults to "line") |
cn.column |
(integer or character vector) The name or number of the column with CN information.(defaults to "cn") |
labels |
(character) The text of the label to identify the data. If NA, no label will be plotted. If NULL, if snps is a single sample GRanges it will default to "Segments", if it's a list of samples it will default to the names in the list or consecutive numbers if names(snps) is NULL. (defaults to NULL) |
label.cex |
(numeric) The size of the label (defaults to 1) |
add.axis |
(logical) Whether to plot an axis (defaults to TRUE) |
axis.cex |
(numeric) The size of the axis labels.(defaults to 1) |
numticks |
The number of tocks in the axis (defaults to NULL) |
col |
(color) The color of the lines (defaults to "black") |
ymin |
(numeric) (karyoploteR parameter) The minimum value of y to be plotted. If NULL, it is set to the min value of the selected data panel. (defaults to -4) |
ymax |
(numeric) (karyoploteR parameter) (numeric) The maximum value of y to be plotted. If NULL, it is set to the max value of the selected data panel. (defaults to 2) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to 0) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to 1) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
Plots the segments
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions
cncalls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) cncalls <- loadCopyNumberCalls(cncalls.file) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls) kp <- plotKaryotype("hg19", chromosomes="chr3") #plotCopyNumberCalls(kp, cncalls, r0=0, r1=0.2) #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.25, r1=0.55) #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.65, r1=0.95, style="segments", col="red", ymin=0, ymax=4, numticks=3) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.2, r1=0.4, style="segments", add.axis=FALSE, labels="CopyNumber") #List of GRanges cncalls.list <- list(s1=cncalls, s2 =cncalls) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls.list,style="segments", add.axis=FALSE)cncalls.file <- system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE) cncalls <- loadCopyNumberCalls(cncalls.file) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls) kp <- plotKaryotype("hg19", chromosomes="chr3") #plotCopyNumberCalls(kp, cncalls, r0=0, r1=0.2) #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.25, r1=0.55) #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.65, r1=0.95, style="segments", col="red", ymin=0, ymax=4, numticks=3) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls, r0=0.2, r1=0.4, style="segments", add.axis=FALSE, labels="CopyNumber") #List of GRanges cncalls.list <- list(s1=cncalls, s2 =cncalls) kp <- plotKaryotype("hg19") #plotCopyNumberCallsAsLines(kp, cncalls.list,style="segments", add.axis=FALSE)
Plot a summary of copy number status over a number of samples using a histogram-like representation.
plotCopyNumberSummary(karyoplot, cn.calls, direction="in", gain.color=NULL, normal.color=NULL, loss.color=NULL, add.grid=FALSE, grid.color="white", labels=NULL, label.cex=1, label.srt=0, pos=2, r0=0, r1=1, ...)plotCopyNumberSummary(karyoplot, cn.calls, direction="in", gain.color=NULL, normal.color=NULL, loss.color=NULL, add.grid=FALSE, grid.color="white", labels=NULL, label.cex=1, label.srt=0, pos=2, r0=0, r1=1, ...)
karyoplot |
(a KaryoPlot object) The object returned by the |
cn.calls |
(a list of GRanges or a GRangesList) A list of GRanges or a GRangesList containing the GRanges objects with cn.column value |
direction |
The direction to which the coverage plot point, either "in" for inward or "out" for outward. (defaults to "in") |
gain.color |
(color) The color assigned to gains (defaults to NULL) |
normal.color |
(color) The color assigned to normal ploidy(defaults to NULL) |
loss.color |
(colors) The color assigned to losses(defaults to NULL) |
add.grid |
(logical) Whether to add lines as a grid in the plot (defaults to FALSE) |
grid.color |
(color) The color of the grid (defaults to "white") |
labels |
(a character) The text of the label to identify the data. If NA, no label will be plotted. If NULL, if snps is a single sample GRanges it will default to "CN", if it's a list of samples it will default to the names in the list or consecutive numbers if names(snps) is NULL. (defaults to NULL) |
label.cex |
(numeric) The size of the label (defaults to 1.5) |
label.srt |
(numeric) The rotation of the label (defaults to 90, vertical text) |
pos |
The position of the label (defaults to 2) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
2 types of plots. Convergent or divergent barplots
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions
all.scnas <- list( loadCopyNumberCalls(system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)), loadCopyNumberCalls(system.file("extdata", "S2.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)), loadCopyNumberCalls(system.file("extdata", "S3.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)) ) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, all.scnas, r0=0.4, r1=1) #plotCopyNumberSummary(kp, all.scnas, r0=0, r1=0.35) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberSummary(kp, all.scnas, gain.col="blue", loss.col="red", labels="Copy Number", r0=0, r1=0.3, label.srt=90, pos=3) #plotCopyNumberSummary(kp, all.scnas, direction="out", add.grid=TRUE, r0=0.35, r1=0.65) #plotCopyNumberSummary(kp, all.scnas, direction="out", r0=0.7, r1=1) kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=1, tick.pos = c(0,5,10)) kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=0.7, tick.pos = c(5,10)) #NOT RUN (time constraints in Bioconductor checks): # Use a random example with more realistic results # gg <- filterChromosomes(getGenome("hg19")) # all.scnas <- list() # for(i in seq_len(10)) { # scnas <- createRandomRegions(40, 10e6, 10e6) # scnas$cn <- floor(runif(40, 0, 4)) # scnas$loh <- ifelse(scnas$cn<2, 1, 0) # normal.regs <- subtractRegions(gg, scnas) # normal.regs$cn <- 2 # scnas <- sort(c(scnas, normal.regs)) # all.scnas[[paste0("samp", i)]] <- scnas # } # kp <- plotKaryotype("hg19", plot.type=4) # plotCopyNumberSummary(kp, all.scnas) # kp <- plotKaryotype("hg19", plot.type=4) # plotCopyNumberSummary(kp, all.scnas, gain.col="blue", loss.col="red", labels="Copy Number", r0=0, r1=0.3, label.srt=90, pos=3) # plotCopyNumberSummary(kp, all.scnas, direction="out", add.grid=TRUE, r0=0.35, r1=0.65) # plotCopyNumberSummary(kp, all.scnas, direction="out", r0=0.7, r1=1) # kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=1, tick.pos = c(0,5,10)) # kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=0.7, tick.pos = c(5,10))all.scnas <- list( loadCopyNumberCalls(system.file("extdata", "S1.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)), loadCopyNumberCalls(system.file("extdata", "S2.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)), loadCopyNumberCalls(system.file("extdata", "S3.segments.txt", package = "CopyNumberPlots", mustWork = TRUE)) ) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberCalls(kp, all.scnas, r0=0.4, r1=1) #plotCopyNumberSummary(kp, all.scnas, r0=0, r1=0.35) kp <- plotKaryotype(chromosomes="chr1") #plotCopyNumberSummary(kp, all.scnas, gain.col="blue", loss.col="red", labels="Copy Number", r0=0, r1=0.3, label.srt=90, pos=3) #plotCopyNumberSummary(kp, all.scnas, direction="out", add.grid=TRUE, r0=0.35, r1=0.65) #plotCopyNumberSummary(kp, all.scnas, direction="out", r0=0.7, r1=1) kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=1, tick.pos = c(0,5,10)) kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=0.7, tick.pos = c(5,10)) #NOT RUN (time constraints in Bioconductor checks): # Use a random example with more realistic results # gg <- filterChromosomes(getGenome("hg19")) # all.scnas <- list() # for(i in seq_len(10)) { # scnas <- createRandomRegions(40, 10e6, 10e6) # scnas$cn <- floor(runif(40, 0, 4)) # scnas$loh <- ifelse(scnas$cn<2, 1, 0) # normal.regs <- subtractRegions(gg, scnas) # normal.regs$cn <- 2 # scnas <- sort(c(scnas, normal.regs)) # all.scnas[[paste0("samp", i)]] <- scnas # } # kp <- plotKaryotype("hg19", plot.type=4) # plotCopyNumberSummary(kp, all.scnas) # kp <- plotKaryotype("hg19", plot.type=4) # plotCopyNumberSummary(kp, all.scnas, gain.col="blue", loss.col="red", labels="Copy Number", r0=0, r1=0.3, label.srt=90, pos=3) # plotCopyNumberSummary(kp, all.scnas, direction="out", add.grid=TRUE, r0=0.35, r1=0.65) # plotCopyNumberSummary(kp, all.scnas, direction="out", r0=0.7, r1=1) # kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=1, tick.pos = c(0,5,10)) # kpAxis(kp, ymin=0, ymax=10, r0=0.85, r1=0.7, tick.pos = c(5,10))
Plots the raw SNP array data using karyoploteR
plotLRR(karyoplot, snps, lrr.column="lrr", labels="LRR", ymin=-4, ymax=2, out.of.range = "points", out.of.range.col="red", density.height=0.05, density.window=1e5, line.at.0 = TRUE, line.at.0.col="blue", r0=0, r1=1, points.cex=0.3, points.col="#333333", points.pch=16, label.cex=1.5, label.srt=90, label.margin=0.03, add.axis=TRUE, axis.cex=1.2, track.margin=0.1, data.panel=1, verbose=FALSE, ...)plotLRR(karyoplot, snps, lrr.column="lrr", labels="LRR", ymin=-4, ymax=2, out.of.range = "points", out.of.range.col="red", density.height=0.05, density.window=1e5, line.at.0 = TRUE, line.at.0.col="blue", r0=0, r1=1, points.cex=0.3, points.col="#333333", points.pch=16, label.cex=1.5, label.srt=90, label.margin=0.03, add.axis=TRUE, axis.cex=1.2, track.margin=0.1, data.panel=1, verbose=FALSE, ...)
karyoplot |
(a KaryoPlot object) The object returned by the |
snps |
(a GRanges, a list of GRanges or a GRangesList) An object with the positions of the SNPs and a column with the LRR values. Other columns are ignored. If it's a list of GRanges with different samples, all samples will be plotted, splitting the total plot space between them. |
lrr.column |
(number or character) The name or number of the column with LRR information. (defaults to "lrr") |
labels |
(character) The text of the label to identify the data. If NA, no label will be plotted. If NULL, if snps is a single sample GRanges it will default to "LRR", if it's a list of samples it will default to the names in the list or consecutive numbers if names(snps) is NULL.(defaults to "LRR") |
ymin |
(numeric) (karyoploteR parameter) The minimum value of y to be plotted. If NULL, it is set to the min value of the selected data panel. (defaults to -4) |
ymax |
(numeric) (karyoploteR parameter) (numeric) The maximum value of y to be plotted. If NULL, it is set to the max value of the selected data panel. (defaults to 2) |
out.of.range |
(a character) Either to plot "points" or "density" (defaults to "points") |
out.of.range.col |
The color the out-of-range points should be plotted (defaults to "red") |
density.height |
The height of the maximum density peak if out.of.range is "density" (defaults to 0.05) |
density.window |
The window used to compute the uot-of-range density (defaults to 1e5) |
line.at.0 |
(logical) Whether to plot an horizontal line at 0. (defaults to TRUE) |
line.at.0.col |
(color) The color of the horizontal line plotted at 0. (defaults to "blue") |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL) |
points.cex |
(numeric) The size of the points. (defaults to 0.3) |
points.col |
(a color) The color of the points (defaults to "#333333") |
points.pch |
(numeric) The shape of the points. (defaults to 16, a filled circle) |
label.cex |
(numeric) The size of the label (defaults to 1.5) |
label.srt |
(numeric) The rotation of the label (defaults to 90, vertical text) |
label.margin |
(numeric) The margin between the label and the origin of the plot in plot coordinates (the width of the plot is 1). (defaults to 0.03) |
add.axis |
(logical) Whether to add an axis (defaults to TRUE) |
axis.cex |
(numeric) The size of the axis labels. (defaults to 1.2) |
track.margin |
(numeric) If snps is a list object, this is the margin between the samples BAF. (deafults to 0.1) |
data.panel |
(numeric) (karyoploteR parameter) The identifier of the data panel where the data is to be plotted. The available data panels depend on the plot type selected in the call to |
verbose |
(logical) Wether messages with information on the processing should be generated (defaults to FALSE) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
Creates a plot with the LRR values along the genome
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions
pos <- floor(runif(1000, 1, 10000)) lrr.data <- toGRanges("chr1", pos, pos) lrr.data$lrr <- rnorm(1000, mean = 0, sd = 0.3) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotLRR(kp, lrr.data) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotLRR(kp, lrr.data, lrr.column=1, points.col="orange", labels="First", label.cex=0.8, points.cex=1.4)pos <- floor(runif(1000, 1, 10000)) lrr.data <- toGRanges("chr1", pos, pos) lrr.data$lrr <- rnorm(1000, mean = 0, sd = 0.3) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotLRR(kp, lrr.data) kp <- plotKaryotype(zoom=toGRanges("chr1", 1, 10000)) plotLRR(kp, lrr.data, lrr.column=1, points.col="orange", labels="First", label.cex=0.8, points.cex=1.4)
Plot the segments representing the copy number calls from a single-cell CNV data file. The file must be an HDF5 file with the same format as the ones produced by 10X CellRanger software.
plotSingleCellCopyNumberCalls(karyoplot, cnv.file, reorder=TRUE, cn.colors=NULL, track.margin=0, r0=0, r1=1, ...)plotSingleCellCopyNumberCalls(karyoplot, cnv.file, reorder=TRUE, cn.colors=NULL, track.margin=0, r0=0, r1=1, ...)
karyoplot |
A karyoplote object |
cnv.file |
(an HDF5 file) The path to an HDF5 file containing the single-cell CNV data. |
reorder |
(logical) If TRUE, the cells will be plotted inthe order specified by the hirearchical clustering tree embedded in the HDF5 file. If FALSE the cells will be plotted in the order they are stored in the file. (defaukts to TRUE) |
cn.colors |
(colors) The colors assigned to gains and losses. |
track.margin |
(numeric) This is the margin between the cells CN, in portion of the total per cell space. (deafults to 0) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 0) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 1) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
This function will open the HDF5 file, extract the CNV values for each
cell and plot them as a different colored segments using
plotCopyNumberCalls.
By default cells will be reordered and plotted according the hierarchical
clustering tree embedded in the file. If reorder is FALSE or the tree is
not present, cell will be plotted in the order they are stored in the file.
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions. The returned pbject will have an additional "latest.plot" element with a list containing: the number of cells, the hierarchical clustering tree, the bin size used to partition the genome, the windows representing such partitioning, the number of bins per chromosome and a GRAnges with the regions where no cell had any data, the no-call regions.
If the file is open by any other application the function will fail.
kp <- plotKaryotype(plot.type=4, genome="hg38") #NOT RUN - Using 10X example data from https://www.10xgenomics.com/resources/datasets/ #plotSingleCellCopyNumberCalls(kp, "breast_tissue_D_2k_cnv_data.h5")kp <- plotKaryotype(plot.type=4, genome="hg38") #NOT RUN - Using 10X example data from https://www.10xgenomics.com/resources/datasets/ #plotSingleCellCopyNumberCalls(kp, "breast_tissue_D_2k_cnv_data.h5")
Plot a summary of the copy number calls from a single-cell CNV data file. Plot a histogram-like plot with the number of cells with gains and losses. The file must be an HDF5 file with the same format as the ones produced by 10X CellRanger software.
plotSingleCellCopyNumberSummary(karyoplot, cnv.file, direction="in", gain.color=NULL, normal.color=NULL, loss.color=NULL, r0=0, r1=1, ...)plotSingleCellCopyNumberSummary(karyoplot, cnv.file, direction="in", gain.color=NULL, normal.color=NULL, loss.color=NULL, r0=0, r1=1, ...)
karyoplot |
A karyoplot object |
cnv.file |
(an HDF5 file) The path to an HDF5 file containing the single-cell CNV data. |
direction |
The direction to which the coverage plot point, either "in" for inward or "out" for outward. (defaults to "in") |
gain.color |
(color) The color assigned to gains (defaults to NULL) |
normal.color |
(color) The color assigned to normal ploidy(defaults to NULL) |
loss.color |
(colors) The color assigned to losses(defaults to NULL) |
r0 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 0) |
r1 |
(numeric) (karyoploteR parameter) r0 and r1 define the vertical range of the data panel to be used to draw this plot. They can be used to split the data panel in different vertical ranges (similar to tracks in a genome browser) to plot differents data. If NULL, they are set to the min and max of the data panel, it is, to use all the available space. (defaults to NULL)(defaults to 1) |
... |
The ellipsis operator can be used to specify any additional graphical parameters. Any additional parameter will be passed to the internal calls to karyoploteR functions. |
This function will open the HDF5 file, extract the CNV values and counts, for each genomic region, the number of cells with a gain or a loss. It then plots a histogram-like summary of these numbers.
Invisibly returns the karyoplot object representing the plot. With it it is possible to add other elements to the plot using standrad karyoploteR functions. The returned object will have an additional "latest.plot" element with a list containing: the number of cells, the bin size used to partition the genome, the windows representing such partitioning with the number of cells with gains and losses in that each window, the number of bins per chromosome and a GRanges with the regions where no cell had any data, the no-call regions.
If the file is open by any other application the function will fail.
kp <- plotKaryotype(plot.type=4, genome="hg38") #NOT RUN - Using 10X example data from https://www.10xgenomics.com/resources/datasets/ #plotSingleCellCopyNumberSummary(kp, "breast_tissue_D_2k_cnv_data.h5")kp <- plotKaryotype(plot.type=4, genome="hg38") #NOT RUN - Using 10X example data from https://www.10xgenomics.com/resources/datasets/ #plotSingleCellCopyNumberSummary(kp, "breast_tissue_D_2k_cnv_data.h5")
Prepare the Labels to plot.
prepareLabels(labels, x)prepareLabels(labels, x)
labels |
(character) labels to plot. (defaults to NULL) |
x |
(list or GRangesList) The list or GRangesList where extracting the labels |
prepareLabels return a vector with the same length as the length of x. If labels is NULL and x have names, it will return the names of x. If there are not names it will return numbers as labels. If labes is not NULL, prepareLabels will cut or recycle the names as needed.
#List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) prepareLabels(x = seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) prepareLabels(x = seg.data)#List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) prepareLabels(x = seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) prepareLabels(x = seg.data)
Read the Ztree (scipy matrix representarion of a hierarchical clustering) contained in a 10X single-cell CNV HDF5 file and return a valid R hclust object representing the same tree.
More info on the Ztree matrix: https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/
readHDF5Ztree(hdf5.file)readHDF5Ztree(hdf5.file)
hdf5.file |
(H5IdComponent object) The result of opening an HDF5 file with rhdf5::H5Fopen(data.file). The file must contain a "tree" element with Ztree. For example, HDF5 files for CNV data produced by 10X CellRanger. |
A valid hclust object (standard R hierarchical clustering results)
not.run <- TRUE #We have no valid hdf5 file small enough to fit in a package #Not Run #data.file <- "path to a single-cell CNV data file" #open.file <- rhdf5::H5Fopen(data.file) #hc.tree <- readHDF5Ztree(open.file)not.run <- TRUE #We have no valid hdf5 file small enough to fit in a package #Not Run #data.file <- "path to a single-cell CNV data file" #open.file <- rhdf5::H5Fopen(data.file) #hc.tree <- readHDF5Ztree(open.file)
Removing NA from snp data.
removeNAs(snp.data, lrr.na = TRUE, baf.na = TRUE, id.na = TRUE, verbose = TRUE)removeNAs(snp.data, lrr.na = TRUE, baf.na = TRUE, id.na = TRUE, verbose = TRUE)
snp.data |
(GRanges, GRangesList or list) A GRanges, GRangesList or a list of GRanges. |
lrr.na |
(logical) Whether to remove the rows that have NAs in the lrr column. (defaults to TRUE) |
baf.na |
(logical) Whether to remove the rows that have NAs in the baf column. (defaults to TRUE) |
id.na |
(logical) Whether to remove the rows that have NAs in the id column. (defaults to TRUE) |
verbose |
(logical) Whether to show information messages. (defaults to TRUE) |
This function will remove rows with NA values in snp data. We can decide which columns we take into account to detect NAs, using the lrr.na, baf.na and id.na parameters.
A GRanges or a list of GRanges where the NA values have been removed.
#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- removeNAs(snp.data = seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- removeNAs(snp.data = seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- removeNAs(snp.data = seg.data)#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- removeNAs(snp.data = seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- removeNAs(snp.data = seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("chr1", "chr1", "chr2", "chr5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- removeNAs(snp.data = seg.data)
Transformation of the chromosomes
transformChr(chr, chr.transformation = "23:X,24:Y,25:MT")transformChr(chr, chr.transformation = "23:X,24:Y,25:MT")
chr |
(character) The chromosome names to transform |
chr.transformation |
(character) The transformation of the chromosomes names in a comma separated "key:value" format.(defaults to "23:X,24:Y,25:MT") |
This function will transform the name of the chormosomes, according to the parameter chr.transformation. This is useful in situations were files, such as .seg files as the ones generated by CNVkit, have the chromosome names as numbers. In order to transform the sexual and mitocondrial chromosomes from numbers back to characters, we can use this function. The exact transformation might be provided as described at https://cnvkit.readthedocs.io/en/stable/importexport.html#import-seg, (e.g. "key:value")
A character vector where the name of the chromosomes have been changed according to chr.transformation parameter.
seg.file <- system.file("extdata", "DNACopy_output.seg", package = "CopyNumberPlots", mustWork = TRUE) seg.data <- read.table(file = seg.file, sep = "\t", skip = 1, stringsAsFactors = FALSE) colnames(seg.data) <- c("ID", "chrom", "loc.start", "loc.end", "num.mark", "seg.mean") # here we have the name of the chromosomes in a number format and how many segments are in each one. table(seg.data$chrom) # by this way we have the name 23 and 24 respectively transformed to X and Y. seg.data$chrom <- transformChr(chr = seg.data$chrom, chr.transformation ="23:X,24:Y,25:MT") table(seg.data$chrom)seg.file <- system.file("extdata", "DNACopy_output.seg", package = "CopyNumberPlots", mustWork = TRUE) seg.data <- read.table(file = seg.file, sep = "\t", skip = 1, stringsAsFactors = FALSE) colnames(seg.data) <- c("ID", "chrom", "loc.start", "loc.end", "num.mark", "seg.mean") # here we have the name of the chromosomes in a number format and how many segments are in each one. table(seg.data$chrom) # by this way we have the name 23 and 24 respectively transformed to X and Y. seg.data$chrom <- transformChr(chr = seg.data$chrom, chr.transformation ="23:X,24:Y,25:MT") table(seg.data$chrom)
Set the style of the chromosome names to UCSC ("chr1" instead of "1")
UCSCStyle(x)UCSCStyle(x)
x |
(GRanges, GRangesList or list of GRanges) The object to transform to UCSC style |
The same x object with the styles of the seqlevels set to UCSC
#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- UCSCStyle(seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- UCSCStyle(seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- UCSCStyle(seg.data)#GRanges seg.data <- regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA))) seg.data <- UCSCStyle(seg.data) #List of GRanges seg.data <- list(a = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA),baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b=regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- UCSCStyle(seg.data) #GRangesList seg.data <- GRangesList(a = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(NA,0.25,1.5,NA), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459"))), b = regioneR::toGRanges(data.frame(chr = c("1", "1", "2", "5"), start = c(0,50000,8014630,14523572), end = c(48953, 7023664,9216331,153245687), lrr = c(2.5,NA,1.5,0.25), baf = c(1.5,2.5,NA,6), id = c("rs52456","rs52457","rs52458","rs52459")))) seg.data <- UCSCStyle(seg.data)
Transform a Z matrix representing the hierarchical clustering of elements into a valid hclust R object. The Z representation is used by scipy hierarchical clustering and in 10X single-cell CNV HDF5 files.
More info on the Ztree matrix: https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/
Ztree2Hclust(Ztree)Ztree2Hclust(Ztree)
Ztree |
(matrix) A matrix with 4 rows and num.elements - 1 columns. The first and second row represent the elements or clusters merged at each step and the third row the distance between the merged elements. This is the format used by scipy hierarchical clustering. |
A valid hclust object (standard R hierarchical clustering results)
ztree <- matrix(c(0,4,0.1,2, 1,3,0.2,2, 5,2,0.3,2, 6,7,0.4,2), nrow=4) hc.tree <- Ztree2Hclust(ztree) plot(hc.tree)ztree <- matrix(c(0,4,0.1,2, 1,3,0.2,2, 5,2,0.3,2, 6,7,0.4,2), nrow=4) hc.tree <- Ztree2Hclust(ztree) plot(hc.tree)