RNAseqdata: Import, check and normalization and transformation of RNAseq...
In DRomics: Dose Response for Omics
RNAseqdata R Documentation
Import, check and normalization and transformation of RNAseq data
Description
RNAseq data in raw counts (integer values) are imported from a .txt file
(internally imported using the function read.table),
checked or from a R object of class data.frame
(see the description
of argument file for the required format
of data),
normalized with respect to library size and tranformed in
a log2 scale using
variance stabilizing transformation or regularized logarithm.
Usage
RNAseqdata(file, backgrounddose, check = TRUE, transfo.method,
transfo.blind = TRUE, round.counts = FALSE)
## S3 method for class 'RNAseqdata'
print(x, ...)
## S3 method for class 'RNAseqdata'
plot(x, range4boxplot = 0, ...)
Arguments
file
The name of the .txt file (e.g. "mydata.txt")
containing one row per item, with the first column corresponding to the identifier of each item,
and the other columns giving the responses
of the item for each replicate at each dose or concentration.
In the first line, after a name for the identifier column, we must have the tested
doses or concentrations in a numeric format for the corresponding replicate
(for example, if there are triplicates for each treatment,
the first line could be "item", 0, 0, 0, 0.1, 0.1, 0.1, etc.).
This file is imported within the function using the function
read.table with its default field separator (sep argument) and
its default decimal separator (dec argument at ".").
Alternatively an R object of class data.frame can be directly given in input,
corresponding to the output
of read.table(file, header = FALSE) on a file described as above. The two
alternatives are illustrated below in examples.
backgrounddose
This argument must be used when there is no dose at zero in the data, to prevent the calculation of the BMD by extrapolation. All doses below or equal to the value given in backgrounddose will be fixed at 0, so as to be considered at the background level of exposition.
check
If TRUE the format of the input file is checked.
transfo.method
The method chosen to transform raw counts in a log2 scale
using the
DESeq2: "rlog" for regularized logarithm or
"vst" for variance stabilizing transformation. If missing, default value
defined at "rlog" for datasets with less than 30 samples and at "vst" if not
transfo.blind
Argument given to function rlog or vst,
see rlog and vst for an explaination, by default
at TRUE as in the DESeq2 package .
round.counts
Put it to TRUE if your counts come from Kallisto or Salmon
in order to round them before treatment with DESeq2.
x
An object of class "RNAseqdata".
range4boxplot
An argument passed to boxplot(), fixed by default at 0
to prevent the producing of very large plot files due to many outliers.
Can be put at 1.5 to obtain more classical boxplots.
...
further arguments passed to print or plot functions.
Details
This function imports the data, checks their format (see the description
of argument file for the required format
of data) and gives in the print information
that should help the user to check that the coding of data is correct : the tested doses (or concentrations)
the number of replicates for each dose, the number of items, the identifiers
of the first 20 items. Data are normalized with respect to library size
and tranformed using functions rlog or vst of the
DESeq2 package depending on the specified method : "rlog"
(recommended default choice) or
"vst".
Value
RNAseqdata returns an object of class "RNAseqdata", a list with 9 components:
data
the numeric matrix of normalized and transformed
responses of each item in each replicate
(one line per item, one column per replicate)
dose
the numeric vector of the tested doses or concentrations corresponding
to each column of data
item
the character vector of the identifiers of the items, corresponding to
each line of data
design
a table with the experimental design (tested doses and number of
replicates for each dose) for control by the user
data.mean
the numeric matrix of mean responses of each item per dose (mean
of the corresponding replicates) (one line per item, one column per unique value
of the dose)
data.sd
the numeric matrix of standard deviations of the response of each
item per dose (sd of the corresponding replicates, NA if no replicate)
(one line per item, one column per unique value of the dose)
transfo.method
The transformation method specified in input
raw.counts
the numeric matrix of non transformed responses (raw counts)
of each item in each replicate
(one line per item, one column per replicate) before normalization
containsNA
always at FALSE as RNAseq data are not allowed to contain
NA values
The print of a RNAseqdata object gives the tested doses (or concentrations)
and number of replicates for each dose, the number of items, the identifiers
of the first 20 items (for check of good coding of data) and the tranformation method.
The plot of a RNAseqdata object shows the data distribution for each dose or concentration and replicate before and after normalization and tranformation.
Author(s)
Marie-Laure Delignette-Muller
References
Love MI, Huber W, and Anders S (2014), Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 550.
See Also
See read.table the function used to import data,
rlog and vst for details about the
transformation methods and
microarraydata, continuousomicdata and
continuousanchoringdata for other types of data.
Examples
# (1) import, check, normalization and transformation of RNAseq data
# An example on a subsample of a data set published by Zhou et al. 2017
# Effect on mouse kidney transcriptomes of tetrachloroethylene
# (see ? Zhou for details)
#
datafilename <- system.file("extdata", "RNAseq_sample.txt", package="DRomics")
(o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "vst"))
plot(o)
# If you want to use your own data set just replace datafilename,
# the first argument of RNAseqdata(),
# by the name of your data file (e.g. "mydata.txt")
#
# You should take care that the field separator of this data file is one
# of the default field separators recognised by the read.table() function
# when it is used with its default field separator (sep argument)
# Tabs are recommended.
# Use of an R object of class data.frame
# below the same example taking a subsample of the data set
# Zhou_kidney_pce (see ?Zhou for details)
data(Zhou_kidney_pce)
subsample <- Zhou_kidney_pce[1:1000, ]
(o <- RNAseqdata(subsample, check = TRUE, transfo.method = "vst"))
plot(o)
PCAdataplot(o)
# (2) transformation with two methods on the whole data set
data(Zhou_kidney_pce)
# variance stabilizing tranformation
(o1 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst"))
plot(o1)
# regularized logarithm
(o2 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog"))
plot(o2)
# variance stabilizing tranformation (blind to the experimental design)
(o3 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst",
transfo.blind = TRUE))
plot(o3)
# regularized logarithm
(o4 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog",
transfo.blind = TRUE))
plot(o4)
DRomics documentation built on Oct. 16, 2024, 5:09 p.m.
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| RNAseqdata | R Documentation |
Import, check and normalization and transformation of RNAseq data
Description
RNAseq data in raw counts (integer values) are imported from a .txt file
(internally imported using the function read.table),
checked or from a R object of class data.frame
(see the description
of argument file for the required format
of data),
normalized with respect to library size and tranformed in
a log2 scale using
variance stabilizing transformation or regularized logarithm.
Usage
RNAseqdata(file, backgrounddose, check = TRUE, transfo.method,
transfo.blind = TRUE, round.counts = FALSE)
## S3 method for class 'RNAseqdata'
print(x, ...)
## S3 method for class 'RNAseqdata'
plot(x, range4boxplot = 0, ...)
Arguments
file |
The name of the .txt file (e.g. |
backgrounddose |
This argument must be used when there is no dose at zero in the data, to prevent the calculation of the BMD by extrapolation. All doses below or equal to the value given in backgrounddose will be fixed at 0, so as to be considered at the background level of exposition. |
check |
If TRUE the format of the input file is checked. |
transfo.method |
The method chosen to transform raw counts in a log2 scale
using the
|
transfo.blind |
Argument given to function |
round.counts |
Put it to TRUE if your counts come from Kallisto or Salmon
in order to round them before treatment with |
x |
An object of class |
range4boxplot |
An argument passed to boxplot(), fixed by default at 0 to prevent the producing of very large plot files due to many outliers. Can be put at 1.5 to obtain more classical boxplots. |
... |
further arguments passed to print or plot functions. |
Details
This function imports the data, checks their format (see the description
of argument file for the required format
of data) and gives in the print information
that should help the user to check that the coding of data is correct : the tested doses (or concentrations)
the number of replicates for each dose, the number of items, the identifiers
of the first 20 items. Data are normalized with respect to library size
and tranformed using functions rlog or vst of the
DESeq2 package depending on the specified method : "rlog"
(recommended default choice) or
"vst".
Value
RNAseqdata returns an object of class "RNAseqdata", a list with 9 components:
data |
the numeric matrix of normalized and transformed responses of each item in each replicate (one line per item, one column per replicate) |
dose |
the numeric vector of the tested doses or concentrations corresponding to each column of data |
item |
the character vector of the identifiers of the items, corresponding to each line of data |
design |
a table with the experimental design (tested doses and number of replicates for each dose) for control by the user |
data.mean |
the numeric matrix of mean responses of each item per dose (mean of the corresponding replicates) (one line per item, one column per unique value of the dose) |
data.sd |
the numeric matrix of standard deviations of the response of each item per dose (sd of the corresponding replicates, NA if no replicate) (one line per item, one column per unique value of the dose) |
transfo.method |
The transformation method specified in input |
raw.counts |
the numeric matrix of non transformed responses (raw counts) of each item in each replicate (one line per item, one column per replicate) before normalization |
containsNA |
always at FALSE as RNAseq data are not allowed to contain NA values |
The print of a RNAseqdata object gives the tested doses (or concentrations)
and number of replicates for each dose, the number of items, the identifiers
of the first 20 items (for check of good coding of data) and the tranformation method.
The plot of a RNAseqdata object shows the data distribution for each dose or concentration and replicate before and after normalization and tranformation.
Author(s)
Marie-Laure Delignette-Muller
References
Love MI, Huber W, and Anders S (2014), Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 550.
See Also
See read.table the function used to import data,
rlog and vst for details about the
transformation methods and
microarraydata, continuousomicdata and
continuousanchoringdata for other types of data.
Examples
# (1) import, check, normalization and transformation of RNAseq data
# An example on a subsample of a data set published by Zhou et al. 2017
# Effect on mouse kidney transcriptomes of tetrachloroethylene
# (see ? Zhou for details)
#
datafilename <- system.file("extdata", "RNAseq_sample.txt", package="DRomics")
(o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "vst"))
plot(o)
# If you want to use your own data set just replace datafilename,
# the first argument of RNAseqdata(),
# by the name of your data file (e.g. "mydata.txt")
#
# You should take care that the field separator of this data file is one
# of the default field separators recognised by the read.table() function
# when it is used with its default field separator (sep argument)
# Tabs are recommended.
# Use of an R object of class data.frame
# below the same example taking a subsample of the data set
# Zhou_kidney_pce (see ?Zhou for details)
data(Zhou_kidney_pce)
subsample <- Zhou_kidney_pce[1:1000, ]
(o <- RNAseqdata(subsample, check = TRUE, transfo.method = "vst"))
plot(o)
PCAdataplot(o)
# (2) transformation with two methods on the whole data set
data(Zhou_kidney_pce)
# variance stabilizing tranformation
(o1 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst"))
plot(o1)
# regularized logarithm
(o2 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog"))
plot(o2)
# variance stabilizing tranformation (blind to the experimental design)
(o3 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "vst",
transfo.blind = TRUE))
plot(o3)
# regularized logarithm
(o4 <- RNAseqdata(Zhou_kidney_pce, check = TRUE, transfo.method = "rlog",
transfo.blind = TRUE))
plot(o4)
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