| Type: | Package |
| Title: | Biosensor Development using Omics Data |
| Version: | 0.2.0 |
| Maintainer: | Takahiko Koizumi <takahiko.koizumi@gmail.com> |
| Description: | A method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data. |
| Depends: | R (≥ 3.5.0) |
| License: | GPL (≥ 3) |
| Language: | en-US |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| URL: | <https://github.com/takakoizumi/OmicSense> |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Imports: | ggplot2, kernlab |
| NeedsCompilation: | no |
| Packaged: | 2023-09-22 21:14:43 UTC; taka |
| Author: | Takahiko Koizumi [aut, cre], Kenta Suzuki [ctb], Yasunori Ichihashi [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2023-09-22 21:30:08 UTC |
Transcriptomes of Pinus roots under a Temperature Gradient
Description
This dataset gives the TPM values of 200 selected genes obtained from 60 Pinus root samples (30 samples each for training and test data) under a temperature gradient, generated by RNA-seq.
Usage
PinusDetails
A gene expression data matrix of 30 root samples of P. thunbergii under five temperature conditions (8, 13, 18, 23, 28 °C) with six biological replicates is in the first element of the list.
A gene expression data matrix of another 30 root samples of P. thunbergii under the same condition is in the second one.
Temperature conditions where 30 root samples in each data matrix were generated are in the third one.
Gene expressions are normalized in the TPM value.
Source
original (not published)
References
original (not published)
Clean data by eliminating predictors with many missing values
Description
Clean data by eliminating predictors with many missing values
Usage
os.clean(x, missing = 0.1, lowest = 10)
Arguments
x |
A data matrix (raw: samples, col: predictors). |
missing |
A ratio of missing values in each column allowed to be remained in the data. |
lowest |
The lowest value to be leaved in the data. |
Value
A data matrix (raw: samples, col: qualified predictors)
Author(s)
Takahiko Koizumi
Examples
data(Pinus)
train.raw <- Pinus$train
ncol(train.raw)
train <- os.clean(train.raw)
ncol(train)
Visualize predictors using principal coordinate analysis
Description
Visualize predictors using principal coordinate analysis
Usage
os.pca(x, y, method = "linear", thresh = 0, n.pred = ncol(x), size = 1)
Arguments
x |
A data matrix (row: samples, col: predictors). |
y |
A vector of target value. |
method |
A string to specify the regression function for calculating R-squared values. "linear" (default), "quadratic" or "cubic" function can be specified. |
thresh |
The lower threshold of R-squared value to be indicated in a PCA plot (default: 0). |
n.pred |
The number of candidate predictors for prediction model to be indicated in a PCA plot (default: ncol(x)). |
size |
The size of symbols in a PCA plot (default: 1). |
Value
A PCA plot
Author(s)
Takahiko Koizumi
Examples
data(Pinus)
train <- os.clean(Pinus$train)
target <- Pinus$target
os.pca(train, target)
Construct and apply the OmicSense model with your own data
Description
Construct and apply the OmicSense model with your own data
Usage
os.pred(x, y, newx = x, method = "linear", thresh = 0, n.pred = 0)
Arguments
x |
A data matrix (row: samples, col: predictors). |
y |
A vector of target value. |
newx |
A data matrix (row: samples, col: predictors). |
method |
A string to specify the regression function for calculating R-squared values. "linear" (default), "quadratic" or "cubic" function can be specified. |
thresh |
The lower threshold of R-squared value to be leaved in prediction model (default: 0). |
n.pred |
The number of candidate predictors to be leaved in prediction model (default: 30). |
Value
A vector of the environment in which the samples of newx were collected
Author(s)
Takahiko Koizumi
Examples
data(Pinus)
train <- os.clean(Pinus$train)
test <- Pinus$test
test <- test[, colnames(train)]
target <- Pinus$target
cor(target, os.pred(train, target, newx = test, method = "cubic"))
Visualize R-squared value distribution in target-predictor relationship
Description
Visualize R-squared value distribution in target-predictor relationship
Usage
os.rank(
x,
y,
method = "linear",
thresh = 0,
n.pred = ncol(x),
upper.xlim = ncol(x)
)
Arguments
x |
A data matrix (row: samples, col: predictors). |
y |
A vector of target value. |
method |
A string to specify the regression function for calculating R-squared values. "linear" (default), "quadratic" or "cubic" function can be specified. |
thresh |
The lower threshold of R-squared value to be leaved in prediction model (default: 0). |
n.pred |
The number of predictors to be leaved in prediction model (default: ncol(x)). |
upper.xlim |
The upper limitation of x axis (i.e., the number of predictors) in the resulted figure (default: ncol(x)). |
Value
A rank order plot
Author(s)
Takahiko Koizumi
Examples
data(Pinus)
train <- os.clean(Pinus$train)
target <- Pinus$target
train <- os.sort(train, target)
os.rank(train, target)
Sort and select predictors according to the strength of target-predictor relationship
Description
Sort and select predictors according to the strength of target-predictor relationship
Usage
os.sort(x, y, method = "linear", n.pred = ncol(x), thresh = 1)
Arguments
x |
A data matrix (raw: samples, col: predictors). |
y |
A vector of target value. |
method |
A string to specify the regression function for calculating R-squared values. "linear" (default), "quadratic" or "cubic" function can be specified. |
n.pred |
The number of predictors to be leaved in prediction model (default: ncol(x)). |
thresh |
The lower threshold of R-squared value to be leaved in prediction model (default: 1). |
Value
A data matrix (raw: samples, col: sorted predictors)
Author(s)
Takahiko Koizumi
Examples
data(Pinus)
train <- os.clean(Pinus$train)
target <- Pinus$target
cor(target, train[, 1])
train <- os.sort(train, target, thresh = 0.5)
cor(target, train[, 1])