| Title: | Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data |
| Version: | 0.1.0 |
| Description: | BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions. |
| Depends: | R (≥ 3.3.1) |
| Imports: | data.table, ggplot2, shiny, shinydashboard, plotly, BSDA, outliers |
| Suggests: | testthat |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-12-05 11:08:16 UTC; Naoto |
| Author: | Naoto Imamachi [aut, cre] |
| Maintainer: | Naoto Imamachi <naoto.imamachi@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-12-05 14:49:24 |
BridgeR basic function for calculating RNA half-life from BRIC-seq data
Description
BridgeRCore is a basic function for calculating RNA half-life
BRIC-seq data and a wrapper of the other individual bridger2 functions.
Usage
BridgeRCore(inputFile, inforColumn = 4, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), RPKMcutoff = 0.1, cutoffBelow = 0.1,
YMin = -2, YMax = 2, downsamplingFig = 0.2, makeFig = FALSE,
cutoffQuantile = 0.975, inforHKGenesRow = "symbol", HKGenes = c("GAPDH",
"PGK1", "PPIA", "ENO1", "ATP5B", "ALDOA"), CutoffTimePointNumber = 4,
R2_criteria = 0.9, TimePointRemoval1 = c(1, 2), TimePointRemoval2 = c(8,
12), ThresholdHalfLife1 = 3, ThresholdHalfLife2 = 12, save = TRUE,
outputPrefix = "BridgeR", normalization = "default", method = "default")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
inforColumn |
The number of information columns. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
RPKMcutoff |
Cutoff value of RPKM at 0hr. |
cutoffBelow |
Cutoff value of RPKM at all time points. |
YMin |
Y-axis min. |
YMax |
Y-axis max. |
downsamplingFig |
the factor for downsampling. |
makeFig |
Whether to save the figure of normalization factor. |
cutoffQuantile |
cutoff value of quantile.#' @param save Whether to save the output matrix file. |
inforHKGenesRow |
The column number of house-keeping gene information. |
HKGenes |
The vector of house-keeping genes. |
CutoffTimePointNumber |
The number of minimum time points for calc. |
R2_criteria |
The cutoff of R2 for R2 selection. |
TimePointRemoval1 |
The candicate_1 of time point removal. |
TimePointRemoval2 |
The candicate_2 of time point removal. |
ThresholdHalfLife1 |
The cutoff of TimePointRemoval1. |
ThresholdHalfLife2 |
The cutoff of TimePointRemoval2. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
normalization |
select "default" (percentile method) or "house_keeping_genes" |
method |
select "default" (R2 selection/1st-order) or "3models". |
Value
data.table object including RNA half-life, R2 and the selected fitting model.
Examples
halflife_table <- BridgeRCore(RNA_halflife_comparison[1:30,],
save = FALSE)
halflife_table <- BridgeRCore(RNA_halflife_comparison_HK[177:206],
save = FALSE,
normalization = "house_keeping_genes",
method = "3models")
Calculate relative RPKM expression from data.table format.
Description
BridgeRDataSetFromMatrix calculates the relative RPKM values compared with 0hr,
importing data.table format.
Usage
BridgeRDataSetFromMatrix(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), cutoff = 0.1, cutoffBelow = 0.1,
inforColumn = 4, save = T, outputPrefix = "BridgeR_1")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
cutoff |
Cutoff value of RPKM at 0hr. |
cutoffBelow |
Cutoff value of RPKM at all time points. |
inforColumn |
The number of information columns. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about relative RPKM values.
Examples
library(data.table)
rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(41, 5, 5),
CTRL_1_1h = c(48, 7, 6),
CTRL_1_2h = c(56, 10, 6),
CTRL_1_4h = c(87, 12, 10),
CTRL_1_8h = c(124, 20, 11),
CTRL_1_12h = c(185, 22, 15),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(21, 10, 3),
KD_1_1h = c(33, 11, 3),
KD_1_2h = c(42, 15, 4),
KD_1_4h = c(60, 20, 5),
KD_1_8h = c(65, 37, 6),
KD_1_12h = c(70, 42, 6))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
test_table <- BridgeRDataSetFromMatrix(inputFile = rpkm_matrix,
group = group,
hour = hour,
cutoff = 0.1,
inforColumn = 4,
save = FALSE)
Calculate relative RPKM expression from raw data.
Description
BridgeRDataSetFromRaw calculates the relative RPKM values
compared with 0hr, importing tab-delimited txt file.
Usage
BridgeRDataSetFromRaw(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), cutoff = 0.1, cutoffBelow = 0.1,
inforColumn = 4, save = T, outputPrefix = "BridgeR_1")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
cutoff |
Cutoff value of RPKM at 0hr. |
cutoffBelow |
Cutoff value of RPKM at all time points. |
inforColumn |
The number of information columns. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about relative RPKM values.
BRIC-seq Dataset checker
Description
BridgeRDatasetChecker returns several BRIC-seq dataset information.
This function is used for checking your BRIC-seq dataset quality.
Usage
BridgeRDatasetChecker(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, percentile = c(0.99, 0.95,
0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05), save = T,
outputPrefix = "BridgeR_2_raw")
Arguments
inputFile |
Input matrix object. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
percentile |
Percentile numbers. |
save |
Whether to save the output fig file. |
outputPrefix |
The prefix for the name of the output. |
Value
list object about ggplot2 fig data.
Examples
library(data.table)
normalized_table <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
fig_list_norm <- BridgeRDatasetChecker(inputFile = normalized_table,
save = FALSE)
RNA half-life Grubbs test.
Description
BridgeRGrubbsTest calculates the p-value for each gene
using grubbs test. The estimation is based on the standard deviation of
RNA half-lives in control conditions.
Usage
BridgeRGrubbsTest(controlFile, compFile, hour = c(0, 1, 2, 4, 8, 12),
controlGroup = c("CTRL_PUM1", "CTRL_PUM2", "CTRL_DKD"), inforColumn = 4,
compIndex = 2, save = T, outputPrefix = "BridgeR_8")
Arguments
controlFile |
The dataframe of halflife table. |
compFile |
The dataframe of RPKM table. |
hour |
The vector of time course about BRIC-seq experiment. |
controlGroup |
The vector of group names. |
inforColumn |
The number of information columns. |
compIndex |
The number of information columns. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about Grabbs test result.
Examples
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
half_sd_table <- CalcHalflifeDeviation(halflife_table,
RNA_halflife_grubbs_test,
group = c("CTRL_1",
"CTRL_2",
"CTRL_3"),
save = FALSE)
grubbs_table <- BridgeRGrubbsTest(half_sd_table,
halflife_table,
compIndex = 4,
controlGroup = c("CTRL_1",
"CTRL_2",
"CTRL_3"),
save = FALSE)
Calculate RNA half-life for each gene using 3model method.
Description
BridgeRHalfLifeCalc3models calculates RNA half-life for each gene
using 3 models methods (older version).
Usage
BridgeRHalfLifeCalc3models(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, CutoffTimePointNumber = 4,
save = T, outputPrefix = "BridgeR_5")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
CutoffTimePointNumber |
The number of minimum time points for calc. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about RNA half-life, R2 and fitting model.
Examples
library(data.table)
normalized_rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
halflife_table <- BridgeRHalfLifeCalc3models(normalized_rpkm_matrix,
group = group,
hour = hour,
save = FALSE)
Calculate RNA half-life for each gene using R2 selection method.
Description
BridgeRHalfLifeCalcR2Select calculates RNA half-life for each gene
using R2 selection method (default version).
Usage
BridgeRHalfLifeCalcR2Select(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, CutoffTimePointNumber = 4,
R2_criteria = 0.9, TimePointRemoval1 = c(1, 2), TimePointRemoval2 = c(8,
12), ThresholdHalfLife1 = 3, ThresholdHalfLife2 = 12, save = T,
outputPrefix = "BridgeR_5")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
CutoffTimePointNumber |
The number of minimum time points for calc. |
R2_criteria |
The cutoff of R2 for R2 selection. |
TimePointRemoval1 |
The candicate_1 of time point removal. |
TimePointRemoval2 |
The candicate_2 of time point removal. |
ThresholdHalfLife1 |
The cutoff of TimePointRemoval1. |
ThresholdHalfLife2 |
The cutoff of TimePointRemoval2. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about RNA half-life, R2 and fitting model.
Examples
library(data.table)
normalized_rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
halflife_table <- BridgeRHalfLifeCalcR2Select(normalized_rpkm_matrix,
group = group,
hour = hour,
save = FALSE)
Calculate the normalized RPKM for BRIC-seq dataset.
Description
BridgeRNormalization calculates the normalized RPKM values.
Usage
BridgeRNormalization(inputFile, normFactorFile, group = c("Control",
"Knockdown"), hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, save = T,
outputPrefix = "BridgeR_4")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
normFactorFile |
The vector of tab-delimited normalization factor file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about normalized RPKM values.
Examples
library(data.table)
rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(41, 5, 5),
CTRL_1_1h = c(48, 7, 6),
CTRL_1_2h = c(56, 10, 6),
CTRL_1_4h = c(87, 12, 10),
CTRL_1_8h = c(124, 20, 11),
CTRL_1_12h = c(185, 22, 15),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(21, 10, 3),
KD_1_1h = c(33, 11, 3),
KD_1_2h = c(42, 15, 4),
KD_1_4h = c(60, 20, 5),
KD_1_8h = c(65, 37, 6),
KD_1_12h = c(70, 42, 6))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
rpkm_list <- BridgeRDataSetFromMatrix(inputFile = rpkm_matrix,
group = group,
hour = hour,
cutoff = 0.1,
inforColumn = 4,
save = FALSE)
raw_table <- rpkm_list[[1]]
test_table <- rpkm_list[[2]]
factor_table <- BridgeRNormalizationFactors(test_table,
save = FALSE)
normalized_table <- BridgeRNormalization(test_table,
factor_table,
save = FALSE)
Calculate normalization factors for BRIC-seq datasets.
Description
BridgeRNormalizationFactors calculates the normalization factors
for BRIC-seq datasets.
Usage
BridgeRNormalizationFactors(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, save = T, YMin = -2,
YMax = 2, downsamplingFig = 0.2, makeFig = FALSE,
cutoffQuantile = 0.975, figOutputPrefix = "BridgeR_3_fig",
factorOutputPrefix = "BridgeR_3")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
save |
Whether to save the output matrix file. |
YMin |
Y-axis min. |
YMax |
Y-axis max. |
downsamplingFig |
the factor for downsampling. |
makeFig |
Whether to save the figure of normalization factor. |
cutoffQuantile |
cutoff value of quantile. |
figOutputPrefix |
The prefix for the name of figure output. |
factorOutputPrefix |
The prefix for the name of factor output. |
Value
data.table object about normalization factors calculated by quantile method.
Examples
library(data.table)
rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(41, 5, 5),
CTRL_1_1h = c(48, 7, 6),
CTRL_1_2h = c(56, 10, 6),
CTRL_1_4h = c(87, 12, 10),
CTRL_1_8h = c(124, 20, 11),
CTRL_1_12h = c(185, 22, 15),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(21, 10, 3),
KD_1_1h = c(33, 11, 3),
KD_1_2h = c(42, 15, 4),
KD_1_4h = c(60, 20, 5),
KD_1_8h = c(65, 37, 6),
KD_1_12h = c(70, 42, 6))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
rpkm_list <- BridgeRDataSetFromMatrix(inputFile = rpkm_matrix,
group = group,
hour = hour,
cutoff = 0.1,
inforColumn = 4,
save = FALSE)
raw_table <- rpkm_list[[1]]
test_table <- rpkm_list[[2]]
factor_table <- BridgeRNormalizationFactors(test_table,
save = FALSE)
Calculate normalization factors from house-keeping genes.
Description
BridgeRNormalizationFactorsHK calculates the normalization factors
from house-keeping genes.
Usage
BridgeRNormalizationFactorsHK(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4,
inforHKGenesRow = "symbol", HKGenes = c("GAPDH", "PGK1", "PPIA", "ENO1",
"ATP5B", "ALDOA"), save = T, factorOutputPrefix = "BridgeR_3")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
inforHKGenesRow |
The column number of house-keeping gene information. |
HKGenes |
The vector of house-keeping genes. |
save |
Whether to save the output matrix file. |
factorOutputPrefix |
The prefix for the name of factor output. |
Value
data.table object about normalization factor calculated by house-keeping genes.
Examples
library(data.table)
rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(41, 5, 5),
CTRL_1_1h = c(48, 7, 6),
CTRL_1_2h = c(56, 10, 6),
CTRL_1_4h = c(87, 12, 10),
CTRL_1_8h = c(124, 20, 11),
CTRL_1_12h = c(185, 22, 15),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(21, 10, 3),
KD_1_1h = c(33, 11, 3),
KD_1_2h = c(42, 15, 4),
KD_1_4h = c(60, 20, 5),
KD_1_8h = c(65, 37, 6),
KD_1_12h = c(70, 42, 6))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
rpkm_list <- BridgeRDataSetFromMatrix(inputFile = rpkm_matrix,
group = group,
hour = hour,
cutoff = 0.1,
inforColumn = 4,
save = FALSE)
raw_table <- rpkm_list[[1]]
test_table <- rpkm_list[[2]]
factor_table <- BridgeRNormalizationFactorsHK(test_table,
save = FALSE)
Calculate Fold-change of RNA half-life and p-value.
Description
BridgeRPvalueEvaluation calculates the fold-change of RNA half-life
and p-value between two conditions.
Usage
BridgeRPvalueEvaluation(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), comparisonFile = c("Control", "Knockdown"),
inforColumn = 4, CutoffTimePointNumber = 4, calibration = FALSE,
save = TRUE, outputPrefix = "BridgeR_6")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
comparisonFile |
The vector of group names. |
inforColumn |
The number of information columns. |
CutoffTimePointNumber |
The number of minimum time points for calc. |
calibration |
Calibration of RNA half-life. |
save |
Whether to save the output matrix file. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about Fold-change of RNA half-lives, p-value.
Examples
library(data.table)
normalized_rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
halflife_table <- BridgeRHalfLifeCalcR2Select(normalized_rpkm_matrix,
group = group,
hour = hour,
save = FALSE)
pvalue_table <- BridgeRPvalueEvaluation(halflife_table,
group = group,
hour = hour,
save = FALSE)
BRIC-seq result checker
Description
BridgeRResultChecker returns several BRIC-seq result information.
This function is used for checking the distribution of genome-wide RNA half-lives.
Usage
BridgeRResultChecker(inputFile, group = c("Control", "Knockdown"),
hour = c(0, 1, 2, 4, 8, 12), inforColumn = 4, save = T,
outputPrefix = "BridgeR_9")
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
inforColumn |
The number of information columns. |
save |
Whether to save the output fig file. |
outputPrefix |
The prefix for the name of the output. |
Value
list object about ggplot2 fig data.
Examples
library(data.table)
normalized_table <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
halflife_table <- BridgeRHalfLifeCalcR2Select(normalized_table,
group = group,
hour = hour,
save = FALSE)
pvalue_table <- BridgeRPvalueEvaluation(halflife_table,
group = group,
hour = hour,
save = FALSE)
result_fig <- BridgeRResultChecker(pvalue_table,
save = FALSE)
Shinyapp reporting for drawing RNA decay curve.
Description
BridgeReport returns a shinyapp object to draw RNA decay curve.
You can easily check RNA half-life and RNA decay fitting curve on
your web browser.
Usage
BridgeReport(inputFile, group = c("Control", "Knockdown"), hour = c(0, 1, 2,
4, 8, 12), comparisonFile = c("Control", "Knockdown"),
searchRowName = "symbol", inforColumn = 4, color = c("black", "red"),
TimePointRemoval1 = c(1, 2), TimePointRemoval2 = c(8, 12))
Arguments
inputFile |
The vector of tab-delimited matrix file. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
comparisonFile |
The vector of group names. |
searchRowName |
Row name for searching. |
inforColumn |
The number of information columns. |
color |
color of line graph for two decay curve. |
TimePointRemoval1 |
The candicate_1 of time point removal. |
TimePointRemoval2 |
The candicate_2 of time point removal. |
Value
shiny.appobj object for searching and showing RNA decay curve for each gene.
Examples
library(data.table)
normalized_rpkm_matrix <- data.table(gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
CTRL_1_0h = c(1.00, 1.00, 1.00),
CTRL_1_1h = c(1.00, 0.86, 0.96),
CTRL_1_2h = c(1.00, 0.96, 0.88),
CTRL_1_4h = c(1.00, 0.74, 0.85),
CTRL_1_8h = c(1.00, 0.86, 0.68),
CTRL_1_12h = c(1.01, 0.65, 0.60),
gr_id = c(8, 9, 14),
symbol = c("AAAS", "AACS", "AADAT"),
accession_id = c("NM_015665", "NM_023928", "NM_182662"),
locus = c("chr12", "chr12", "chr4"),
KD_1_0h = c(1.00, 1.00, 1.00),
KD_1_1h = c(1.01, 0.73, 0.71),
KD_1_2h = c(1.01, 0.77, 0.69),
KD_1_4h = c(1.01, 0.72, 0.67),
KD_1_8h = c(1.01, 0.64, 0.38),
KD_1_12h = c(1.00, 0.89, 0.63))
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
halflife_table <- BridgeRHalfLifeCalcR2Select(normalized_rpkm_matrix,
group = group,
hour = hour,
save = FALSE)
pvalue_table <- BridgeRPvalueEvaluation(halflife_table,
save = FALSE)
shiny_test <- BridgeReport(pvalue_table)
Calculate RNA half-life SD.
Description
BridgeRHalfLifeCalcR2Select calculates RPKM SD and RNA half-life SD
for each gene.
Usage
CalcHalflifeDeviation(inputFile, rawFile, group = c("CTRL_PUM1", "CTRL_PUM2",
"CTRL_DKD"), hour = c(0, 1, 2, 4, 8, 12), save = T, figSave = F,
inforColumn = 4, outputPrefix = "BridgeR_7")
Arguments
inputFile |
The dataframe of halflife table. |
rawFile |
The dataframe of RPKM table. |
group |
The vector of group names. |
hour |
The vector of time course about BRIC-seq experiment. |
save |
Whether to save the output matrix file. |
figSave |
Whether to save the output fig file. |
inforColumn |
The number of information columns. |
outputPrefix |
The prefix for the name of the output. |
Value
data.table object about RNA half-life SD.
Examples
group <- c("Control", "Knockdown")
hour <- c(0, 1, 2, 4, 8, 12)
half_sd_table <- CalcHalflifeDeviation(halflife_table,
RNA_halflife_grubbs_test,
group = c("CTRL_1",
"CTRL_2",
"CTRL_3"),
save = FALSE)
test BRIC-seq dataset for RNA half-life comparison
Description
A dataset containing the RPKM for each time point and information column about 200 genes. The variables are as follows:
Usage
RNA_halflife_comparison
Format
A data frame with 200 rows and 20 variables:
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- CTRL_1_0h
RPKM value at 0h in control condition
- CTRL_1_1h
RPKM value at 1h in control condition
- CTRL_1_2h
RPKM value at 2h in control condition
- CTRL_1_4h
RPKM value at 4h in control condition
- CTRL_1_8h
RPKM value at 8h in control condition
- CTRL_1_12h
RPKM value at 12h in control condition
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- KD_1_0h
RPKM value at 0h in knockdown condition
- KD_1_1h
RPKM value at 1h in knockdown condition
- KD_1_2h
RPKM value at 2h in knockdown condition
- KD_1_4h
RPKM value at 4h in knockdown condition
- KD_1_8h
RPKM value at 8h in knockdown condition
- KD_1_12h
RPKM value at 12h in knockdown condition
test BRIC-seq dataset for RNA half-life comparison using House-keeping genes.
Description
A dataset containing the RPKM for each time point and information column about 200 genes + house-keeping genes. The variables are as follows:
Usage
RNA_halflife_comparison_HK
Format
A data frame with 200 rows and 20 variables:
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- CTRL_1_0h
RPKM value at 0h in control condition
- CTRL_1_1h
RPKM value at 1h in control condition
- CTRL_1_2h
RPKM value at 2h in control condition
- CTRL_1_4h
RPKM value at 4h in control condition
- CTRL_1_8h
RPKM value at 8h in control condition
- CTRL_1_12h
RPKM value at 12h in control condition
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- KD_1_0h
RPKM value at 0h in knockdown condition
- KD_1_1h
RPKM value at 1h in knockdown condition
- KD_1_2h
RPKM value at 2h in knockdown condition
- KD_1_4h
RPKM value at 4h in knockdown condition
- KD_1_8h
RPKM value at 8h in knockdown condition
- KD_1_12h
RPKM value at 12h in knockdown condition
test BRIC-seq dataset for p-value estimation using grubbs test
Description
A dataset containing the RPKM for each time point and information column about 200 genes. The variables are as follows:
Usage
RNA_halflife_grubbs_test
Format
A data frame with 200 rows and 40 variables:
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- CTRL_1_0h
RPKM value at 0h in control condition
- CTRL_1_1h
RPKM value at 1h in control condition
- CTRL_1_2h
RPKM value at 2h in control condition
- CTRL_1_4h
RPKM value at 4h in control condition
- CTRL_1_8h
RPKM value at 8h in control condition
- CTRL_1_12h
RPKM value at 12h in control condition
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- CTRL_2_0h
RPKM value at 0h in control condition
- CTRL_2_1h
RPKM value at 1h in control condition
- CTRL_2_2h
RPKM value at 2h in control condition
- CTRL_2_4h
RPKM value at 4h in control condition
- CTRL_2_8h
RPKM value at 8h in control condition
- CTRL_2_12h
RPKM value at 12h in control condition
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- CTRL_3_0h
RPKM value at 0h in control condition
- CTRL_3_1h
RPKM value at 1h in control condition
- CTRL_3_2h
RPKM value at 2h in control condition
- CTRL_3_4h
RPKM value at 4h in control condition
- CTRL_3_8h
RPKM value at 8h in control condition
- CTRL_3_12h
RPKM value at 12h in control condition
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- KD_1_0h
RPKM value at 0h in knockdown condition
- KD_1_1h
RPKM value at 1h in knockdown condition
- KD_1_2h
RPKM value at 2h in knockdown condition
- KD_1_4h
RPKM value at 4h in knockdown condition
- KD_1_8h
RPKM value at 8h in knockdown condition
- KD_1_12h
RPKM value at 12h in knockdown condition
BRIC-seq result dataset for p-value estimation using grubbs test
Description
A dataset containing the RPKM for each time point, information column, RNA half-life, R2 and fitting model about 200 genes. The variables are as follows:
Usage
halflife_table
Format
A data frame with 200 rows and 52 variables:
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- T00_1
RPKM value at 0h in control condition
- T01_1
RPKM value at 1h in control condition
- T02_1
RPKM value at 2h in control condition
- T04_1
RPKM value at 4h in control condition
- T08_1
RPKM value at 8h in control condition
- T12_1
RPKM value at 12h in control condition
- Model
RNA decay fitting model
- R2
R2 for fitting curve
- half_life
RNA half-life
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- T00_2
RPKM value at 0h in control condition
- T01_2
RPKM value at 1h in control condition
- T02_2
RPKM value at 2h in control condition
- T04_2
RPKM value at 4h in control condition
- T08_2
RPKM value at 8h in control condition
- T12_2
RPKM value at 12h in control condition
- Model
RNA decay fitting model
- R2
R2 for fitting curve
- half_life
RNA half-life
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- T00_3
RPKM value at 0h in control condition
- T01_3
RPKM value at 1h in control condition
- T02_3
RPKM value at 2h in control condition
- T04_3
RPKM value at 4h in control condition
- T08_3
RPKM value at 8h in control condition
- T12_3
RPKM value at 12h in control condition
- Model
RNA decay fitting model
- R2
R2 for fitting curve
- half_life
RNA half-life
- gr_id
Group id
- symbol
Gene symbol
- accession_id
Gene accession id (RefSeq)
- locus
Genome locus
- T00_4
RPKM value at 0h in knockdown condition
- T01_4
RPKM value at 1h in knockdown condition
- T02_4
RPKM value at 2h in knockdown condition
- T04_4
RPKM value at 4h in knockdown condition
- T08_4
RPKM value at 8h in knockdown condition
- T12_4
RPKM value at 12h in knockdown condition
- Model
RNA decay fitting model
- R2
R2 for fitting curve
- half_life
RNA half-life