| Title: | Local Pooled Error Test for Differential Expression with Paired High-throughput Data |
|---|---|
| Description: | This package performs tests for paired high-throughput data. |
| Authors: | HyungJun Cho <[email protected]> and Jae K. Lee <[email protected]> |
| Maintainer: | Soo-heang Eo <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.65.0 |
| Built: | 2024-07-17 11:59:44 UTC |
| Source: | https://github.com/bioc/PLPE |
This invetigates differential expression for paired high-throughput data.
lpe.paired(x,...)lpe.paired(x,...)
x |
an object for which the extraction of model lpe.paired is meaningful. |
... |
other arguments |
x |
design matrix; condition index in the first column and pair index in the sceond column |
... |
data type: 'ms' for mass spectrometry data, 'cdna' for cDNA microarray data |
HyungJun Cho and Jae K. Lee
Cho H, Smalley DM, Ross MM, Theodorescu D, Ley K and Lee JK (2007). Statistical Identification of Differentially Labelled Peptides from Liquid Chromatography Tandem Mass Spectrometry, Proteomics, 7:3681-3692.
#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out$test.out[1:10,]#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out$test.out[1:10,]
This invetigates differential expression for paired high-throughput data.
## Default S3 method: lpe.paired(x, design, data.type, q=0.01, probe.ID = NULL, estimator="median", w=0.5, w.estimator="fixed", iseed=1234, ...)## Default S3 method: lpe.paired(x, design, data.type, q=0.01, probe.ID = NULL, estimator="median", w=0.5, w.estimator="fixed", iseed=1234, ...)
x |
data matrix |
design |
design matrix; condition index in the first column and pair index in the sceond column |
q |
quantile for intervals of intensities |
probe.ID |
probe set IDs; if NULL, row numbers are assigned. |
data.type |
data type: 'ms' for mass spectrometry data, 'cdna' for cDNA microarray data |
estimator |
specification for the estimator: 'median', 'mean' and 'huber' |
w |
weight paramter between individual variance estimate and pooling variance estimate, 0<= w <=1 |
w.estimator |
two approaches to estimate the weight: 'random' or 'fixed' |
iseed |
seed number |
... |
other arguments |
design |
design matrix; condition index in the first column and pair index in the sceond column |
data.type |
data type: 'ms' for mass spectrometry data, 'cdna' for cDNA microarray data |
q |
quantile for intervals of intensities |
estimator |
specification for the estimator: 'median', 'mean' and 'huber' |
w.estimator |
two approaches to estimate the weight: 'random' or 'fixed' |
w |
weight paramter between individual variance estimate and pooling variance estimate, 0<= w <=1 |
test.out |
matrix for test results |
HyungJun Cho and Jae K. Lee
Cho H, Smalley DM, Ross MM, Theodorescu D, Ley K and Lee JK (2007). Statistical Identification of Differentially Labelled Peptides from Liquid Chromatography Tandem Mass Spectrometry, Proteomics, 7:3681-3692.
#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out$test.out[1:10,] summary(out)#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out$test.out[1:10,] summary(out)
This computes FDR for PLPE.
lpe.paired.fdr(x,...)lpe.paired.fdr(x,...)
x |
data matrix |
... |
other arguments |
HyungJun Cho and Jae K. Lee
Cho H, Smalley DM, Ross MM, Theodorescu D, Ley K and Lee JK (2007). Statistical Identification of Differentially Labelled Peptides from Liquid Chromatography Tandem Mass Spectrometry, Proteomics, 7:3681-3692.
#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out.fdr <- lpe.paired.fdr(x,obj=out) out.fdr$FDR[1:10,]#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out.fdr <- lpe.paired.fdr(x,obj=out) out.fdr$FDR[1:10,]
This computes FDR for PLPE.
## Default S3 method: lpe.paired.fdr(x, obj, n.iter=5, lambda=0.9, ...)## Default S3 method: lpe.paired.fdr(x, obj, n.iter=5, lambda=0.9, ...)
x |
data matrix |
obj |
object created from lpe.paired |
n.iter |
number of iterations |
lambda |
numeric vector of probabilities with values in [0,1] |
... |
other argument |
design |
design matrix; condition index in the first column and pair index in the sceond column |
data.type |
data type: 'ms' for mass spectrometry data, 'cdna' for cDNA microarray data |
estimator |
specification for the estimator: 'median', 'mean' and 'huber' |
w.estimator |
two approaches to estimate the weight: 'random' or 'fixed' |
w |
weight paramter between individual variance estimate and pooling variance estimate, 0<= w <=1 |
pi0 |
estimated proportion of non-null peptides |
FDR |
matrix for test results including FDRs |
... |
other arguments |
HyungJun Cho and Jae K. Lee
Cho H, Smalley DM, Ross MM, Theodorescu D, Ley K and Lee JK (2007). Statistical Identification of Differentially Labelled Peptides from Liquid Chromatography Tandem Mass Spectrometry, Proteomics, 7:3681-3692.
#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out.fdr <- lpe.paired.fdr(x,obj=out) out.fdr$FDR[1:10,]#LC-MS/MS proteomic data for platelets MPs library(PLPE) data(plateletSet) x <- exprs(plateletSet) x <- log2(x) cond <- c(1, 2, 1, 2, 1, 2) pair <- c(1, 1, 2, 2, 3, 3) design <- cbind(cond, pair) out <- lpe.paired(x, design, q=0.1, data.type="ms") out.fdr <- lpe.paired.fdr(x,obj=out) out.fdr$FDR[1:10,]
This data set consists of LC-MS/MS data with three replicates of paired samples.
Garcia BA, Smalley DM, Cho H, Shabanowitz J, Ley K and Hunt DF (2005). The Platelet Microparticle Proteome, Journal of Proteome Research, 4:1516-1521.