| Title: | Tools to assess and compare miRNA expression estimatation methods |
|---|---|
| Description: | Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. |
| Authors: | Matthew N. McCall <[email protected]>, Lauren Kemperman <[email protected]> |
| Maintainer: | Matthew N. McCall <[email protected]> |
| License: | GPL-3 | file LICENSE |
| Version: | 1.35.0 |
| Built: | 2024-09-28 04:12:12 UTC |
| Source: | https://github.com/bioc/miRcomp |
This function assesses the signal detect slope (a measure of accuracy) for each feature.
accuracy(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, bins=3, label1=NULL, label2=NULL)accuracy(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, bins=3, label1=NULL, label2=NULL)
object1 |
a list containing two elements: ct (the expression estimates) and qc (quality scores) |
qcThreshold1 |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
object2 |
an optional second list of the same format as object1, used to compare two methods. |
qcThreshold2 |
a numeric threshold corresponding to object2$qc below which values are considered low quality. |
commonFeatures |
if TRUE and object2 is non-NULL, only high quality non-NA features in common between both objects are used. |
bins |
the number of bins to divide the data into. |
label1 |
optional label corresponding to object 1 to be used in plotting. |
label2 |
optional label corresponding to object 2 to be used in plotting. |
A plot of signal detect slopes stratified by difference in pure sample expression is produced, and a summary (median and MAD) of the signal detect slopes in each bin is returned.
Matthew N. McCall
data(lifetech) accuracy(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) accuracy(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)data(lifetech) accuracy(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) accuracy(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)
This function determines the number of features that are good quality and non-NA across all samples using a given quality threshold.
completeFeatures(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, label1=NULL, label2=NULL)completeFeatures(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, label1=NULL, label2=NULL)
object1 |
a list containing two elements: ct (the expression estiamtes) and qc (quality scores) |
qcThreshold1 |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
object2 |
an optional second list of the same format as object1, used to compare two methods. |
qcThreshold2 |
a numeric threshold corresponding to object2$qc below which values are considered low quality. |
label1 |
optional label corresponding to object 1 to be used in plotting. |
label2 |
optional label corresponding to object 2 to be used in plotting. |
The function generates a table of the number of complete, partial, and absent features.
Matthew N. McCall
data(lifetech) completeFeatures(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) completeFeatures(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)data(lifetech) completeFeatures(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) completeFeatures(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)
This function allows one to easily compare the expression estimates generated by two different algorithms. Feature / sample combinations that differ above a given threshold can be easily highlighted.
expressionComp(object1, qcThreshold1, object2, qcThreshold2, label1=NULL, label2=NULL, diffThreshold=2, plotType=c("scatterplot","MAplot"))expressionComp(object1, qcThreshold1, object2, qcThreshold2, label1=NULL, label2=NULL, diffThreshold=2, plotType=c("scatterplot","MAplot"))
object1 |
a list containing two elements: ct (the expression estimates) and qc (quality scores) |
qcThreshold1 |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
object2 |
a list containing two elements: ct (the expression estimates) and qc (quality scores) |
qcThreshold2 |
a numeric threshold corresponding to object2$qc below which values are considered low quality. |
label1 |
an optional label for plotting on the x-axis |
label2 |
an optional label for plotting on the y-axis |
diffThreshold |
feature / sample combinations for which the expression estimates differ by more than this value are returned by the function and highlighted in the plot. |
plotType |
scatterplot or MA-plot |
This function plots the expresison estimates produced by two different methods. Feature / sample combinations for which the expression estimates differ by more than diffThreshold are returned by the function and highlighted in the plot.
Matthew N. McCall
data(lifetech) data(qpcRdefault) tmp <- expressionComp(object1=lifetech, qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99,plotType="scatter")data(lifetech) data(qpcRdefault) tmp <- expressionComp(object1=lifetech, qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99,plotType="scatter")
These data are also used to demonstrate the functionality of the miRcomp package.
data(lifetech)data(lifetech)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here AmpScores) |
data(lifetech)data(lifetech)
This function assesses the limit of detection in one of two ways: (1) the distribution of expression estimates stratified by the proportion of poor quality values within replicates, (2) the average vs expected expression for the two most diluted sample types.
limitOfDetection(object, qcThreshold, plotType=c("boxplot","scatterplot","MAplot"))limitOfDetection(object, qcThreshold, plotType=c("boxplot","scatterplot","MAplot"))
object |
a list containing two elements: ct (the expression estiamtes) and qc (quality scores) |
qcThreshold |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
plotType |
the desired output type – boxplot is option (1); scatterplot is option (2), an MA-plot is option (3). |
This function assesses the limit of detection in several ways. If plotType is boxplot, then boxplots of expression estimates stratified by the proportion of poor quality values within replicates is displayed. The function also outputs a list with the values plotted in each box of the boxplot. If plotType is scatterplot, then the average within replicate expression vs expected expression (based on pure sample expression) is displayed for the 0.1/0.1 dilution and 0.01/0.01 dilution. If plotType is MAplot, then the difference in expression (average within replicate expression - expected expression) is displayed for the 0.1/0.1 dilution and 0.01/0.01 dilution. For both plotTypes, scatterplot and MAplot, the function outputs a matrix containing estimates of the limit of detection for four different tolerances. Specifically, the two columns correspond to the two dilutions (0.1/0.1 and 0.01/0.01) and rows correspond to the median difference between the observed and expected values. The values in the matrix are the expected expression values such that the median absolute difference of all larger expected expression values is approximately equal to the given tolerance.
Matthew N. McCall
data(lifetech) tmp <- limitOfDetection(object=lifetech,qcThreshold=1.25) data(qpcRdefault) limitOfDetection(object=qpcRdefault,qcThreshold=0.99,plotType="scatter")data(lifetech) tmp <- limitOfDetection(object=lifetech,qcThreshold=1.25) data(qpcRdefault) limitOfDetection(object=qpcRdefault,qcThreshold=0.99,plotType="scatter")
This function launches the miRcomp Shiny app locally.
miRcompShinyApp()miRcompShinyApp()
Matthew N. McCall and Lauren Kemperman
## miRcompShinyApp()## miRcompShinyApp()
This function assesses the within-replicate precision for each feature.
precision(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, statistic=c("sd","cv"), scale=c("none","log","log10"), bins=3, label1=NULL, label2=NULL)precision(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, statistic=c("sd","cv"), scale=c("none","log","log10"), bins=3, label1=NULL, label2=NULL)
object1 |
a list containing two elements: ct (the expression estiamtes) and qc (quality scores) |
qcThreshold1 |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
object2 |
an optional second list of the same format as object1, used to compare two methods. |
qcThreshold2 |
a numeric threshold corresponding to object2$qc below which values are considered low quality. |
commonFeatures |
if TRUE and object2 is non-NULL, only high quality non-NA features in common between both objects are used. |
statistic |
whether to compute the standard deviation (sd) or coefficient of variation (cv). |
scale |
optional scaling of the values. This can help with visualizing the distributions. |
bins |
the number of bins to divide the data into. |
label1 |
optional label corresponding to object 1 to be used in plotting. |
label2 |
optional label corresponding to object 2 to be used in plotting. |
A boxplot of either the standard deviation or coefficient of variation stratified by expression is produced. The values plotted in each box of the boxplot are returned.
Matthew N. McCall
data(lifetech) tmp1 <- precision(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) tmp2 <- precision(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)data(lifetech) tmp1 <- precision(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) tmp2 <- precision(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)
These data are also used to demonstrate the functionality of the miRcomp package.
data(qpcRb4)data(qpcRb4)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here R-squared values) |
data(qpcRb4)data(qpcRb4)
These data are also used to demonstrate the functionality of the miRcomp package.
data(qpcRb5)data(qpcRb5)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here R-squared values) |
data(qpcRb5)data(qpcRb5)
These data are also used to demonstrate the functionality of the miRcomp package.
data(qpcRdefault)data(qpcRdefault)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here R-squared values) |
data(qpcRdefault)data(qpcRdefault)
These data are also used to demonstrate the functionality of the miRcomp package.
data(qpcRl5)data(qpcRl5)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here R-squared values) |
data(qpcRl5)data(qpcRl5)
These data are also used to demonstrate the functionality of the miRcomp package.
data(qpcRlinexp)data(qpcRlinexp)
A list with two elements.
| ct | a miRNA x sample matrix of expression estimates |
| qc | a miRNA x sample matrix of quality scores (here R-squared values) |
data(qpcRlinexp)data(qpcRlinexp)
This function produces several figures to examine quality scores.
qualityAssessment(object1, object2=NULL, cloglog1=FALSE, cloglog2=FALSE, na.rm=FALSE, plotType=c("scatterplot","boxplot"), label1=NULL, label2=NULL)qualityAssessment(object1, object2=NULL, cloglog1=FALSE, cloglog2=FALSE, na.rm=FALSE, plotType=c("scatterplot","boxplot"), label1=NULL, label2=NULL)
object1 |
a list containing two elements: ct (the expression estiamtes) and qc (quality scores) |
object2 |
an optional second list of the same format as object1, used to compare two methods. |
cloglog1 |
if TRUE, the -log(-log(object1$qc)) is plotted. This is useful to visualize certain quality scores such as R-squared. |
cloglog2 |
if TRUE, the -log(-log(object2$qc)) is plotted. This is useful to visualize certain quality scores such as R-squared. |
na.rm |
if TRUE, quality scores corresponding to NA expression estimates are removed from analysis. This is only used if plotType=="boxplot". |
plotType |
type of plot to return. Options are a scatterplot or a boxplot. |
label1 |
optional label corresponding to object 1 to be used in plotting. |
label2 |
optional label corresponding to object 2 to be used in plotting. |
The function generates a plot of the desired type.
Matthew N. McCall
data(lifetech) qualityAssessment(lifetech, plotType="boxplot") data(qpcRdefault) qualityAssessment(object1=lifetech,object2=qpcRdefault,cloglog2=TRUE)data(lifetech) qualityAssessment(lifetech, plotType="boxplot") data(qpcRdefault) qualityAssessment(object1=lifetech,object2=qpcRdefault,cloglog2=TRUE)
This function determines
titrationResponse(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, label1=NULL, label2=NULL)titrationResponse(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL, commonFeatures=TRUE, label1=NULL, label2=NULL)
object1 |
a list containing two elements: ct (the expression estiamtes) and qc (quality scores) |
qcThreshold1 |
a numeric threshold corresponding to object1$qc below which values are considered low quality. |
object2 |
an optional second list of the same format as object1, used to compare two methods. |
qcThreshold2 |
a numeric threshold corresponding to object2$qc below which values are considered low quality. |
commonFeatures |
if TRUE and object2 is non-NULL, only high quality non-NA features in common between both objects are used. |
label1 |
optional label corresponding to object 1 to be used in plotting. |
label2 |
optional label corresponding to object 2 to be used in plotting. |
A table listing the number of features showing a monotone titration response and a figure showing the proportion of features showing a monotone titration response vs the difference in pure sample expression.
Matthew N. McCall
data(lifetech) titrationResponse(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) titrationResponse(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)data(lifetech) titrationResponse(object1=lifetech,qcThreshold1=1.25) data(qpcRdefault) titrationResponse(object1=lifetech,qcThreshold1=1.25, object2=qpcRdefault,qcThreshold2=0.99)