Package 'Anaquin'

Create conjoint plots

Description

Create scatter plot for conjoint sequins.

Usage

plotConjoint(seqs, units, x, y, title=NULL, xlab=NULL, ylab=NULL)

Arguments

seqs	Sequin names
units	Copy units
x	Expected copy number on the x-axis
y	Measued abundance on the y-axis
title	Label of the plot. Default to NULL.
xlab	Label for the x-axis. Default to NULL.
ylab	Label for the y-axis. Default to NULL.

Details

This is an experimental function for the conjoint sequins, and thus might not be fully utilized.

Value

This function does not return anything.

Author(s)

Ted Wong [email protected]

---

Plot linear model for sequins

Description

Create linear model for sequins, between input concentation on the x-axis and measurment on the y-axis.

Usage

plotLinear(seqs, x, y, std, title, xlab, ylab, showSD, showLOQ, showStats,
           xBreaks, yBreaks, errors, showLinear, showAxis)

Arguments

seqs	Sequin names
x	Input concentration on the x-axis
y	Measurement on the y-axis
std	Standard deviation. (Default to NULL).
title	Label of the plot. (Default to NULL).
xlab	Label for the x-axis. (Default to NULL).
ylab	Label for the y-axis. (Default to NULL).
xBreaks	Breaks for the x-axis. (Default to NULL).
yBreaks	Breaks for the y-axis. (Default to NULL).
showSD	Display vertical standard deviation bars. (Default to FALSE).
showLOQ	Display limit-of-quantification? Default to TRUE.
showStats	Display regression statistics? Default to TRUE.
errors	How errors bar should be calculated. SD or Range.
showLinear	Display regression line. (Default to TRUE).
showAxis	Display x-axis and y-axis. (Default to TRUE).

Details

The plotLinear function plots a scatter plot with input concentration on the x-axis, and measurement on the y-axis. The input concentration is typically the concentration level in ladder mixture, although other measures (such as expected copy number) are also possible. The function builds a linear regression between the two variables, and reports associated statistics (R2, correlation and regression parameters) on the plot.

The function also estimates limit-of-quantification (LOQ) breakpoint, and reports it on the plot if found. LOQ is defined as the lowest empirical detection limit, a threshold value beyond which stochastic behavior occur. LOQ is estimated by fitting segmented linear regression with two segments on the entire data set, while minimizing the total sum of squares of the differences between the variables.

Value

The function prints a scatter plot and return it's LOQ statistics.

Author(s)

Ted Wong [email protected]

Examples

library(Anaquin)

#
# Data set generated by Cufflinks and Anaquin. described in Section 5.4.6.3 of
# the user guide.
#
data(UserGuideData_5.4.6.3)

title <- 'Gene Expression'
xlab  <- 'Input Concentration (log2)'
ylab  <- 'FPKM (log2)'

# Sequin names
seqs <- row.names(UserGuideData_5.4.6.3)

# Input concentration
x <- log2(UserGuideData_5.4.6.3$Input)

# Measured FPKM
y <- log2(UserGuideData_5.4.6.3[,2:4])

plotLinear(seqs, x, y, title=title, xlab=xlab, ylab=ylab, showLOQ=TRUE)

---

Create Limit-of-Detection Ratio (LOD) plot

Description

Create Limit-of-Detection Ratio (LOD) plot between measured abundance (x-axis) and p-value probability (y-axis).

Usage

plotLOD(measured, pval, ratio, qval, FDR, title, xlab, ylab, legTitle, showConf)

Arguments

measured	Measured abundance
pval	P-value probability
ratio	How to group ROC points
qval	Q-value probability. (Default to NULL).
FDR	Chosen false-discovery-rate. Default to NULL).
title	Title of the plot. (Default to NULL).
xlab	Label for the x-axis. (Default to NULL).
ylab	Label for the y-axis. (Default to NULL).
legTitle	Title for the legend. (Default to 'Ratio').
showConf	Display confidence interval. (Default to FALSE).

Details

Create a Limit-of-Detection Ratio (LOD) plot between measured abundance (x-axis) and p-value probability (y-axis).

The LOD plot indicates the confidence in measurement relative to the magnitude of the measurement. For example, p-value should converge to zero as the sequencing depth increases.

The function also fits non-parametric curves for each sequin ratio group. The curves are modelled with local regression analysis, and are colored by the sequin group.

plotLODR is a simplification from the ERCC dashboard R-package. Further details on the statistical algorithm is available in the ERCC documentation at https://bioconductor.org/packages/release/bioc/html/erccdashboard.html.

Value

The function prints a LODR plot and return associated statistics.

Author(s)

Ted Wong [email protected]

Examples

library(Anaquin)

#
# Data set generated by DESeq2 and Anaquin. described in Section 5.6.3.3 of
# the user guide.
#
data(UserGuideData_5.6.3)

xlab  <- 'Average Counts'
ylab  <- 'P-value'
title <- 'LOD Curves'

# Sequin names
seqs <- row.names(UserGuideData_5.6.3)

# Expected log-fold
group <- UserGuideData_5.6.3$ExpLFC

# Measured average abundance
measured <- UserGuideData_5.6.3$Mean

# P-value
pval <- UserGuideData_5.6.3$Pval

# Q-value
qval <- UserGuideData_5.6.3$Qval

plotLOD(measured, pval, group, qval, xlab=xlab, ylab=ylab, title=title, FDR=0.1)

---

Plot logistic model for sequins

Description

Create a scatter plot with input concentration on the x-axis, and measured proportion on the y-axis.

Usage

plotLogistic(seqs, x, y, title, xlab, ylab, showLOA, threshold)

Arguments

seqs	Sequin names
x	Expected input concentration on the x-axis
y	Measured proportion on the y-axis
title	Title of the plot. (Default to NULL).
xlab	Label for the x-axis. (Default to NULL).
ylab	Label for the y-axis. (Default to NULL).
showLOA	Display limit-of-assembly. (Default to TRUE).
threshold	Threshold required for limit-of-assembly (LOA). (Default to 0.7).

Details

The plotLogistic function creates a scatter plot with input concentration on the x-axis, and measured proportion on the y-axis. Common measured statistics include p-value, percentage and sensitivity. The plot builds a logistic regression model between the two variables.

The function also estimates limit-of-assembly (LOA) breakpoint, and reports it on the plot if found. The LOA breakpoint is an empirical detection limit, and also the abundance whereby the fitted logistic curve exceeds a user-defined threshold.

Value

The function returns the limit of quantification.

Author(s)

Ted Wong [email protected]

Examples

library(Anaquin)

#
# Data set generated by Cufflinks and Anaquin. described in Section 5.4.5.1 of
# the user guide.
#
data(UserGuideData_5.4.5.1)

title <- 'Assembly Plot'
xlab  <- 'Input Concentration (log2)'
ylab  <- 'Sensitivity'

# Sequin names
seqs <- row.names(UserGuideData_5.4.5.1)

# Input concentration
x <- log2(UserGuideData_5.4.5.1$Input)

# Measured sensitivity
y <- UserGuideData_5.4.5.1$Sn

plotLogistic(seqs, x, y, title=title, xlab=xlab, ylab=ylab, showLOA=TRUE)

---

Create ROC plot

Description

Create receiver operating characteristic (ROC) plot at various threshold settings.

Usage

plotROC(seqs, score, group, label, refGroup, title, legTitle)

Arguments

seqs	Sequin names
score	How to rank ROC points
group	How to group ROC points
label	True-positive (TP) or false positive (FP)
refGroup	Reference ratio groups
title	Label of the plot. Default to NULL.
legTitle	Title of the legend. Default to Ratio.

Details

Create a receiver operating characteristic (ROC) plot at various threshold settings. The true positive rate (TPR) is plotted on the x-axis and false positive rate (FPR) is plotted on the y-axis.

The function requires a scoring threshold function, and illustrates the performance of the data as the threshold is varied. Common scoring threshold include p-value, sequencing depth and allele frequency, etc.

ROC plot is a useful diagnostic performance tool; it provides tools to select possibly optimal models and to discard suboptimal ones. In particularly, the AUC statistics indicate the performance of the model relatively to a random experiment (AUC 0.5).

Value

The function prints ROC plot and return it's AUC statistics.

Author(s)

Ted Wong [email protected]

Examples

library(Anaquin)

#
# Data set generated by DESeq2 and Anaquin. described in Section 5.6.3.3 of
# the user guide.
#
data(UserGuideData_5.6.3)

# Sequin names
seqs <- row.names(UserGuideData_5.6.3)

# Expected log-fold
group <- abs(UserGuideData_5.6.3$ExpLFC)

# How the ROC curves are ranked
score <- 1-UserGuideData_5.6.3$Pval

# Classified labels (TP/FP)
label <- UserGuideData_5.6.3$Label

plotROC(seqs, score, group, label, title='ROC Plot', refGroup=0)

---

RnaQuin mixture (gene level)

Description

Individual sequins are combined across a range of precise concentrations to formulate mixtures. By modulating the concentration at which each sequin is present in the mixture, we can emulate quantitative features of genome biology.

This is the mixture A and B in RnaQuin. File name is A.R.6.csv on http://www.sequins.xyz.

Usage

data(RnaQuinGeneMixture)

Format

Data frame:

• Name: Sequin name

• Length: Gene length

• MixA: Input concentration for mixture A

• MixB: Input concentration for mixture B

Value

Data frame with columns defined in Format.

---

RnaQuin mixture (isoform level)

Description

Individual sequins are combined across a range of precise concentrations to formulate mixtures. By modulating the concentration at which each sequin is present in the mixture, we can emulate quantitative features of genome biology.

This is the mixture A and B in RnaQuin. File name is A.R.5.csv on http://www.sequins.xyz.

Usage

data(RnaQuinIsoformMixture)

Format

Data frame:

• Name: Sequin name

• Length: Sequin length

• MixA: Input concentration for mixture A

• MixB: Input concentration for mixture B

Value

Data frame with columns defined in Format.

---

Section 5.4.5.1 Assembly Dataset

Description

Assembly sensitivity estimated by Cuffcompare. Section 5.4.5.1 of the Anaquin user guide has details on the data set.

Usage

data(UserGuideData_5.4.5.1)

Format

Data frame:

• InputConcent: Input concentration in attomol/ul

• Sn: Measured sensitivity

Value

Data frame with columns defined in Format.

Source

S.A Hardwick. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nature Methods, 2016.

---

Gene expression (RnaQuin)

Description

Gene expression estimated by Cufflinks. Section 5.4.6.3 of the Anaquin user guide has details on the data set.

Usage

data(UserGuideData_5.4.6.3)

Format

Data frame:

• InputConcent: Input concentration in attomol/ul

• Observed1: Measured FPKM for the first replicate

• Observed2: Measured FPKM for the second replicate

• Observed3: Measured FPKM for the third replicate

Value

Data frame with columns defined in Format.

Source

S.A Hardwick. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nature Methods, 2016.

---

Differential expression (RnaQuin)

Description

Differential gene expression estimated by DESeq2. Section 5.6.3 has details on the data set.

Usage

data(UserGuideData_5.6.3)

Format

Data frame:

• ExpLFC: Expected log-fold change

• ObsLFC: Observed log-fold change

• SD: Standard deviation of the measurment

• Pval: P-value probability

• Qval: Q-value probability

• Mean: Average counts across the samples

• Label: Average counts across the samples

Value

Data frame with columns defined in Format.

Source

S.A Hardwick. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nature Methods, 2016.

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