Help for package SIEVEseq

Title:

Unified Differential Expression, Variability and Skewness Analysis for RNA-Seq Data

Version:

0.0.0

Date:

2026-06-07

Maintainer:

Hongxiang Li <hxli@ynnu.edu.cn>

Description:

Provides a unified framework for the simultaneous testing of differential expression, variability, and skewness of genes using RNA-Seq data. The framework adopts a compositional data analysis approach for modelling RNA-Seq count data, applies the centered log-ratio transformation to obtain continuous variables, and uses the skew-normal distribution for statistical inference. Methods are described in Li and Khang (bioRxiv preprint, 2024, version 3) <doi:10.1101/2024.04.09.588804>.

Depends:

R (≥ 4.1)

Imports:

sn, stats, ggplot2

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

URL:

https://github.com/Divo-Lee/SIEVEseq

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Encoding:

UTF-8

LazyData:

TRUE

Config/roxygen2/version:

8.0.0

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-06-22 14:21:19 UTC; chels

Author:

Hongxiang Li [aut, cre], Tsung Fei Khang [aut]

Repository:

CRAN

Date/Publication:

2026-06-25 16:10:09 UTC

Distribution of CLR-transformed counts

Description

Produce a histogram of observed CLR-transformed counts, with the fitted skew-normal probability density function for a particular gene/transcript.

Usage

SN.plot(data)

Arguments

data

CLR-transformed counts for a particular gene/transcript.

Value

No return value. This function is called for its side effect of generating a plot showing the observed CLR-transformed counts and the fitted skew-normal density.

Examples

   library(SIEVEseq)
   data("clrCounts1")
   SN.plot(clrCounts1[1,])
   SN.plot(clrCounts1[2,])
   SN.plot(clrCounts1[3,])

Fit the skew-normal distribution to CLR-transformed RNA-Seq data

Description

Estimate the mean, standard deviation, and skewness parameters of the skew-normal distribution using centered log-ratio (CLR) transformed RNA-Seq data.

Usage

clr.SN.fit(data)

Arguments

data

A table of CLR-transformed count data, with genes/transcripts on the rows and samples on columns.

Value

mu

The maximum likelihood estimate of the mean parameter.

se.mu

The standard error of the maximum likelihood estimate of mu.

z.mu

The Wald statistic for mu.

p.mu

The p-value of the Wald statistic for mu.

sigma

The maximum likelihood estimate of the standard deviation parameter.

se.sigma

The standard error of the maximum likelihood estimate of sigma.

z.sigma

The Wald statistic for sigma.

p.sigma

The p-value of the Wald statistic for sigma.

gamma

The maximum likelihood estimate of the skewness parameter.

se.gamma

The standard error of the maximum likelihood estimate of gamma.

z.gamma

The Wald statistic for gamma.

p.gamma

The p-value of the Wald statistic for gamma.

References

Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics 12(2), 171–178, JSTOR

Azzalini, A. and Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.

Azzalini, A. and Arellano-Valle, R. B. (2013). Maximum penalized likelihood estimation for skew-normal and skew-t distributions. Journal of Statistical Planning and Inference 143, 419–433.

Azzalini, A. (2022). The R package sn: The skew-normal and related distribution such as the skew-t and the SUN (version 2.0.2). Universit\'a degli Studi di Padova, Italia. Home page: https://cran.r-project.org/package=sn.

Aitchison, J. (1986). The Statistical Analysis of Compositional Data. Chapman & Hall, London.

Examples

 library(SIEVEseq)
 data(clrCounts1)
 clr.SN.fit(clrCounts1[1:2, ])
 clr.SN.fit(clrCounts1[1, ])

Simulated CLR-transformed count table

Description

A simulated CLR-transformed count table with genes on the rows and samples on the columns for one condition.

Usage

data(clrCounts1)

Format

A data frame containing CLR-transformed counts, with 200 genes and 500 samples.

Simulated DV genes CLR-transformed count table

Description

A simulated CLR-transformed count table with genes on the rows and samples on the columns for two groups. Ten percent of the genes are DV genes (gene1 to gene50).

Usage

data(clrCounts2)

Format

A data frame containing CLR-transformed counts, with 500 genes and 200 samples per group.

Simulated DE genes CLR-transformed count table

Description

A simulated CLR-transformed count table with genes on the rows and samples on the columns for two groups. Ten percent of the genes are DE genes (gene1 to gene50).

Usage

data(clrCounts3)

Format

A data frame containing CLR-transformed counts, with 500 genes and 200 samples per group.

Differential Expression (DE) Test of CLR-transformed RNA-Seq Data

Description

Model CLR-transformed RNA-Seq data using the skew-normal distribution, and then conduct a statistical test for finding genes/transcripts with differential expression using the Wald test.

Usage

clrDE(data = NULL, group = NULL)

Arguments

data

CLR-transformed counts matrix with genes/transcripts on the rows and samples on the columns

group

2 groups (control vs. treatment)

Value

An object of 'clrDE' class that contains the results of the DV test and associated information:

DE

The difference of mu between group 2 and group 1 (mu2 - mu1).

se

The standard error of DE.

z

The observed Wald statistic.

pval

The unadjusted p-value of the Wald test.

adj_pval

The p-value of the Wald test adjusted using the Benjamini-Yekutieli procedure.

mu1

The maximum likelihood estimate of the mean parameter of group 1.

se.mu1

The standard error of the maximum likelihood estimate of mu1.

z.mu1

The Wald statistic for mu1.

p.mu1

The p-value of the Wald test for mu1.

mu2

The maximum likelihood estimate of the mean parameter of group 2.

se.mu2

The standard error of the maximum likelihood estimate of mu2.

z.mu2

The Wald statistic for mu2.

p.mu2

The p-value of the Wald test for mu2.

Examples

 library(SIEVEseq)
 data(clrCounts3) # The first 50 genes (gene1 to gene50) are DE genes
 groups <- c(rep(0, 200), rep(1, 200))
 clrDE_test <- clrDE(clrCounts3[46:100, ], group = groups)
 sum(is.na(clrDE_test))  # check NA values
 head(clrDE_test, 5) # adj_pval < 0.05, DE genes
 tail(clrDE_test, 5) # adj_pval > 0.05, non-DE genes

Differential variability (DV) test of CLR-transformed RNA-Seq data

Description

Model CLR-transformed RNA-Seq data using the skew-normal distribution, and then conduct a statistical test for finding genes/transcripts with differential variability using the Wald test.

Usage

clrDV(data = NULL, group = NULL)

Arguments

data

A CLR-transformed count matrix with genes/transcripts on the rows and samples on the columns.

group

A vector specifying the group labels of the data.

Value

An object of 'clrDV' class that contains the results of the DV test and associated information:

DV

The difference of standard deviation (sigma) between group 2 and group 1 (sigma2 - sigma1).

se

The standard error of DV.

z

The observed Wald statistic.

pval

The unadjusted p-value of Wald test.

adj_pval

The p-value of the Wald test adjusted using the Benjamini-Yekutieli procedure.

sigma1

The maximum likelihood estimate of the standard deviation parameter for group 1.

se.sigma1

The standard error of the maximum likelihood estimate of sigma1.

z.sigma1

The Wald statistic for sigma1.

p.sigma1

The p-value of the Wald test for sigma1.

sigma2

The maximum likelihood estimate of the standard deviation parameter for group 2.

se.sigma2

The standard error of the maximum likelihood estimate of gamma2.

z.sigma2

The Wald statistic for sigma2.

p.sigma2

The p-value of the Wald test for sigma2.

Examples

   library(SIEVEseq)
   data(clrCounts2) # first 50 genes (gene1 to gene50) are DV genes
   groups <- c(rep(0, 200), rep(1, 200))
   clrDV_test <- clrDV(clrCounts2[46:100, ], group = groups)
   sum(is.na(clrDV_test))  # check NA values
   head(clrDV_test, 5)  # adj_pval < 0.05, DV genes
   tail(clrDV_test, 5)  # adj_pval > 0.05, non-DV genes

Simultaneous Differential Expression, Variability and Skewness Analysis Using RNA-Seq Data

Description

Model CLR-transformed RNA-Seq data using the skew-normal distribution, and then conduct statistical tests for finding genes/transcripts with differential expression, variability and skewness using the Wald test.

Usage

clrSIEVE(
  clrSeq_result = NULL,
  alpha_level = 0.05,
  order_DE = FALSE,
  order_LFC = FALSE,
  order_DS = FALSE,
  order_sieve = FALSE
)

Arguments

clrSeq_result

The result of clrSeq() function.

alpha_level

The adjusted p-value cutoff used for flagging genes that show significant differential expression, variability and skewness.

order_DE

Logical string. "FALSE" for no ordering; "TRUE" for ordering by the value of DE.

order_LFC

Logical string. "FALSE" for no ordering; "TRUE" for ordering by the value of LFC.

order_DS

Logical string. "FALSE" for no ordering; "TRUE" for ordering by the value of DS.

order_sieve

Character/logical string specifying the order method. Possibilities are "DE" for the value of DE, "LFC" for log2 fold change of variability, "DS" for the value of DS or "FALSE" for no ordering.

Value

clrDE_test, clrDV_test and clrDS_test contain the result of the DE, DV and DS tests, respectively. clrSIEVE_tests contains the result of all three tests.

clrDE_test

A data.frame contating the results of differential expression test.

clrDV_test

A data.frame contating the results of differential variability test.

clrDS_test

A data.frame contating the results of differential skewness test.

clrSIEVE_tests

A data.frame contating the results of differential expression, variability and skewness tests.

DE

The difference of mean (mu) between group 2 and group 1 (mu2 - mu1).

se_DE

The standard error of DE.

z_DE

The observed Wald statistic of DE.

pval_DE

The unadjusted p-value of Wald test of DE.

adj_pval_DE

The p-value of the Wald test of DE, adjusted using the Benjamini-Yekutieli procedure.

mu1

The maximum likelihood estimate of the mean parameter for group 1.

mu2

The maximum likelihood estimate of the mean parameter for group 2.

de_indicator

1: DE gene; 0: non-DE gene.

SD_ratio

The ratio of standard deviation (sigma) between group 2 and group 1 (sigma2/sigma1).

LFC

log2 fold change: log2(sigma2/sigma1).

DV

The difference of standard deviation (sigma) between group 2 and group 1 (sigma2 - sigma1).

se_DV

The standard error of DV.

z_DV

The observed Wald statistic of DV.

pval_DV

The unadjusted p-value of Wald test of DV.

adj_pval_DV

The p-value of the Wald test of DV, adjusted using the Benjamini-Yekutieli procedure.

sigma1

The maximum likelihood estimate of the standard deviation parameter for group 1.

sigma2

The maximum likelihood estimate of the standard deviation parameter for group 2.

dv_indicator

1: DV gene; 0: non-DV gene.

DS

The difference of skewness parameter (gamma) between group 2 and group 1 (gamma2 - gamma1.).

se_DS

The standard error of DS.

z_DS

The observed Wald statistic of DS.

pval_DS

The unadjusted p-value of Wald test of DS.

adj_pval_DS

The p-value of the Wald test of DS adjusted using the Benjamini-Yekutieli procedure.

gamma1

The maxiimum likelihood estimate of the skewness parameter for group 1.

gamma2

The maxiimum likelihood estimate of the skewness parameter for group 2.

ds_indicator

1: DS gene; 0: non-DS gene.

References

Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics 12(2), 171–178, JSTOR

Azzalini, A. and Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.

Azzalini, A. and Arellano-Valle, R. B. (2013). Maximum penalized likelihood estimation for skew-normal and skew-t distributions. Journal of Statistical Planning and Inference 143, 419–433.

Aitchison, J. (1986). The Statistical Analysis of Compositional Data. Chapman & Hall, London.

Examples

library(SIEVEseq)
data(clrCounts3) # first 50 genes (gene1 to gene50) are DE genes
groups <- c(rep(0, 200), rep(1, 200))
clrSeq_result3 <- clrSeq(clrCounts3[46:100, ], group = groups) # DE dataset
clrSIEVE_result3 <- clrSIEVE(clrSeq_result = clrSeq_result3,
                             alpha_level = 0.05,
                             order_DE = FALSE,
                             order_LFC = FALSE,
                             order_DS = FALSE,
                             order_sieve = FALSE)
clrDE_test3 <- clrSIEVE_result3$clrDE_test # DE test
head(clrDE_test3, 5)
clrDS_test3 <- clrSIEVE_result3$clrDS_test # DS test
clrDS_test3[clrDS_test3$adj_pval_DS < 0.05, ]
clrSIEVE_tests3 <- clrSIEVE_result3$clrSIEVE_tests # Sieve DE, DV and DS genes
head(clrSIEVE_tests3, 5)
tail(clrSIEVE_tests3, 5)

Fit the skew-normal model to CLR-transformed RNA-Seq data for 2 groups

Description

Estimate the mean, standard deviation, and skewness parameters of the skew-normal distribution using centered log-ratio (CLR) transformed RNA-Seq data for 2 groups. The related computational works (standard error, Wald statistic, and p value) are also provided.

Usage

clrSeq(data = NULL, group = NULL)

Arguments

data

A table of CLR-transformed count data, with genes/transcripts on the rows and samples on the columns for 2 groups.

group

A vector specifying the group labels of the data.

Value

mu1

The maximum likelihood estimate of the mean parameter of group 1.

se.mu1

The standard error of the maximum likelihood estimate of mu1.

z.mu1

The Wald statistic for mu1.

p.mu1

The p-value of the Wald test for mu1.

sigma1

The maximum likelihood estimate of the standard deviation parameter of group 1.

se.sigma1

The standard error of the maximum likelihood estimate of sigma1.

z.sigma1

The Wald statistic for sigma1.

p.sigma1

The p-value of the Wald test for sigma1.

gamma1

The maximum likelihood estimate of the skewness parameter of group 1.

se.gamma1

The standard error of the maximum likelihood estimate of gamma1.

z.gamma1

The Wald statistic for gamma1.

p.gamma1

The p-value of the Wald test for gamma1.

mu2

The maximum likelihood estimate of mean parameter of group 2.

se.mu2

The standard error of the maximum likelihood estimate of mu2.

z.mu2

The Wald statistic for mu2.

p.mu2

The p-value of the Wald test for mu2.

sigma2

The maximum likelihood estimate of the standard deviation parameter of group 2.

se.sigma2

The standard error of the maximum likelihood estimate of sigma2.

z.sigma2

The Wald statistic for sigma2.

p.sigma2

The p-value of the Wald test for sigma2.

gamma2

The maximum likelihood estimate of the skewness parameter of group 2.

se.gamma2

The standard error of the maximum likelihood estimate of gamma2.

z.gamma2

The Wald statistic for gamma2.

p.gamma2

The p-value of the Wald test for gamma2.

References

Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics 12(2), 171–178, JSTOR

Azzalini, A. and Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.

Azzalini, A. and Arellano-Valle, R. B. (2013). Maximum penalized likelihood estimation for skew-normal and skew-t distributions. Journal of Statistical Planning and Inference 143, 419–433.

Aitchison, J. (1986). The Statistical Analysis of Compositional Data. Chapman & Hall, London.

Examples

   library(SIEVEseq)
   data("clrCounts2")
   groups <- c(rep(0, 200), rep(1, 200))
   clrSeq_result <- clrSeq(clrCounts2[46:100, ], group = groups)
   tail(clrSeq_result, 5)
   SN.plot(clrCounts2[1, 1:200])
   clrSeq_result[1, c("mu1", "sigma1", "gamma1")]

Violin plots

Description

Produce violin plots of CLR-transformed count data for two or three groups.

Usage

violin.plot.SIEVE(
  data = NULL,
  name.gene = NULL,
  group = NULL,
  group.names = NULL,
  xlab = "CLR-transformed count",
  ylab = "Condition"
)

Arguments

data

A CLR-transformed count table.

name.gene

Gene/transcript name.

group

A vector specifying the group labels of the data.

group.names

A vector specifying the group names.

xlab

Name of the x-axis.

ylab

Name of the y-axis.

Value

A ggplot object displaying the distribution of CLR-transformed expression values for the specified gene across groups.

Examples

  library(SIEVEseq)
  data(clrCounts2)  # first 50 genes (gene1 to gene50) are DV genes
  data(clrCounts3)  # first 50 genes (gene1 to gene50) are DE genes
  group0 <- c(rep(0, 200), rep(1, 200))
  group1 <- c(rep(0, 200), rep(1, 100), rep(2, 100))
  violin.plot.SIEVE(data = clrCounts2, "gene1", group = group0,
                group.names = c("control", "case")) # DV
  violin.plot.SIEVE(data = clrCounts2, "gene1", group = group1,
                group.names = c("control", "case1", "case2")) # DV
  violin.plot.SIEVE(data = clrCounts3, "gene1", group = group0,
                group.names = c("control", "case")) # DE
  violin.plot.SIEVE(data = clrCounts3, "gene2", group = group0,
                group.names = c("control", "case")) # DE
  violin.plot.SIEVE(data = clrCounts3, "gene200", group = group0,
                group.names = c("control", "case")) # non-DE

Package {SIEVEseq}

Distribution of CLR-transformed counts

Description

Usage

Arguments

Value

Examples

Fit the skew-normal distribution to CLR-transformed RNA-Seq data

Description

Usage

Arguments

Value

References

Examples

Simulated CLR-transformed count table

Description

Usage

Format

Simulated DV genes CLR-transformed count table

Description

Usage

Format

Simulated DE genes CLR-transformed count table

Description

Usage

Format

Differential Expression (DE) Test of CLR-transformed RNA-Seq Data

Description

Usage

Arguments

Value

See Also

Examples

Differential variability (DV) test of CLR-transformed RNA-Seq data

Description

Usage

Arguments

Value

See Also

Examples

Simultaneous Differential Expression, Variability and Skewness Analysis Using RNA-Seq Data

Description

Usage

Arguments

Value

References

See Also

Examples

Fit the skew-normal model to CLR-transformed RNA-Seq data for 2 groups

Description

Usage

Arguments

Value

References

See Also

Examples

Violin plots

Description

Usage

Arguments

Value

Examples