dryR

dryR (Differential RhythmicitY analysis in R) is an R package that provides the statistical framework to assess differential rhythmicity of a time series of RNA-Seq data with two and more conditions.

Getting Started

These instructions will allow you to get dryR running on your machine.

Prerequisites

You need to install R (see https://www.r-project.org/).

dryR accepts count data typically produced by RNA-Seq with a time dimension and several conditions/groups. The input count data should contain only integer values and be organized in a matrix with rows indicating a specific gene and the column refering to a sample.

The pipelines that can be used to produce these count data from RNA-Seq data (FASTQ files) are described in more detail here: http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

A user-friendly web application to generate a count table from FASTQ files is provided here: https://amp.pharm.mssm.edu/biojupies/

Installing

To install dryR run the following code in R.

install.packages("devtools")
devtools::install_github("naef-lab/dryR")

Quick start

Example dataset

dryR comes with example data in form of a list called simData. The list contains count data simData[["countData"]], a vector with the different conditions/groups simData[["group"]], and a vector indicating Zeitgeber Time simData[["time"]]. The data was generated using simphony https://github.com/hugheylab/simphony.

Running an example

library("dryR")

# prepare arguments
countData = simData[["countData"]]
group     = simData[["group"]]
time      = simData[["time"]]

# prefilter
keep <- rowSums(countData) > 0
countData <- countData[keep,]

# run the analysis for count data (e.g. RNA-Seq data)
dryList   = dryseq(countData,group,time)

# explore the results
dryList[["results"]]     # data frame summarizing results
dryList[["parameters"]]  # coefficients: phase, amplitude and mean for each group
dryList[["ncounts"]]     # normalized counts
dryList[["counts"]]      # raw counts
dryList[["cook"]]        # cook's distance for outlier detection
dryList[["BICW_rhythm"]] # BICW for each rhythmic model
dryList[["BICW_mean"]]   # BICW for each mean model

# generate a pdf with a global summary of all models
plot_models_rhythm(dryList, "./")

# plot a feature of interest
dry_plot(dryList, "feature_113")

Decision Tree for Various Scenarios

The dryR package is equipped to handle a variety of dataset types and experimental designs extending beyond RNA-Seq data with multiple conditions/groups (see example above). The decision tree provided below serves as a guide for selecting the appropriate function to use. For example code, refer to the chapter "Non-standard Scenarios."

Non-standard Scenarios

Rhythmicity detection in RNA-Seq datasets with one condition

To detect rhythmic gene expression in RNA-Seq data with only one condition, we implemented the function dryseq_single.

library("dryR")

# prepare arguments for a one condition scenario
sel = grep("cond_1", simData[["group"]])
countData_single = simData[["countData"]][,sel]
group_single     = simData[["group"]][sel]
time_single      = simData[["time"]][sel]

# run the analysis for count data.
dryList   = dryseq_single(countData_single,group_single,time_single)

# explore the results
dryList[["results"]]    # data frame summarizing results

# plot a feature of interest
plot_single_cond(dryList, "feature_004")

Normally distributed data

To asses temporal variation of normally distributed measurements, we implemented the function drylm that can deal with gaussian noise using linear models.

library("dryR")

# prepare arguments
data      = log(simData[["countData"]]+1)
group     = simData[["group"]]
time      = simData[["time"]]

# run the analysis with normally distributed data
dryList = drylm(data,group,time)

# explore the results
dryList[["results"]]    # data frame summarizing results
dryList[["parameters"]] # coefficients: phase, amplitude and mean for each group

# generate a pdf with a global summary of all models
plot_models_rhythm(dryList, "./")

# plot a feature of interest
dry_plot(dryList, "feature_013")

Normally distributed data with one condition

To detect rhythmicity in normally disributed data with only one condition, we implemented the function f_24.

library("dryR")

# prepare arguments for a one condition scenario
simData_norm = simData
simData[["normData"]] = log(simData[["countData"]]+1)
sel = grep("cond_1", simData[["group"]])
normData_single       = simData[["normData"]][,sel]
time_single           = simData[["time"]][sel]

# run the analysis for count data.
dryList   = f_24(normData_single,time_single)

# explore the results
dryList[["results"]]    # data frame summarizing results

# plot a feature of interest
plot_single_cond_lm(dryList, "feature_004")

DryR with a simple vector as input

You can run drylm with a simple vector that contains data from a time series of multiple groups.

library("dryR")

# define time and group for each sample
time        = c(1:48,1:48)   # Zeitgeber time or Circadian time in h for each sample
group       = c(rep("KO",48), rep("WT",48))

# run the analysis with normally distributed data
dryList = drylm(vector_example,group,time)

# explore the results
dryList[["results"]]    # data frame summarizing results. Row 2 is a copy of row 1.
dryList[["parameters"]] # coefficients: phase, amplitude and mean for each group. Row 2 is a copy of row 1.

#plot the result of the selected model
dry_plot(dryList, "X1")

Help

A documentation using ?dryseq, ?drylm or ?dryseq_single is available.