CuffDiffOptions
Option set for cuffdiff
Description
A CuffDiffOptions
object sets options for the
cuffdiff
function, which identifies significant changes in transcript
expression [1].
Creation
Syntax
Description
creates a
cuffdiffOpt
= CuffDiffOptionsCuffDiffOptions
object with the default property values.
CuffDiffOptions
requires the Cufflinks Support Package for the Bioinformatics Toolbox™. If the support package is not installed, then the function provides a download
link. For details, see Bioinformatics Toolbox Software Support Packages.
sets the object properties using
one or more name-value pair arguments. Enclose each property name in quotes. For example,
cuffdiffOpt
= CuffDiffOptions(Name,Value)cuffdiffOpt = CuffDiffOptions('DoIsoformSwitch',true)
specifies to
perform isoform switching tests.
specifies optional parameters using a string or character vector
cuffdiffOpt
= CuffDiffOptions(S
)S
.
Input Arguments
S
— cuffdiff
options
string | character vector
cuffdiff
options, specified as a string or character vector.
S
must be in the original cuffdiff
option
syntax (prefixed by one or two dashes).
Example: '--seed 5'
Properties
ConditionLabels
— Sample labels
string | string vector | character vector | cell array of character vector
Sample labels, specified as a string, string vector, character
vector, or cell array of character vectors. The number of labels must equal the number of
samples or the value must be empty []
.
Example:
["Control","Mutant1","Mutant2"]
Data Types: string
| char
| cell
ContrastFile
— Contrast file name
string | character vector
Contrast file name, specified as a string or character vector.
The file must be a two-column tab-delimited text file, where each line indicates two conditions
to compare using cuffdiff
. The condition labels in the file must match
either the labels specified for ConditionLabels
or the sample names. The
file must have a single header line as the first line, followed by one line for each contrast.
An example of the contrast file format follows.
condition_A | condition_B |
---|---|
Control | Mutant1 |
Control | Mutant2 |
If you do not provide this file, cuffdiff
compares every
pair of input conditions, which can impact performance.
Example:
"contrast.txt"
Data Types: char
| string
DispersionMethod
— Method to model variance in fragment counts
"pooled"
(default) | "per-condition"
| "blind"
| "poisson"
Method to model the variance in fragment counts across replicates, specified as one of the following options:
"pooled"
— The function uses each replicated condition to build a model and averages these models into a global model for all conditions in the experiment."per-condition"
— The function produces a model for each condition. You can use this option only if all conditions have replicates."blind"
— The function treats all samples as replicates of a single global distribution and produces one model."poisson"
— Variance in fragment counts is a poisson model, where the fragment count is predicted to be the mean across replicates. This method is not recommended.
Select a method depending on whether you expect the variability in each group of samples to be similar.
When comparing two groups where the first group has low cross-replicate variability and the second group has high variability, choose the
per-condition
method.If the conditions have similar levels of variability, choose the
pooled
method.If you have only a single replicate in each condition, choose the
blind
method.
Example:
"blind"
Data Types: char
| string
DoIsoformSwitch
— Flag to perform isoform switching tests
true
(default) | false
Flag to perform isoform switching tests, specified as
true
or false
. These tests estimate how much
differential splicing exists in isoforms from a single primary transcript. By default, the value
is true
and the test results are saved in the output file
splicing.diff
.
Example:
false
Data Types: logical
EffectiveLengthCorrection
— Flag to normalize fragment counts
true
(default) | false
Flag to normalize fragment counts to fragments per kilobase per million mapped reads (FPKM), specified as true
or false
.
Example:
false
Data Types: logical
ExtraCommand
— Additional commands
""
(default) | string | character vector
The commands must be in the native syntax (prefixed by one or two dashes). Use this option to apply undocumented flags and flags without corresponding MATLAB® properties.
When the software converts the original flags to MATLAB properties, it stores any unrecognized flags in this property.
Example: '--library-type fr-secondstrand'
Data Types: char
| string
FalseDiscoveryRate
— False discovery rate
0.05
(default) | scalar between 0
and 1
False discovery rate used during statistical tests, specified as a scalar between 0
and 1
.
Example:
0.01
Data Types: double
FragmentBiasCorrection
— Name of FASTA file with reference transcripts to detect bias
string | character vector
Name of the FASTA file with reference transcripts to detect bias in fragment counts, specified as a string or character vector. Library preparation can introduce sequence-specific bias into RNA-Seq experiments. Providing reference transcripts improves the accuracy of the transcript abundance estimates.
Example:
"bias.fasta"
Data Types: char
| string
FragmentLengthMean
— Expected mean fragment length in base pairs
200
(default) | positive integer
Expected mean fragment length, specified as a positive integer.
The default value is 200
base pairs. The function can learn the fragment
length mean for each SAM file. Using this option is not recommended for paired-end reads.
Example: 100
Data Types: double
FragmentLengthSD
— Expected standard deviation for fragment length distribution
80
(default) | positive scalar
Expected standard deviation for the fragment length
distribution, specified as a positive scalar. The default value is 80
base
pairs. The function can learn the fragment length standard deviation for each SAM file. Using
this option is not recommended for paired-end reads.
Example: 70
Data Types: double
GenerateAnalysisDiff
— Flag to create differential analysis files
true
(default) | false
Flag to create differential analysis files
(*.diff
), specified as true
or
false
.
Example:
false
Data Types: logical
IncludeAll
— Flag to use all object properties
false
(default) | true
Flag to include all the object properties with the
corresponding default values when converting to the original options syntax, specified as
true
or false
. You can convert the properties to the
original syntax prefixed by one or two dashes (such as '-d 100 -e 80'
) by
using getCommand
. The
default value false
means that when you call
getCommand(optionsObject)
, it converts only the specified properties.
If the value is true
, getCommand
converts all available
properties, with default values for unspecified properties, to the original syntax.
Note
If you set IncludeAll
to true
, the software
converts all available properties, using default values for unspecified properties. The
only exception is when the default value of a property is NaN
,
Inf
, []
, ''
, or
""
. In this case, the software does not translate the
corresponding property.
Example: true
Data Types: logical
IsoformShiftReplicates
— Minimum number of replicates to test genes for differential regulation
3
(default) | positive integer
Minimum number of replicates to test genes for differential regulation, specified as a positive integer. The function skips the tests when the number of replicates is smaller than the specified value.
Example:
2
Data Types: double
LengthCorrection
— Flag to correct by transcript length
true
(default) | false
Flag to correct by the transcript length, specified as
true
or false
. Set this value to
false
only when the fragment count is independent of the feature size,
such as for small RNA libraries with no fragmentation and for 3' end sequencing, where all
fragments have the same length.
Example: false
Data Types: logical
LibraryNormalizationMethod
— Method to normalize library size
"geometric"
(default) | "classic-fpkm"
| "quartile"
Method to normalize the library size, specified as one of the following options:
"geometric"
— The function scales the FPKM values by the median geometric mean of fragment counts across all libraries as described in [2]."classic-fpkm"
— The function applies no scaling to the FPKM values or fragment counts."quartile"
— The function scales the FPKM values by the ratio of upper quartiles between fragment counts and the average value across all libraries.
Example:
"classic-fpkm"
Data Types: char
| string
MaskFile
— Name of GTF or GFF file containing transcripts to ignore
string | character vector
Name of the GTF or GFF file containing transcripts to ignore during analysis, specified as a string or character vector. Some examples of transcripts to ignore include annotated rRNA transcripts, mitochondrial transcripts, and other abundant transcripts. Ignoring these transcripts improves the robustness of the abundance estimates.
Example: "excludes.gtf"
Data Types: char
| string
MaxBundleFrags
— Maximum number of fragments to include for each locus before skipping
500000
(default) | positive integer
Maximum number of fragments to include for each locus before
skipping new fragments, specified as a positive integer. Skipped fragments are marked with the
status HIDATA
in the file skipped.gtf
.
Example: 400000
Data Types: double
MaxFragAlignments
— Maximum number of aligned reads to include for each fragment
Inf
(default) | positive integer
Maximum number of aligned reads to include for each fragment
before skipping new reads, specified as a positive integer. Inf
, the default
value, sets no limit on the maximum number of aligned reads.
Example: 1000
Data Types: double
MaxMLEIterations
— Maximum number of iterations for maximum likelihood estimation
5000
(default) | positive integer
Maximum number of iterations for the maximum likelihood estimation of abundances, specified as a positive integer.
Example: 4000
Data Types: double
MinAlignmentCount
— Minimum number of alignments required in locus for significance testing
10
(default) | positive integer
Minimum number of alignments required in a locus to perform the significance testing for differences between samples, specified as a positive integer.
Example:
8
Data Types: double
MinIsoformFraction
— Minimum abundance of isoform to include in differential expression tests
1e-5
(default) | scalar between 0
and 1
Minimum abundance of an isoform to include in differential
expression tests, specified as a scalar between 0
and 1
.
For alternative isoforms quantified at below the specified value, the function rounds down the
abundance to zero. The specified value is a fraction of the major isoform. The function performs
this filtering after MLE estimation but before MAP estimation to improve the robustness of
confidence interval generation and differential expression analysis. Using a parameter value
other than the default is not recommended.
Example: 1e-5
Data Types: double
MultiReadCorrection
— Flag to improve abundance estimation using rescue method
false
(default) | true
Flag to improve abundance estimation for reads mapped to
multiple genomic positions using the rescue method, specified as true
or
false
. If the value is false
, the function divides
multimapped reads uniformly to all mapped positions. If the value is true
,
the function uses additional information, including gene abundance estimation, inferred fragment
length, and fragment bias, to improve transcript abundance estimation.
The rescue method is described in [3].
Example: true
Data Types: logical
NormalizeCompatibleHits
— Flag to use only fragments compatible with reference transcript to calculate FPKM values
true
(default) | false
Flag to use only fragments compatible with a reference
transcript to calculate FPKM values, specified as true
or
false
.
Example: false
Data Types: logical
NormalizeTotalHits
— Flag to include all fragments to calculate FPKM values
false
(default) | true
Flag to include all fragments to calculate FPKM values,
specified as true
or false
. If the value is
true
, the function includes all fragments, including fragments without a
compatible reference.
Example: true
Data Types: logical
NumFragAssignmentDraws
— Number of fragment assignments to perform on each transcript
50
(default) | positive integer
Number of fragment assignments to perform on each transcript, specified as a positive integer. For each fragment drawn from a transcript, the function performs the specified number of assignments probabilistically to determine the transcript assignment uncertainty and to estimate the variance-covariance matrix for the assigned fragment counts.
Example: 40
Data Types: double
NumFragDraws
— Number of draws from negative binomial random number generator
100
(default) | positive integer
Number of draws from the negative binomial random number generator for each transcript, specified as a positive integer. Each draw is a number of fragments that the function probabilistically assigns to transcripts in the transcriptome to determine the assignment uncertainty and to estimate the variance-covariance matrix for assigned fragment counts.
Example: 90
Data Types: double
NumThreads
— Number of parallel threads to use
1
(default) | positive integer
Number of parallel threads to use, specified as a positive integer. Threads are run on separate processors or cores. Increasing the number of threads generally improves the runtime significantly, but increases the memory footprint.
Example: 4
Data Types: double
OutputDirectory
— Directory to store analysis results
current directory ("./"
) (default) | string | character vector
Directory to store analysis results, specified as a string or character vector.
Example: "./AnalysisResults/"
Data Types: char
| string
Seed
— Seed for random number generator
0
(default) | nonnegative integer
Seed for the random number generator, specified as a nonnegative integer. Setting a seed value ensures the reproducibility of the analysis results.
Example: 10
Data Types: double
TimeSeries
— Flag to treat input samples as time series
false
(default) | true
Flag to treat input samples as a time series rather than as
independent experimental conditions, specified as true
or
false
. If you set the value to true
, you must provide
samples in order of increasing time: the first SAM file must be for the first time point, the
second SAM file for the second time point, and so on.
Example:
true
Data Types: logical
Version
— Supported version
string
This property is read-only.
Supported version of the original cufflinks software, returned as a string.
Example: "2.2.1"
Data Types: string
Object Functions
getCommand | Translate object properties to original options syntax |
getOptionsTable | Return table with all properties and equivalent options in original syntax |
Examples
Create CuffDiffOptions Object
Create a CuffDiffOptions
object with the default values.
opt = CuffDiffOptions;
Create an object using name-value pairs.
opt2 = CuffDiffOptions('FalseDiscoveryRate',0.01,'NumThreads',4)
Create an object by using the original syntax.
opt3 = CuffDiffOptions('--FDR 0.01 --num-threads 4')
Assemble Transcriptome and Perform Differential Expression Testing
Create a CufflinksOptions
object to define cufflinks options, such
as the number of parallel threads and the output directory to store the results.
cflOpt = CufflinksOptions;
cflOpt.NumThreads = 8;
cflOpt.OutputDirectory = "./cufflinksOut";
The SAM files provided for this example contain aligned reads for Mycoplasma
pneumoniae from two samples with three replicates each. The reads are
simulated 100bp-reads for two genes (gyrA
and
gyrB
) located next to each other on the genome. All the reads are
sorted by reference position, as required by cufflinks
.
sams = ["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam",... "Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"];
Assemble the transcriptome from the aligned reads.
[gtfs,isofpkm,genes,skipped] = cufflinks(sams,cflOpt);
gtfs
is a list of GTF files that contain assembled isoforms.
Compare the assembled isoforms using cuffcompare
.
stats = cuffcompare(gtfs);
Merge the assembled transcripts using cuffmerge
.
mergedGTF = cuffmerge(gtfs,'OutputDirectory','./cuffMergeOutput');
mergedGTF
reports only one transcript. This is because the two
genes of interest are located next to each other, and cuffmerge
cannot distinguish two distinct genes. To guide cuffmerge
, use a
reference GTF (gyrAB.gtf
) containing information about these two
genes. If the file is not located in the same directory that you run
cuffmerge
from, you must also specify the file path.
gyrAB = which('gyrAB.gtf'); mergedGTF2 = cuffmerge(gtfs,'OutputDirectory','./cuffMergeOutput2',... 'ReferenceGTF',gyrAB);
Calculate abundances (expression levels) from aligned reads for each sample.
abundances1 = cuffquant(mergedGTF2,["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam"],... 'OutputDirectory','./cuffquantOutput1'); abundances2 = cuffquant(mergedGTF2,["Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"],... 'OutputDirectory','./cuffquantOutput2');
Assess the significance of changes in expression for genes and transcripts between
conditions by performing the differential testing using cuffdiff
.
The cuffdiff
function operates in two distinct steps: the function
first estimates abundances from aligned reads, and then performs the statistical
analysis. In some cases (for example, distributing computing load across multiple
workers), performing the two steps separately is desirable. After performing the first
step with cuffquant
, you can then use the binary CXB output file as
an input to cuffdiff
to perform statistical analysis. Because
cuffdiff
returns several files, specify the output directory is
recommended.
isoformDiff = cuffdiff(mergedGTF2,[abundances1,abundances2],... 'OutputDirectory','./cuffdiffOutput');
Display a table containing the differential expression test results for the two genes
gyrB
and gyrA
.
readtable(isoformDiff,'FileType','text')
ans = 2×14 table test_id gene_id gene locus sample_1 sample_2 status value_1 value_2 log2_fold_change_ test_stat p_value q_value significant ________________ _____________ ______ _______________________ ________ ________ ______ __________ __________ _________________ _________ _______ _______ ___________ 'TCONS_00000001' 'XLOC_000001' 'gyrB' 'NC_000912.1:2868-7340' 'q1' 'q2' 'OK' 1.0913e+05 4.2228e+05 1.9522 7.8886 5e-05 5e-05 'yes' 'TCONS_00000002' 'XLOC_000001' 'gyrA' 'NC_000912.1:2868-7340' 'q1' 'q2' 'OK' 3.5158e+05 1.1546e+05 -1.6064 -7.3811 5e-05 5e-05 'yes'
You can use cuffnorm
to generate normalized expression tables for
further analyses. cuffnorm
results are useful when you have many
samples and you want to cluster them or plot expression levels for genes that are
important in your study. Note that you cannot perform differential expression analysis
using cuffnorm
.
Specify a cell array, where each element is a string vector containing file names for a single sample with replicates.
alignmentFiles = {["Myco_1_1.sam","Myco_1_2.sam","Myco_1_3.sam"],... ["Myco_2_1.sam", "Myco_2_2.sam", "Myco_2_3.sam"]} isoformNorm = cuffnorm(mergedGTF2, alignmentFiles,... 'OutputDirectory', './cuffnormOutput');
Display a table containing the normalized expression levels for each transcript.
readtable(isoformNorm,'FileType','text')
ans = 2×7 table tracking_id q1_0 q1_2 q1_1 q2_1 q2_0 q2_2 ________________ __________ __________ __________ __________ __________ __________ 'TCONS_00000001' 1.0913e+05 78628 1.2132e+05 4.3639e+05 4.2228e+05 4.2814e+05 'TCONS_00000002' 3.5158e+05 3.7458e+05 3.4238e+05 1.0483e+05 1.1546e+05 1.1105e+05
Column names starting with q have the format: conditionX_N, indicating that the column contains values for replicate N of conditionX.
References
[1] Trapnell, Cole, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J van Baren, Steven L Salzberg, Barbara J Wold, and Lior Pachter. “Transcript Assembly and Quantification by RNA-Seq Reveals Unannotated Transcripts and Isoform Switching during Cell Differentiation.” Nature Biotechnology 28, no. 5 (May 2010): 511–15.
[2] Anders, Simon, and Wolfgang Huber. “Differential Expression Analysis for Sequence Count Data.” Genome Biology 11, no. 10 (October 2010): R106. https://doi.org/10.1186/gb-2010-11-10-r106.
[3] Mortazavi, Ali, Brian A Williams, Kenneth McCue, Lorian Schaeffer, and Barbara Wold. “Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq.” Nature Methods 5, no. 7 (July 2008): 621–28. https://doi.org/10.1038/nmeth.1226.
Version History
Introduced in R2019a
See Also
cufflinks
| CufflinksOptions
| cuffcompare
| cuffdiff
| cuffmerge
| cuffnorm
| cuffquant
| cuffgtf2sam
| cuffgffread
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