CuffQuantOptions

Option set for cuffquant

expand all in page

Description

A CuffQuantOptions object contains options to run the cuffquant function, which quantifies gene and transcript expression data [1].

Creation

Syntax

cuffquantOpt = CuffQuantOptions
cuffquantOpt = CuffQuantOptions(Name,Value)
cuffquantOpt = CuffQuantOptions(S)

Description

example

cuffquantOpt = CuffQuantOptions creates a CuffQuantOptions object with default property values.

CuffQuantOptions 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.

Note

CuffQuantOptions is supported on the Mac and UNIX® platforms only.

cuffquantOpt = CuffQuantOptions(Name,Value) sets the object properties using one or more name-value pair arguments. Enclose each property name in quotes. For example, cuffquantOpt = CuffQuantOptions('NumThreads',8) specifies to use eight parallel threads.

cuffquantOpt = CuffQuantOptions(S) specifies optional parameters using a string or character vector S.

Input Arguments

expand all

Cuffquant options, specified as a string or character vector. S must be in the cuffquant option syntax (prefixed by one or two dashes).

Example: '--seed 5'

Properties

expand all

Flag to normalize fragment counts to fragments per kilobase per million mapped reads (FPKM), specified as true or false.

Example: false

Data Types: logical

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

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

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

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

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 translates all available properties, with default values for unspecified properties. The only exception is that when the default value of a property is NaN, Inf, [], '', or "", then the software does not translate the corresponding property.

Example: true

Data Types: logical

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

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

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

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

Maximum number of iterations for the maximum likelihood estimation of abundances, specified as a positive integer.

Example: 4000

Data Types: double

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

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 [2].

Example: true

Data Types: logical

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

Directory to store analysis results, specified as a string or character vector.

Example: "./AnalysisResults/"

Data Types: char | string

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

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

getCommandTranslate object properties to original options syntax
getOptionsTableReturn table with all properties and equivalent options in original syntax

Examples

collapse all

Create a CuffQuantOptions object with the default values.

opt = CuffQuantOptions;

Create an object using name-value pairs.

opt2 = CuffQuantOptions('NumThreads',4,'MinAlignmentCount',50)

Create an object by using the original syntax.

opt3 = CuffQuantOptions('-p 4 --min-alignment-count 50')

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] 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.

See Also

| | | | | | |

Topics

External Websites

Introduced in R2019a

Bioinformatics Toolbox Documentation

Support