cuffquant

Quantify gene and transcript expression profiles

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Syntax

cxbFile = cuffquant(transcriptsAnnot,alignmentFiles)
cxbFile = cuffquant(transcriptsAnnot,alignmentFiles,opt)
cxbFile = cuffquant(transcriptsAnnot,alignmentFiles,Name,Value)

Description

example

cxbFile = cuffquant(transcriptsAnnot,alignmentFiles) generates abundance estimates for the samples in alignmentFiles using the reference annotation file transcriptsAnnot [1]. You can use the generated CXB-format abundance (*.CXB) as input for cuffdiff to perform downstream differential expression analysis.

cuffquant 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

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

cxbFile = cuffquant(transcriptsAnnot,alignmentFiles,opt) uses additional options specified by opt.

cxbFile = cuffquant(transcriptsAnnot,alignmentFiles,Name,Value) uses additional options specified by one or more name-value pair arguments. For example, cuffquant('gyrAB.gtf',["Myco_1_1.sam", "Myco_2_1.sam"],'NumThreads',5) specifies to use five parallel threads.

Examples

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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.

Input Arguments

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Name of the transcript annotation file, specified as a string or character vector. The file can be a GTF or GFF file produced by cufflinks, cuffcompare, or another source of GTF annotations.

Example: "gyrAB.gtf"

Data Types: char | string

Names of SAM, BAM, or CXB files containing alignment records for each sample, specified as a string vector or cell array. If you use a cell array, each element must be a string vector or cell array of character vectors specifying alignment files for every replicate of the same sample.

Example: ["Myco_1_1.sam", "Myco_2_1.sam"]

Data Types: char | string | cell

cuffquant options, specified as a CuffQuantOptions object, string, or character vector. The string or character vector must be in the original cuffquant option syntax (prefixed by one or two dashes) [1].

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: cuffquant(transcripts,alignmentFiles,'NumThreads',4,'Seed',1)

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

Example: 'EffectiveLengthCorrection',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.

Example: 'ExtraCommand','--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: 'FragmentBiasCorrection',"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: 'FragmentLengthMean',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: 'FragmentLengthSD',70

Data Types: double

The original (native) syntax is prefixed by one or two dashes. By default, the function converts only the specified options. If the value is true, the software converts all available options, with default values for unspecified options, 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: 'IncludeAll',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: 'LengthCorrection',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: 'MaskFile',"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: 'MaxBundleFrags',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: 'MaxFragAlignments',1000

Data Types: double

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

Example: 'MaxMLEIterations',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: 'MinAlignmentCount',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: 'MultiReadCorrection',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: 'NumThreads',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

Output Arguments

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Name of the abundances file, returned as a string.

The output string also includes the directory information defined by OutputDirectory. The default is the current directory. If you set OutputDirectory to "/local/tmp/", the output becomes "/local/tmp/abundances.cxb".

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. https://doi.org/10.1038/nbt.1621.

[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

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