Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • qiongyi
    Member
    • Nov 2010
    • 10

    Test status in Cufflinks and Cuffdiff

    Hi, I ran Cufflinks and Cuffdiff on some RNA-Seq datasets, and I got the result files "isoform_exp.diff" and "gene_exp.diff". When I looked at these two list, I found something interesting. Here I take gene "Phactr1" as an example:

    gene level estimation:
    Code:
    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
    Phactr1	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	OK	1.0429	1.49812	0.522548	-1.36759	0.171441	0.677408	no
    isoform level estimation:
    Code:
    test_id	gene_id		locus	sample_1	sample_2	status	value_1	value_2	log2(fold_change)	test_stat	p_value	q_value	significant
    NM_001005740	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	1.0429	1.49812	0.522548	-1.36759	0.171441	1	no
    NM_001005748	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	0	0	0	0	1	1	no
    NM_198419	Phactr1	Phactr1	chr13:42680622-43138512	KB	NB	NOTEST	0	0	0	0	1	1	no
    Gene "Phactr1" has three isoforms (NM_001005740, NM_001005748, NM_198419).
    Why the gene is given status "OK" while the isoform "NM_001005740" is given "NOTEST". It seems strange that cufflinks/cuffdiff extends different treatment to isoform and gene with the same expression value.
    I wonder what the criteria to set the status tags for genes and isoforms? Different criteria?
    Last edited by qiongyi; 08-24-2012, 04:43 AM.
  • qiongyi
    Member
    • Nov 2010
    • 10

    #2
    Hope someone here could help to clarify this...

    Comment

    • drosoform
      Junior Member
      • Apr 2012
      • 6

      #3
      I was wondering the exact same thing in my RNA-seq data from Cuffdiff. If anyone could explain why this happens, it would be very helpful!

      Comment

      • bhootnaath
        Junior Member
        • Jul 2009
        • 5

        #4
        Here is a somewhat related behavior of Cuffdiff / cummerbund that gives erroneous results.

        A) Ran Tophat v2.0.4 for 9 samples (3 conditions c1, c2, and c3 each with 3 biological replicates _1, _2 and _3), for example for c1_1:

        Code:
        tophat -p 8 -G genes.gtf -o tophat.output.c1_1 --no-novel-juncs --solexa1.3-quals bowtie_index c1_1.fq
        Repeated the above for c1_2, c1_3, c2_1, c2_2, c2_3, c3_1, c3_2, and c3_3.

        B) Ran Cuffdiff v2.0.2 (3522) for differential expression analysis for known list of genes/junctions:

        Code:
        cuffdiff -p 8 -b genome.fa -u genes.gtf -o cuffdiff.out -L c1,c2,c3 tophat.output.c1_1/accepted_hits.bam,tophat.output.c1_2/accepted_hits.bam,tophat.output.c1_3/accepted_hits.bam tophat.output.c2_1/accepted_hits.bam,tophat.output.c2_2/accepted_hits.bam,tophat.output.c2_3/accepted_hits.bam tophat.output.c3_1/accepted_hits.bam,tophat.output.c3_2/accepted_hits.bam,tophat.output.c3_3/accepted_hits.bam
        C) Then issued the following commands in R/cummerbund (v. 1.99.2) to write an output file containing differentially expressed genes for c1 vs. c2 with alpha = 0.05:

        Code:
        > cuff <- readCufflinks()
        > C1vsC2.sigGeneIds<-getSig(cuff,"c1","c2",alpha=0.05,level="genes")
        > C1vsC2.sigGenes <- getGenes(cuff,C1vsC2.sigGeneIds)
        > write.table(diffData(C1vsC2.sigGenes),"C1vsC2sigGenes.diff")
        Now here is one of many such erroneously reported significant genes in the C1vsC2sigGenes.diff file. The following gene *fails* the alpha = 0.05 threshold for c1 vs c2 and yet gets reported!

        Code:
        gene_id	sample_1	sample_2	status	value_1	value_2	log2_fold_change	test_stat	p_value	q_value	significant
        Gene_X	c1	c2	OK	1.12696	0.0921556	-3.61222	3.44126	0.000579016	[COLOR="Red"]0.0751255[/COLOR]	[COLOR="Red"]no[/COLOR]
        Gene_X	c1	c3	OK	1.12696	0.264807	-2.08942	1.30123	0.19318	0.999998	no
        Gene_X	c2	c3	OK	0.0921556	0.264807	1.5228	-0.990653	0.321855	0.999998	no
        Added later:

        This phenomenon has possibly been explained here and here.
        Last edited by bhootnaath; 10-17-2012, 12:20 PM. Reason: Found possible answer

        Comment

        • sdriscoll
          I like code
          • Sep 2009
          • 436

          #5
          I've seen similar inconsistencies. I've had a gene knocked out and it was not found significant at the gene level but at the isoform level it was. The thing is at the isoform level there was only one isoform with expression and its expression was the same as the gene level expression. Seems like a flaw in gene's estimated variance verses the isoform variance. Whatever the case it makes no biological sense which is always gonna be the point.
          /* Shawn Driscoll, Gene Expression Laboratory, Pfaff
          Salk Institute for Biological Studies, La Jolla, CA, USA */

          Comment

          Latest Articles

          Collapse

          • SEQadmin2
            From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data
            by SEQadmin2


            Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.


            The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
            ...
            06-02-2026, 10:05 AM
          • SEQadmin2
            Single-Cell Sequencing at an Inflection Point: Early Impacts of New Platforms and Emerging Trends
            by SEQadmin2


            With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.


            Introduction

            Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...
            05-22-2026, 06:42 AM
          • SEQadmin2
            Environmental Genomics in the Age of NGS: From Microbes to Conservation Strategies
            by SEQadmin2

            Studying ecosystems means dealing with complex, multi-species communities that are hard to observe at scale. This complexity, however, hides many important questions to be answered, from how biogeochemical cycles work and how climate change can affect species distribution to how conservation strategies can work best.


            Genomics, particularly since the expansion of NGS, has transformed ecosystem ecology. By sequencing environmental DNA, we can now assess biodiversity without direct...
            05-06-2026, 09:04 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by SEQadmin2, Today, 08:59 AM
          0 responses
          9 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-02-2026, 12:03 PM
          0 responses
          21 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 06-02-2026, 11:40 AM
          0 responses
          17 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 05-28-2026, 11:40 AM
          0 responses
          30 views
          0 reactions
          Last Post SEQadmin2  
          Working...