48 Replicate RNA-seq experiment

48 Replicate RNA-seq experiment

I and other members of the team have talked about this work at meetings over the last 18 months, but today the first of three (hopefully four) papers about a 48 biological-replicate RNA-seq experiment from my group (www.compbio.dundee.ac.uk),  the Data Analysis Group (www.compbio.dundee.ac.uk/dag.html), and collaborators Tom Owen-Hughes (http://bit.ly/1PkCBjH), Gordon Simpson (http://bit.ly/1JobrGZ)  and Mark Blaxter (http://bit.ly/1GXtC8M) was submitted to a journal and posted on arXiv (http://arxiv.org/abs/1505.00588). The data generated for this experiment has also been submitted to ENA and should be released in the next few hours.

Clearly, referees will have things to say about our manuscript, but I thought it was worth writing a brief summary here of the justification for doing this work and to provide somewhere for open discussion.

Briefly:

Paper I: The paper submitted today, deals with the statistical models used in Differential Gene Expression (DGE) software such as edgeR and DESeq as well as the effect of “bad” replicates on these models.

Paper II: Will be on arXiv in the next day or so, and benchmarks the most popular DGE methods with respect to replicate number. This paper leads to a set of recommendations for experimental design.

Paper III: Is in preparation, but examines the benefits of ERCC RNA spike-ins to determine concerted shifts in expression in RNA-seq experiments as well as estimating the precision of RNA-seq experiments. There will be an R-package accompanying this paper.

The main questions we were aiming to answer in this work when we started it 2 years ago were:

  1. How many replicates should we do?
  2. Which of the growing number of statistical analysis methods should we use?
  3. Are the assumptions made by any of the methods in (2) correct?
  4. How useful are spike-ins to normalise for concerted shifts in expression?

The aim with the experimental design was to control for as many variables as possible (batch and lane effects and so on) to ensure that we were really looking at differences between DGE methods and not being confused by variation introduced elsewhere in the experiment. This careful design was the result of close collaboration between us, (a dry-lab computational biology group), Tom Owen-Hughes’ yeast lab at Dundee, and Mark Blaxter’s sequencing centre at Edinburgh.

This experiment is probably the highest replicate RNA-seq experiment to date and one of the deepest. I hope that the careful design means that in addition to our own analysis, the data will be useful to others who are interested in RNA-seq DGE methods development as well as the wider yeast community.

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13 thoughts on “48 Replicate RNA-seq experiment

  1. Hi Geoff,

    The paper indicates that there is a total of 96 samples (48 of each type), but the ENA and SRA records have 672 total samples (or 336 of each type). Was a subset of samples used for these studies? If so, should those samples be noted in the publication or as supporting materials?

    Like

    1. Hi Robert,
      As I replied on twitter, the difference in numbers is because we split each replicate across 7 lanes in the HiSeq. This gave us technical replicates and allowed us to check for batch effects and so on. The full experimental design is explained in the Methods section of the Differential Expression Tool evaluation paper we posted on arXiv (See blog on Paper II). We are working with ENA to try to improve the metadata that explains our datasets in the archive. We will also deposit the processed data that our analysis is based on in an appropriate repository in due course as well as the code that did it.

      I hope this helps, but if you have other questions, don’t hesitate to ask!

      All the best,

      Geoff.

      Like

      1. That helps. I noticed that the XML for the data on ENA has the submission file name, which does look like it indicates the sample and the technical replicate, so I may end up parsing that.

        Of course, while working with ENA to get more meta-data up, you could put a file on figshare or github that gives the ENA accession to sample-lane replicate information ….

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  2. Appreciate the example of using correlation heat maps to determine problems, as well as the other metrics that indicate possible problems, I know I will try them on my own -omics data (transcriptomics and others).

    I am curious why you wouldn’t use a grayscale map instead of heat??

    Why didn’t you calculate correlations between the individual lanes, as well as put ALL of the samples (biological and technical reps) in a single heatmap? This would show if there were lane problems, sample label mixups, etc, and give a high level overview of the data, as well as show if the within condition correlation is in general better than the between condition correlation. It would be large, but I think as a first pass overview it is useful, and then can be drilled down into within conditions for detail.

    Like

    1. And as I read it over again, I see why. All 96 samples were run on all 7 lanes, and then demultiplexed at the far end to generate the ~600 fastq files. Hmmm, curious if there is any use in calculating correlations using lanes in this situation. Will have to think about this some more.

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    2. Hi Robert,

      We actually did thes very early on in the data analysis. It was somewhat informative but we found it because pretty tricky to compare sample correlations with so much spacing between them – it really is quite large and getting the overarching comparisons was a bit tricky. Once we could demonstrate that the variation between lanes was purely Poisson fluctuations, we really didn’t think there was much point in plotting it this way any more.

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  3. The ENA metadata is going to take a while to sort out, but for now here is the mapping between accession number, lane, sample and biological replicate.

    I hope that clarifies things, but if not please get in touch.

    All the best,

    Geoff.

    RunAccession Lane Sample BiolRep
    ERR458493 1 SNF2 39
    ERR458494 2 SNF2 39
    ERR458495 3 SNF2 39
    ERR458496 4 SNF2 39
    ERR458497 5 SNF2 39
    ERR458498 6 SNF2 39
    ERR458499 7 SNF2 39
    ERR458500 1 WT 48
    ERR458501 2 WT 48
    ERR458502 3 WT 48
    ERR458503 4 WT 48
    ERR458504 5 WT 48
    ERR458505 6 WT 48
    ERR458506 7 WT 48
    ERR458507 1 SNF2 21
    ERR458508 2 SNF2 21
    ERR458509 3 SNF2 21
    ERR458510 4 SNF2 21
    ERR458511 5 SNF2 21
    ERR458512 6 SNF2 21
    ERR458513 7 SNF2 21
    ERR458514 1 SNF2 28
    ERR458515 2 SNF2 28
    ERR458516 3 SNF2 28
    ERR458517 4 SNF2 28
    ERR458518 5 SNF2 28
    ERR458519 6 SNF2 28
    ERR458520 7 SNF2 28
    ERR458521 1 SNF2 11
    ERR458522 2 SNF2 11
    ERR458523 3 SNF2 11
    ERR458524 4 SNF2 11
    ERR458525 5 SNF2 11
    ERR458526 6 SNF2 11
    ERR458527 7 SNF2 11
    ERR458528 1 WT 34
    ERR458529 2 WT 34
    ERR458530 3 WT 34
    ERR458531 4 WT 34
    ERR458532 5 WT 34
    ERR458533 6 WT 34
    ERR458534 7 WT 34
    ERR458535 1 WT 32
    ERR458536 2 WT 32
    ERR458537 3 WT 32
    ERR458538 4 WT 32
    ERR458539 5 WT 32
    ERR458540 6 WT 32
    ERR458541 7 WT 32
    ERR458542 1 SNF2 26
    ERR458543 2 SNF2 26
    ERR458544 3 SNF2 26
    ERR458545 4 SNF2 26
    ERR458546 5 SNF2 26
    ERR458547 6 SNF2 26
    ERR458548 7 SNF2 26
    ERR458549 1 WT 30
    ERR458550 2 WT 30
    ERR458551 3 WT 30
    ERR458552 4 WT 30
    ERR458553 5 WT 30
    ERR458554 6 WT 30
    ERR458555 7 WT 30
    ERR458556 1 SNF2 04
    ERR458557 2 SNF2 04
    ERR458558 3 SNF2 04
    ERR458559 4 SNF2 04
    ERR458560 5 SNF2 04
    ERR458561 6 SNF2 04
    ERR458562 7 SNF2 04
    ERR458563 1 SNF2 38
    ERR458564 2 SNF2 38
    ERR458565 3 SNF2 38
    ERR458566 4 SNF2 38
    ERR458567 5 SNF2 38
    ERR458568 6 SNF2 38
    ERR458569 7 SNF2 38
    ERR458570 1 SNF2 01
    ERR458571 2 SNF2 01
    ERR458572 3 SNF2 01
    ERR458573 4 SNF2 01
    ERR458574 5 SNF2 01
    ERR458575 6 SNF2 01
    ERR458576 7 SNF2 01
    ERR458577 1 SNF2 02
    ERR458578 2 SNF2 02
    ERR458579 3 SNF2 02
    ERR458580 4 SNF2 02
    ERR458581 5 SNF2 02
    ERR458582 6 SNF2 02
    ERR458583 7 SNF2 02
    ERR458584 1 WT 11
    ERR458585 2 WT 11
    ERR458586 3 WT 11
    ERR458587 4 WT 11
    ERR458588 5 WT 11
    ERR458589 6 WT 11
    ERR458590 7 WT 11
    ERR458591 1 SNF2 33
    ERR458592 2 SNF2 33
    ERR458593 3 SNF2 33
    ERR458594 4 SNF2 33
    ERR458595 5 SNF2 33
    ERR458596 6 SNF2 33
    ERR458597 7 SNF2 33
    ERR458598 1 WT 27
    ERR458599 2 WT 27
    ERR458600 3 WT 27
    ERR458601 4 WT 27
    ERR458602 5 WT 27
    ERR458603 6 WT 27
    ERR458604 7 WT 27
    ERR458605 1 WT 24
    ERR458606 2 WT 24
    ERR458607 3 WT 24
    ERR458608 4 WT 24
    ERR458609 5 WT 24
    ERR458610 6 WT 24
    ERR458611 7 WT 24
    ERR458612 1 WT 41
    ERR458613 2 WT 41
    ERR458614 3 WT 41
    ERR458615 4 WT 41
    ERR458616 5 WT 41
    ERR458617 6 WT 41
    ERR458618 7 WT 41
    ERR458619 1 SNF2 40
    ERR458620 2 SNF2 40
    ERR458621 3 SNF2 40
    ERR458622 4 SNF2 40
    ERR458623 5 SNF2 40
    ERR458624 6 SNF2 40
    ERR458625 7 SNF2 40
    ERR458626 1 WT 36
    ERR458627 2 WT 36
    ERR458628 3 WT 36
    ERR458629 4 WT 36
    ERR458630 5 WT 36
    ERR458631 6 WT 36
    ERR458632 7 WT 36
    ERR458633 1 SNF2 15
    ERR458634 2 SNF2 15
    ERR458635 3 SNF2 15
    ERR458636 4 SNF2 15
    ERR458637 5 SNF2 15
    ERR458638 6 SNF2 15
    ERR458639 7 SNF2 15
    ERR458640 1 SNF2 31
    ERR458641 2 SNF2 31
    ERR458642 3 SNF2 31
    ERR458643 4 SNF2 31
    ERR458644 5 SNF2 31
    ERR458645 6 SNF2 31
    ERR458646 7 SNF2 31
    ERR458647 1 WT 05
    ERR458648 2 WT 05
    ERR458649 3 WT 05
    ERR458650 4 WT 05
    ERR458651 5 WT 05
    ERR458652 6 WT 05
    ERR458653 7 WT 05
    ERR458654 1 SNF2 25
    ERR458655 2 SNF2 25
    ERR458656 3 SNF2 25
    ERR458657 4 SNF2 25
    ERR458658 5 SNF2 25
    ERR458659 6 SNF2 25
    ERR458660 7 SNF2 25
    ERR458661 1 SNF2 42
    ERR458662 2 SNF2 42
    ERR458663 3 SNF2 42
    ERR458664 4 SNF2 42
    ERR458665 5 SNF2 42
    ERR458666 6 SNF2 42
    ERR458667 7 SNF2 42
    ERR458668 1 WT 33
    ERR458669 2 WT 33
    ERR458670 3 WT 33
    ERR458671 4 WT 33
    ERR458672 5 WT 33
    ERR458673 6 WT 33
    ERR458674 7 WT 33
    ERR458675 1 WT 23
    ERR458676 2 WT 23
    ERR458677 3 WT 23
    ERR458678 4 WT 23
    ERR458679 5 WT 23
    ERR458680 6 WT 23
    ERR458681 7 WT 23
    ERR458682 1 SNF2 34
    ERR458683 2 SNF2 34
    ERR458684 3 SNF2 34
    ERR458685 4 SNF2 34
    ERR458686 5 SNF2 34
    ERR458687 6 SNF2 34
    ERR458688 7 SNF2 34
    ERR458689 1 WT 26
    ERR458690 2 WT 26
    ERR458691 3 WT 26
    ERR458692 4 WT 26
    ERR458693 5 WT 26
    ERR458694 6 WT 26
    ERR458695 7 WT 26
    ERR458696 1 WT 09
    ERR458697 2 WT 09
    ERR458698 3 WT 09
    ERR458699 4 WT 09
    ERR458700 5 WT 09
    ERR458701 6 WT 09
    ERR458702 7 WT 09
    ERR458703 1 SNF2 16
    ERR458704 2 SNF2 16
    ERR458705 3 SNF2 16
    ERR458706 4 SNF2 16
    ERR458707 5 SNF2 16
    ERR458708 6 SNF2 16
    ERR458709 7 SNF2 16
    ERR458710 1 WT 29
    ERR458711 2 WT 29
    ERR458712 3 WT 29
    ERR458713 4 WT 29
    ERR458714 5 WT 29
    ERR458715 6 WT 29
    ERR458716 7 WT 29
    ERR458717 1 WT 22
    ERR458718 2 WT 22
    ERR458719 3 WT 22
    ERR458720 4 WT 22
    ERR458721 5 WT 22
    ERR458722 6 WT 22
    ERR458723 7 WT 22
    ERR458724 1 SNF2 29
    ERR458725 2 SNF2 29
    ERR458726 3 SNF2 29
    ERR458727 4 SNF2 29
    ERR458728 5 SNF2 29
    ERR458729 6 SNF2 29
    ERR458730 7 SNF2 29
    ERR458731 1 SNF2 14
    ERR458732 2 SNF2 14
    ERR458733 3 SNF2 14
    ERR458734 4 SNF2 14
    ERR458735 5 SNF2 14
    ERR458736 6 SNF2 14
    ERR458737 7 SNF2 14
    ERR458738 1 WT 12
    ERR458739 2 WT 12
    ERR458740 3 WT 12
    ERR458741 4 WT 12
    ERR458742 5 WT 12
    ERR458743 6 WT 12
    ERR458744 7 WT 12
    ERR458745 1 SNF2 23
    ERR458746 2 SNF2 23
    ERR458747 3 SNF2 23
    ERR458748 4 SNF2 23
    ERR458749 5 SNF2 23
    ERR458750 6 SNF2 23
    ERR458751 7 SNF2 23
    ERR458752 1 SNF2 46
    ERR458753 2 SNF2 46
    ERR458754 3 SNF2 46
    ERR458755 4 SNF2 46
    ERR458756 5 SNF2 46
    ERR458757 6 SNF2 46
    ERR458758 7 SNF2 46
    ERR458759 1 SNF2 06
    ERR458760 2 SNF2 06
    ERR458761 3 SNF2 06
    ERR458762 4 SNF2 06
    ERR458763 5 SNF2 06
    ERR458764 6 SNF2 06
    ERR458765 7 SNF2 06
    ERR458766 1 SNF2 19
    ERR458767 2 SNF2 19
    ERR458768 3 SNF2 19
    ERR458769 4 SNF2 19
    ERR458770 5 SNF2 19
    ERR458771 6 SNF2 19
    ERR458772 7 SNF2 19
    ERR458773 1 SNF2 12
    ERR458774 2 SNF2 12
    ERR458775 3 SNF2 12
    ERR458776 4 SNF2 12
    ERR458777 5 SNF2 12
    ERR458778 6 SNF2 12
    ERR458779 7 SNF2 12
    ERR458780 1 WT 31
    ERR458781 2 WT 31
    ERR458782 3 WT 31
    ERR458783 4 WT 31
    ERR458784 5 WT 31
    ERR458785 6 WT 31
    ERR458786 7 WT 31
    ERR458787 1 SNF2 47
    ERR458788 2 SNF2 47
    ERR458789 3 SNF2 47
    ERR458790 4 SNF2 47
    ERR458791 5 SNF2 47
    ERR458792 6 SNF2 47
    ERR458793 7 SNF2 47
    ERR458794 1 WT 25
    ERR458795 2 WT 25
    ERR458796 3 WT 25
    ERR458797 4 WT 25
    ERR458798 5 WT 25
    ERR458799 6 WT 25
    ERR458800 7 WT 25
    ERR458801 1 WT 17
    ERR458802 2 WT 17
    ERR458803 3 WT 17
    ERR458804 4 WT 17
    ERR458805 5 WT 17
    ERR458806 6 WT 17
    ERR458807 7 WT 17
    ERR458808 1 WT 38
    ERR458809 2 WT 38
    ERR458810 3 WT 38
    ERR458811 4 WT 38
    ERR458812 5 WT 38
    ERR458813 6 WT 38
    ERR458814 7 WT 38
    ERR458815 1 SNF2 35
    ERR458816 2 SNF2 35
    ERR458817 3 SNF2 35
    ERR458818 4 SNF2 35
    ERR458819 5 SNF2 35
    ERR458820 6 SNF2 35
    ERR458821 7 SNF2 35
    ERR458822 1 WT 08
    ERR458823 2 WT 08
    ERR458824 3 WT 08
    ERR458825 4 WT 08
    ERR458826 5 WT 08
    ERR458827 6 WT 08
    ERR458828 7 WT 08
    ERR458829 1 WT 07
    ERR458830 2 WT 07
    ERR458831 3 WT 07
    ERR458832 4 WT 07
    ERR458833 5 WT 07
    ERR458834 6 WT 07
    ERR458835 7 WT 07
    ERR458878 1 WT 02
    ERR458879 2 WT 02
    ERR458880 3 WT 02
    ERR458881 4 WT 02
    ERR458882 5 WT 02
    ERR458883 6 WT 02
    ERR458884 7 WT 02
    ERR458885 1 WT 03
    ERR458886 2 WT 03
    ERR458887 3 WT 03
    ERR458888 4 WT 03
    ERR458889 5 WT 03
    ERR458890 6 WT 03
    ERR458891 7 WT 03
    ERR458892 1 WT 10
    ERR458893 2 WT 10
    ERR458894 3 WT 10
    ERR458895 4 WT 10
    ERR458896 5 WT 10
    ERR458897 6 WT 10
    ERR458898 7 WT 10
    ERR458899 1 WT 45
    ERR458900 2 WT 45
    ERR458901 3 WT 45
    ERR458902 4 WT 45
    ERR458903 5 WT 45
    ERR458904 6 WT 45
    ERR458905 7 WT 45
    ERR458906 1 SNF2 30
    ERR458907 2 SNF2 30
    ERR458908 3 SNF2 30
    ERR458909 4 SNF2 30
    ERR458910 5 SNF2 30
    ERR458911 6 SNF2 30
    ERR458912 7 SNF2 30
    ERR458913 1 SNF2 24
    ERR458914 2 SNF2 24
    ERR458915 3 SNF2 24
    ERR458916 4 SNF2 24
    ERR458917 5 SNF2 24
    ERR458918 6 SNF2 24
    ERR458919 7 SNF2 24
    ERR458920 1 SNF2 20
    ERR458921 2 SNF2 20
    ERR458922 3 SNF2 20
    ERR458923 4 SNF2 20
    ERR458924 5 SNF2 20
    ERR458925 6 SNF2 20
    ERR458926 7 SNF2 20
    ERR458927 1 WT 43
    ERR458928 2 WT 43
    ERR458929 3 WT 43
    ERR458930 4 WT 43
    ERR458931 5 WT 43
    ERR458932 6 WT 43
    ERR458933 7 WT 43
    ERR458934 1 SNF2 37
    ERR458935 2 SNF2 37
    ERR458936 3 SNF2 37
    ERR458937 4 SNF2 37
    ERR458938 5 SNF2 37
    ERR458939 6 SNF2 37
    ERR458940 7 SNF2 37
    ERR458941 1 WT 06
    ERR458942 2 WT 06
    ERR458943 3 WT 06
    ERR458944 4 WT 06
    ERR458945 5 WT 06
    ERR458946 6 WT 06
    ERR458947 7 WT 06
    ERR458948 1 SNF2 03
    ERR458949 2 SNF2 03
    ERR458950 3 SNF2 03
    ERR458951 4 SNF2 03
    ERR458952 5 SNF2 03
    ERR458953 6 SNF2 03
    ERR458954 7 SNF2 03
    ERR458955 1 SNF2 45
    ERR458956 2 SNF2 45
    ERR458957 3 SNF2 45
    ERR458958 4 SNF2 45
    ERR458959 5 SNF2 45
    ERR458960 6 SNF2 45
    ERR458961 7 SNF2 45
    ERR458962 1 WT 04
    ERR458963 2 WT 04
    ERR458964 3 WT 04
    ERR458965 4 WT 04
    ERR458966 5 WT 04
    ERR458967 6 WT 04
    ERR458968 7 WT 04
    ERR458969 1 SNF2 09
    ERR458970 2 SNF2 09
    ERR458971 3 SNF2 09
    ERR458972 4 SNF2 09
    ERR458973 5 SNF2 09
    ERR458974 6 SNF2 09
    ERR458975 7 SNF2 09
    ERR458976 1 WT 44
    ERR458977 2 WT 44
    ERR458978 3 WT 44
    ERR458979 4 WT 44
    ERR458980 5 WT 44
    ERR458981 6 WT 44
    ERR458982 7 WT 44
    ERR458983 1 WT 18
    ERR458984 2 WT 18
    ERR458985 3 WT 18
    ERR458986 4 WT 18
    ERR458987 5 WT 18
    ERR458988 6 WT 18
    ERR458989 7 WT 18
    ERR458990 1 SNF2 48
    ERR458991 2 SNF2 48
    ERR458992 3 SNF2 48
    ERR458993 4 SNF2 48
    ERR458994 5 SNF2 48
    ERR458995 6 SNF2 48
    ERR458996 7 SNF2 48
    ERR458997 1 WT 35
    ERR458998 2 WT 35
    ERR458999 3 WT 35
    ERR459000 4 WT 35
    ERR459001 5 WT 35
    ERR459002 6 WT 35
    ERR459003 7 WT 35
    ERR459004 1 WT 42
    ERR459005 2 WT 42
    ERR459006 3 WT 42
    ERR459007 4 WT 42
    ERR459008 5 WT 42
    ERR459009 6 WT 42
    ERR459010 7 WT 42
    ERR459011 1 WT 14
    ERR459012 2 WT 14
    ERR459013 3 WT 14
    ERR459014 4 WT 14
    ERR459015 5 WT 14
    ERR459016 6 WT 14
    ERR459017 7 WT 14
    ERR459018 1 WT 19
    ERR459019 2 WT 19
    ERR459020 3 WT 19
    ERR459021 4 WT 19
    ERR459022 5 WT 19
    ERR459023 6 WT 19
    ERR459024 7 WT 19
    ERR459025 1 SNF2 36
    ERR459026 2 SNF2 36
    ERR459027 3 SNF2 36
    ERR459028 4 SNF2 36
    ERR459029 5 SNF2 36
    ERR459030 6 SNF2 36
    ERR459031 7 SNF2 36
    ERR459032 1 WT 21
    ERR459033 2 WT 21
    ERR459034 3 WT 21
    ERR459035 4 WT 21
    ERR459036 5 WT 21
    ERR459037 6 WT 21
    ERR459038 7 WT 21
    ERR459039 1 WT 13
    ERR459040 2 WT 13
    ERR459041 3 WT 13
    ERR459042 4 WT 13
    ERR459043 5 WT 13
    ERR459044 6 WT 13
    ERR459045 7 WT 13
    ERR459046 1 SNF2 08
    ERR459047 2 SNF2 08
    ERR459048 3 SNF2 08
    ERR459049 4 SNF2 08
    ERR459050 5 SNF2 08
    ERR459051 6 SNF2 08
    ERR459052 7 SNF2 08
    ERR459053 1 SNF2 41
    ERR459054 2 SNF2 41
    ERR459055 3 SNF2 41
    ERR459056 4 SNF2 41
    ERR459057 5 SNF2 41
    ERR459058 6 SNF2 41
    ERR459059 7 SNF2 41
    ERR459060 1 WT 16
    ERR459061 2 WT 16
    ERR459062 3 WT 16
    ERR459063 4 WT 16
    ERR459064 5 WT 16
    ERR459065 6 WT 16
    ERR459066 7 WT 16
    ERR459067 1 WT 20
    ERR459068 2 WT 20
    ERR459069 3 WT 20
    ERR459070 4 WT 20
    ERR459071 5 WT 20
    ERR459072 6 WT 20
    ERR459073 7 WT 20
    ERR459074 1 SNF2 27
    ERR459075 2 SNF2 27
    ERR459076 3 SNF2 27
    ERR459077 4 SNF2 27
    ERR459078 5 SNF2 27
    ERR459079 6 SNF2 27
    ERR459080 7 SNF2 27
    ERR459081 1 SNF2 43
    ERR459082 2 SNF2 43
    ERR459083 3 SNF2 43
    ERR459084 4 SNF2 43
    ERR459085 5 SNF2 43
    ERR459086 6 SNF2 43
    ERR459087 7 SNF2 43
    ERR459088 1 WT 47
    ERR459089 2 WT 47
    ERR459090 3 WT 47
    ERR459091 4 WT 47
    ERR459092 5 WT 47
    ERR459093 6 WT 47
    ERR459094 7 WT 47
    ERR459095 1 WT 40
    ERR459096 2 WT 40
    ERR459097 3 WT 40
    ERR459098 4 WT 40
    ERR459099 5 WT 40
    ERR459100 6 WT 40
    ERR459101 7 WT 40
    ERR459102 1 WT 28
    ERR459103 2 WT 28
    ERR459104 3 WT 28
    ERR459105 4 WT 28
    ERR459106 5 WT 28
    ERR459107 6 WT 28
    ERR459108 7 WT 28
    ERR459109 1 SNF2 32
    ERR459110 2 SNF2 32
    ERR459111 3 SNF2 32
    ERR459112 4 SNF2 32
    ERR459113 5 SNF2 32
    ERR459114 6 SNF2 32
    ERR459115 7 SNF2 32
    ERR459116 1 WT 15
    ERR459117 2 WT 15
    ERR459118 3 WT 15
    ERR459119 4 WT 15
    ERR459120 5 WT 15
    ERR459121 6 WT 15
    ERR459122 7 WT 15
    ERR459123 1 SNF2 07
    ERR459124 2 SNF2 07
    ERR459125 3 SNF2 07
    ERR459126 4 SNF2 07
    ERR459127 5 SNF2 07
    ERR459128 6 SNF2 07
    ERR459129 7 SNF2 07
    ERR459130 1 WT 39
    ERR459131 2 WT 39
    ERR459132 3 WT 39
    ERR459133 4 WT 39
    ERR459134 5 WT 39
    ERR459135 6 WT 39
    ERR459136 7 WT 39
    ERR459137 1 SNF2 05
    ERR459138 2 SNF2 05
    ERR459139 3 SNF2 05
    ERR459140 4 SNF2 05
    ERR459141 5 SNF2 05
    ERR459142 6 SNF2 05
    ERR459143 7 SNF2 05
    ERR459144 1 SNF2 18
    ERR459145 2 SNF2 18
    ERR459146 3 SNF2 18
    ERR459147 4 SNF2 18
    ERR459148 5 SNF2 18
    ERR459149 6 SNF2 18
    ERR459150 7 SNF2 18
    ERR459151 1 SNF2 13
    ERR459152 2 SNF2 13
    ERR459153 3 SNF2 13
    ERR459154 4 SNF2 13
    ERR459155 5 SNF2 13
    ERR459156 6 SNF2 13
    ERR459157 7 SNF2 13
    ERR459158 1 SNF2 10
    ERR459159 2 SNF2 10
    ERR459160 3 SNF2 10
    ERR459161 4 SNF2 10
    ERR459162 5 SNF2 10
    ERR459163 6 SNF2 10
    ERR459164 7 SNF2 10
    ERR459165 1 WT 01
    ERR459166 2 WT 01
    ERR459167 3 WT 01
    ERR459168 4 WT 01
    ERR459169 5 WT 01
    ERR459170 6 WT 01
    ERR459171 7 WT 01
    ERR459172 1 SNF2 22
    ERR459173 2 SNF2 22
    ERR459174 3 SNF2 22
    ERR459175 4 SNF2 22
    ERR459176 5 SNF2 22
    ERR459177 6 SNF2 22
    ERR459178 7 SNF2 22
    ERR459179 1 SNF2 44
    ERR459180 2 SNF2 44
    ERR459181 3 SNF2 44
    ERR459182 4 SNF2 44
    ERR459183 5 SNF2 44
    ERR459184 6 SNF2 44
    ERR459185 7 SNF2 44
    ERR459186 1 WT 46
    ERR459187 2 WT 46
    ERR459188 3 WT 46
    ERR459189 4 WT 46
    ERR459190 5 WT 46
    ERR459191 6 WT 46
    ERR459192 7 WT 46
    ERR459193 1 SNF2 17
    ERR459194 2 SNF2 17
    ERR459195 3 SNF2 17
    ERR459196 4 SNF2 17
    ERR459197 5 SNF2 17
    ERR459198 6 SNF2 17
    ERR459199 7 SNF2 17
    ERR459200 1 WT 37
    ERR459201 2 WT 37
    ERR459202 3 WT 37
    ERR459203 4 WT 37
    ERR459204 5 WT 37
    ERR459205 6 WT 37
    ERR459206 7 WT 37

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  4. thanks a lot for the very interesting paper both on the statistical models as well as the one about how many biological replicates are needed. I have found both of them very worth reading. I would really like to know, how you did the statistical calculations in the paper about the statistical models. Is there also an R package (or a script for that matter), which I might be allow to adapt to my data set.

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