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, 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:
- How many replicates should we do?
- Which of the growing number of statistical analysis methods should we use?
- Are the assumptions made by any of the methods in (2) correct?
- 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.
Extremely useful. Thanks Geoff.
Thanks Nicolas! Hopefully you will enjoy Paper II as well – coming soon!
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?
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.
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 ….
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.
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.
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.
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
Alternatively, the metadata can retrieved from figshare in a slightly more manageable format.
http://figshare.com/articles/Metadata_for_a_highly_replicated_two_condition_yeast_RNAseq_experiment_/1416210
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.
I’m glad you found the papers helpful. We put the code up on github under https://github.com/bartongroup/profDGE48 so you should be able to find what you need there. If you have further questions, then please email the first authors on each of the papers and cc me.