These samples were run by seq2science v0.6.1, a tool for easy preprocessing of NGS data.
Take a look at our docs for info about how to use this report to the fullest.
- Workflow
- rna-seq
- Date
- January 20, 2022
- Project
- results_Dre
- Contact E-mail
- tessa.dewijs2@ru.nl
Report generated on 2022-01-20, 16:20 based on data in:
/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483837.fastp.json
/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483794.fastp.json
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483800.strandedness.txt
/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483745.fastp.json
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483758.strandedness.txt
/bank/tdewijs/results_Dre/B/qc/samtools_stats/star/GRCz11-GSM1483795.samtools-coordinate.samtools_stats.txt
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483821.strandedness.txt
/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483796.fastp.json
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483792.strandedness.txt
/bank/tdewijs/results_Dre/B/qc/samtools_stats/star/GRCz11-GSM1483736.samtools-coordinate.samtools_stats.txt
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483768.strandedness.txt
/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483787.strandedness.txt
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/bank/tdewijs/results_Dre/B/qc/strandedness/GRCz11-GSM1483797.strandedness.txt
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/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483814.fastp.json
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/bank/tdewijs/results_Dre/B/star/GRCz11-GSM1483742.samtools-coordinate-unsieved.bam.mtnucratiomtnuc.json
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/bank/tdewijs/results_Dre/B/qc/samtools_stats/star/GRCz11-GSM1483815.samtools-coordinate.samtools_stats.txt
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/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483824.fastp.json
/bank/tdewijs/results_Dre/B/qc/trimming/GSM1483742.fastp.json
/bank/tdewijs/results_Dre/B/star/GRCz11-GSM1483781.samtools-coordinate-unsieved.bam.mtnucratiomtnuc.json
Change sample names:
General Statistics
Showing 105/105 rows and 10/19 columns.Sample Name | % Duplication | GC content | % PF | % Adapter | % Dups | % Mapped | M Total seqs | Genome coverage | M Genome reads | M MT genome reads |
---|---|---|---|---|---|---|---|---|---|---|
GSM1483736 | 89.3% | 40.6% | 96.9% | 2.6% | 95.6% | 100.0% | 0.9 | 0.0 X | 0.9 | 0.2 |
GSM1483737 | 87.7% | 41.3% | 96.5% | 4.5% | 94.7% | 100.0% | 0.6 | 0.0 X | 0.5 | 0.2 |
GSM1483738 | 90.1% | 40.3% | 98.7% | 95.7% | 100.0% | 2.3 | 0.1 X | 2.3 | 0.4 | |
GSM1483739 | 89.1% | 41.3% | 98.6% | 95.6% | 100.0% | 1.7 | 0.0 X | 1.4 | 0.5 | |
GSM1483740 | 90.0% | 40.7% | 96.9% | 2.5% | 95.2% | 100.0% | 0.6 | 0.0 X | 0.6 | 0.1 |
GSM1483741 | 88.0% | 39.8% | 97.2% | 2.8% | 94.3% | 100.0% | 1.6 | 0.0 X | 1.7 | 0.3 |
GSM1483742 | 89.9% | 40.0% | 97.5% | 2.0% | 95.1% | 100.0% | 1.1 | 0.0 X | 1.2 | 0.2 |
GSM1483743 | 89.1% | 41.8% | 98.7% | 94.9% | 100.0% | 0.3 | 0.0 X | 0.4 | 0.1 | |
GSM1483744 | 85.0% | 40.2% | 96.5% | 1.4% | 92.2% | 100.0% | 0.4 | 0.0 X | 0.4 | 0.1 |
GSM1483745 | 82.7% | 40.9% | 96.7% | 1.1% | 91.7% | 100.0% | 0.5 | 0.0 X | 0.6 | 0.1 |
GSM1483746 | 84.2% | 40.9% | 97.1% | 0.7% | 92.1% | 100.0% | 0.4 | 0.0 X | 0.5 | 0.1 |
GSM1483747 | 85.0% | 40.0% | 97.6% | 0.7% | 92.8% | 100.0% | 0.7 | 0.0 X | 0.8 | 0.1 |
GSM1483748 | 85.5% | 39.5% | 97.2% | 0.6% | 93.2% | 100.0% | 1.2 | 0.0 X | 1.4 | 0.1 |
GSM1483749 | 84.2% | 39.6% | 97.9% | 0.8% | 90.4% | 100.0% | 0.2 | 0.0 X | 0.3 | 0.0 |
GSM1483750 | 85.7% | 38.4% | 97.6% | 0.7% | 92.6% | 100.0% | 1.5 | 0.0 X | 1.7 | 0.1 |
GSM1483751 | 84.3% | 39.9% | 97.5% | 1.0% | 92.1% | 100.0% | 0.8 | 0.0 X | 0.9 | 0.0 |
GSM1483752 | 89.2% | 40.6% | 98.4% | 96.1% | 100.0% | 3.3 | 0.1 X | 3.4 | 0.5 | |
GSM1483753 | 87.7% | 40.3% | 97.5% | 1.3% | 94.2% | 100.0% | 1.4 | 0.0 X | 1.5 | 0.2 |
GSM1483754 | 88.9% | 41.8% | 98.5% | 95.9% | 100.0% | 2.4 | 0.1 X | 2.7 | 0.2 | |
GSM1483755 | 88.3% | 39.7% | 98.4% | 95.0% | 100.0% | 2.3 | 0.1 X | 2.5 | 0.2 | |
GSM1483756 | 89.3% | 42.4% | 98.1% | 0.7% | 95.2% | 100.0% | 0.6 | 0.0 X | 0.7 | 0.0 |
GSM1483757 | 87.6% | 40.5% | 98.3% | 94.4% | 100.0% | 1.4 | 0.0 X | 1.5 | 0.2 | |
GSM1483758 | 89.6% | 40.2% | 98.1% | 0.7% | 96.0% | 100.0% | 3.6 | 0.1 X | 4.0 | 0.2 |
GSM1483759 | 88.8% | 38.9% | 98.2% | 0.5% | 94.8% | 100.0% | 2.2 | 0.1 X | 2.4 | 0.1 |
GSM1483760 | 84.9% | 40.7% | 96.9% | 1.0% | 93.8% | 100.0% | 1.4 | 0.0 X | 1.6 | 0.1 |
GSM1483761 | 84.6% | 39.9% | 94.6% | 3.7% | 93.9% | 100.0% | 2.1 | 0.1 X | 2.4 | 0.1 |
GSM1483762 | 85.1% | 39.6% | 97.5% | 0.8% | 93.2% | 100.0% | 2.2 | 0.1 X | 2.5 | 0.1 |
GSM1483763 | 83.6% | 40.3% | 96.4% | 5.6% | 92.6% | 100.0% | 1.4 | 0.0 X | 1.6 | 0.1 |
GSM1483764 | 86.4% | 41.4% | 97.6% | 0.8% | 94.0% | 100.0% | 1.2 | 0.0 X | 1.4 | 0.1 |
GSM1483765 | 86.7% | 40.8% | 98.1% | 0.7% | 93.9% | 100.0% | 2.1 | 0.1 X | 2.5 | 0.1 |
GSM1483766 | 84.5% | 40.6% | 97.5% | 0.8% | 93.8% | 100.0% | 2.8 | 0.1 X | 3.1 | 0.2 |
GSM1483767 | 85.1% | 40.7% | 98.0% | 92.7% | 100.0% | 1.4 | 0.0 X | 1.5 | 0.1 | |
GSM1483768 | 90.6% | 41.5% | 98.5% | 0.4% | 95.9% | 100.0% | 1.6 | 0.0 X | 1.8 | 0.1 |
GSM1483769 | 91.1% | 41.0% | 98.3% | 1.0% | 95.1% | 100.0% | 0.9 | 0.0 X | 1.0 | 0.1 |
GSM1483770 | 90.3% | 40.2% | 98.9% | 92.7% | 100.0% | 0.0 | 0.0 X | 0.0 | 0.0 | |
GSM1483771 | 89.9% | 40.5% | 98.3% | 1.0% | 95.0% | 100.0% | 1.8 | 0.1 X | 2.0 | 0.2 |
GSM1483772 | 89.4% | 40.8% | 97.6% | 1.5% | 95.6% | 100.0% | 2.0 | 0.1 X | 2.3 | 0.2 |
GSM1483773 | 89.7% | 40.3% | 98.6% | 95.7% | 100.0% | 2.6 | 0.1 X | 3.0 | 0.2 | |
GSM1483774 | 89.8% | 40.5% | 98.7% | 95.4% | 100.0% | 2.3 | 0.1 X | 2.7 | 0.2 | |
GSM1483775 | 89.4% | 40.4% | 98.3% | 0.7% | 95.2% | 100.0% | 1.7 | 0.1 X | 2.1 | 0.1 |
GSM1483776 | 84.3% | 40.7% | 97.0% | 0.6% | 94.1% | 100.0% | 2.5 | 0.1 X | 2.8 | 0.1 |
GSM1483777 | 81.4% | 39.7% | 96.7% | 0.5% | 93.6% | 100.0% | 4.6 | 0.1 X | 5.4 | 0.2 |
GSM1483778 | 84.1% | 40.4% | 96.9% | 0.8% | 93.3% | 100.0% | 2.1 | 0.1 X | 2.4 | 0.1 |
GSM1483779 | 83.6% | 40.1% | 97.0% | 1.0% | 94.5% | 100.0% | 4.3 | 0.1 X | 4.9 | 0.2 |
GSM1483780 | 83.6% | 40.6% | 96.6% | 0.7% | 94.5% | 100.0% | 3.4 | 0.1 X | 3.8 | 0.2 |
GSM1483781 | 83.3% | 41.3% | 97.2% | 0.7% | 93.1% | 100.0% | 1.9 | 0.1 X | 2.2 | 0.1 |
GSM1483782 | 83.3% | 41.2% | 97.0% | 0.7% | 93.9% | 100.0% | 1.7 | 0.0 X | 1.9 | 0.1 |
GSM1483783 | 82.6% | 40.9% | 96.4% | 5.4% | 92.7% | 100.0% | 1.8 | 0.1 X | 2.0 | 0.1 |
GSM1483784 | 87.8% | 40.2% | 97.7% | 0.6% | 96.6% | 100.0% | 7.4 | 0.2 X | 8.3 | 0.4 |
GSM1483785 | 87.4% | 40.3% | 98.0% | 0.6% | 95.1% | 100.0% | 3.3 | 0.1 X | 3.7 | 0.2 |
GSM1483786 | 88.6% | 39.0% | 97.9% | 0.5% | 96.4% | 100.0% | 7.4 | 0.2 X | 8.6 | 0.3 |
GSM1483787 | 87.3% | 39.4% | 97.7% | 0.6% | 95.7% | 100.0% | 5.5 | 0.2 X | 6.3 | 0.3 |
GSM1483788 | 88.5% | 41.4% | 98.1% | 96.5% | 100.0% | 4.9 | 0.1 X | 5.8 | 0.3 | |
GSM1483789 | 84.6% | 41.1% | 97.1% | 0.4% | 93.8% | 100.0% | 1.7 | 0.0 X | 1.9 | 0.2 |
GSM1483790 | 82.7% | 40.4% | 97.1% | 0.7% | 93.2% | 100.0% | 3.8 | 0.1 X | 4.3 | 0.3 |
GSM1483791 | 84.0% | 41.3% | 97.1% | 0.6% | 93.6% | 100.0% | 2.5 | 0.1 X | 2.8 | 0.2 |
GSM1483792 | 82.6% | 40.9% | 96.9% | 0.8% | 94.9% | 100.0% | 6.3 | 0.2 X | 7.2 | 0.6 |
GSM1483793 | 85.1% | 40.2% | 97.3% | 0.6% | 93.9% | 100.0% | 2.3 | 0.1 X | 2.6 | 0.2 |
GSM1483794 | 82.6% | 39.7% | 97.2% | 0.7% | 93.0% | 100.0% | 3.2 | 0.1 X | 3.7 | 0.2 |
GSM1483795 | 85.4% | 39.8% | 97.6% | 0.6% | 92.4% | 100.0% | 1.2 | 0.0 X | 1.5 | 0.1 |
GSM1483796 | 84.4% | 40.8% | 97.5% | 0.7% | 94.4% | 100.0% | 2.9 | 0.1 X | 3.4 | 0.2 |
GSM1483797 | 89.2% | 41.2% | 98.2% | 0.3% | 96.2% | 100.0% | 4.6 | 0.1 X | 5.2 | 0.3 |
GSM1483798 | 87.8% | 41.0% | 98.0% | 0.6% | 95.2% | 100.0% | 3.6 | 0.1 X | 4.1 | 0.3 |
GSM1483799 | 89.0% | 41.6% | 97.3% | 2.1% | 96.5% | 100.0% | 6.3 | 0.2 X | 7.1 | 0.7 |
GSM1483800 | 89.9% | 41.7% | 98.7% | 95.3% | 100.0% | 1.3 | 0.0 X | 1.5 | 0.1 | |
GSM1483801 | 90.1% | 40.9% | 98.5% | 0.4% | 94.1% | 100.0% | 0.1 | 0.0 X | 0.2 | 0.0 |
GSM1483802 | 89.3% | 40.2% | 99.0% | 93.1% | 100.0% | 0.3 | 0.0 X | 0.4 | 0.0 | |
GSM1483803 | 89.7% | 40.0% | 98.1% | 1.4% | 96.4% | 100.0% | 7.4 | 0.2 X | 8.4 | 0.4 |
GSM1483804 | 85.6% | 40.6% | 97.4% | 0.4% | 95.4% | 100.0% | 4.6 | 0.1 X | 5.3 | 0.2 |
GSM1483805 | 84.6% | 41.5% | 97.2% | 0.6% | 94.6% | 100.0% | 2.6 | 0.1 X | 2.9 | 0.2 |
GSM1483806 | 83.7% | 41.6% | 97.5% | 0.7% | 93.3% | 100.0% | 2.7 | 0.1 X | 3.1 | 0.2 |
GSM1483807 | 82.2% | 41.5% | 97.1% | 94.4% | 100.0% | 4.5 | 0.1 X | 5.2 | 0.2 | |
GSM1483808 | 82.8% | 40.3% | 97.3% | 0.7% | 93.5% | 100.0% | 3.7 | 0.1 X | 4.3 | 0.3 |
GSM1483809 | 85.0% | 40.2% | 97.1% | 0.7% | 94.4% | 100.0% | 2.4 | 0.1 X | 2.7 | 0.1 |
GSM1483810 | 83.0% | 34.2% | 97.7% | 86.6% | 100.0% | 0.1 | 0.0 X | 0.1 | 0.0 | |
GSM1483811 | 84.0% | 40.6% | 97.1% | 0.8% | 94.1% | 100.0% | 3.0 | 0.1 X | 3.3 | 0.2 |
GSM1483812 | 84.4% | 39.6% | 97.8% | 0.6% | 92.9% | 100.0% | 2.4 | 0.1 X | 2.7 | 0.1 |
GSM1483813 | 87.3% | 40.5% | 97.7% | 0.6% | 96.2% | 100.0% | 5.6 | 0.2 X | 6.4 | 0.3 |
GSM1483814 | 88.4% | 40.5% | 96.3% | 2.2% | 95.9% | 100.0% | 4.3 | 0.1 X | 5.0 | 0.3 |
GSM1483815 | 62.3% | 42.4% | 89.2% | 83.1% | 100.0% | 0.0 | 0.0 X | 0.0 | 0.0 | |
GSM1483816 | 89.6% | 40.2% | 98.4% | 0.6% | 95.9% | 100.0% | 4.0 | 0.1 X | 4.7 | 0.3 |
GSM1483817 | 90.4% | 42.7% | 98.4% | 0.7% | 96.5% | 100.0% | 2.5 | 0.1 X | 2.8 | 0.1 |
GSM1483818 | 91.0% | 41.0% | 98.6% | 0.7% | 96.2% | 100.0% | 3.8 | 0.1 X | 4.2 | 0.3 |
GSM1483819 | 90.1% | 41.9% | 98.7% | 96.5% | 100.0% | 3.8 | 0.1 X | 4.2 | 0.4 | |
GSM1483820 | 90.1% | 40.0% | 98.3% | 0.7% | 95.6% | 100.0% | 3.7 | 0.1 X | 4.1 | 0.3 |
GSM1483821 | 86.7% | 41.2% | 97.5% | 1.0% | 94.2% | 100.0% | 0.9 | 0.0 X | 1.0 | 0.1 |
GSM1483822 | 86.7% | 40.9% | 98.3% | 0.7% | 94.5% | 100.0% | 2.1 | 0.1 X | 2.4 | 0.1 |
GSM1483823 | 84.5% | 39.4% | 97.5% | 0.8% | 93.7% | 100.0% | 4.2 | 0.1 X | 4.8 | 0.3 |
GSM1483824 | 84.6% | 39.6% | 97.8% | 93.3% | 100.0% | 2.4 | 0.1 X | 2.8 | 0.2 | |
GSM1483825 | 85.6% | 40.6% | 97.1% | 0.9% | 94.4% | 100.0% | 3.5 | 0.1 X | 4.2 | 0.3 |
GSM1483826 | 86.4% | 41.1% | 98.2% | 1.0% | 93.2% | 100.0% | 0.5 | 0.0 X | 0.5 | 0.0 |
GSM1483827 | 84.7% | 40.0% | 97.9% | 93.2% | 100.0% | 1.8 | 0.1 X | 2.0 | 0.1 | |
GSM1483828 | 84.5% | 40.0% | 97.5% | 1.1% | 93.1% | 100.0% | 2.1 | 0.1 X | 2.5 | 0.1 |
GSM1483829 | 85.5% | 40.0% | 97.1% | 0.4% | 96.8% | 100.0% | 16.4 | 0.5 X | 18.7 | 0.9 |
GSM1483830 | 85.9% | 39.4% | 97.6% | 95.3% | 100.0% | 7.3 | 0.2 X | 9.5 | 0.5 | |
GSM1483831 | 89.2% | 40.7% | 98.4% | 94.5% | 100.0% | 0.4 | 0.0 X | 0.4 | 0.0 | |
GSM1483832 | 88.2% | 40.6% | 97.4% | 5.1% | 95.8% | 100.0% | 5.5 | 0.2 X | 6.3 | 0.4 |
GSM1483833 | 86.6% | 40.7% | 97.3% | 0.6% | 96.2% | 100.0% | 6.1 | 0.2 X | 7.1 | 0.4 |
GSM1483834 | 84.3% | 41.1% | 96.5% | 4.4% | 95.4% | 100.0% | 7.1 | 0.2 X | 8.2 | 0.5 |
GSM1483835 | 88.7% | 41.6% | 97.3% | 1.0% | 94.8% | 100.0% | 0.4 | 0.0 X | 0.5 | 0.0 |
GSM1483836 | 84.2% | 39.8% | 95.9% | 6.3% | 95.9% | 100.0% | 9.5 | 0.3 X | 10.8 | 0.6 |
GSM1483837 | 84.3% | 39.0% | 96.7% | 0.8% | 94.4% | 100.0% | 4.7 | 0.1 X | 5.5 | 0.3 |
GSM1483838 | 84.6% | 39.6% | 97.6% | 0.8% | 93.2% | 100.0% | 2.5 | 0.1 X | 2.8 | 0.1 |
GSM1483839 | 84.1% | 40.1% | 97.3% | 0.7% | 94.4% | 100.0% | 2.1 | 0.1 X | 2.3 | 0.1 |
GSM1483841 | 85.9% | 41.8% | 97.3% | 0.8% | 92.3% | 100.0% | 0.1 | 0.0 X | 0.1 | 0.0 |
Workflow explanation
Assembly stats
fastp
fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)
Filtered Reads
Filtering statistics of sampled reads.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Duplication Rates
Duplication rates of sampled reads.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Sequence Quality
Average sequencing quality over each base of all reads.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
GC Content
Average GC content over each base of all reads.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
N content
Average N content over each base of all reads.
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Picard
Picard is a set of Java command line tools for manipulating high-throughput sequencing data.
Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
READS_UNMAPPED = UNMAPPED_READS
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
SamTools pre-sieve
Samtools is a suite of programs for interacting with high-throughput sequencing data.
The pre-sieve statistics are quality metrics measured before applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, read length filtering, and tn5 shift.Percent Mapped
Alignment metrics from samtools stats
; mapped vs. unmapped reads.
For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.
Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Alignment metrics
This module parses the output from samtools stats
. All numbers in millions.
deepTools
deepTools is a suite of tools to process and analyze deep sequencing data.
PCA plot
PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads
Fingerprint plot
Signal fingerprint according to plotFingerprint
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
Strandedness
Strandedness package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.
Sequencing strandedness was inferred for the following samples, and was called if 60% of the sampled reads were explained by either sense (forward) or antisense (reverse).Infer experiment
Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).
Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).
deepTools - Spearman correlation heatmap of reads in bins across the genome
Spearman correlation plot generated by deeptools. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.
deepTools - Pearson correlation heatmap of reads in bins across the genome
Pearson correlation plot generated by deeptools. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.
dupRadar
Figures generated by [dupRadar](https://bioconductor.riken.jp/packages/3.4/bioc/vignettes/dupRadar/inst/doc/dupRadar.html#plotting-and-interpretation). Click the link for help with interpretation.