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        Note that additional data was saved in multiqc_hg38_data when this report was generated.

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.14

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        These samples were run by seq2science v1.2.2, 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
        November 19, 2024
        Project
        no_P7
        Contact E-mail
        slrinzema@science.ru.nl

        Report generated on 2024-11-19, 14:46 CET based on data in:

        Change sample names:

        General Statistics

        Showing 44/44 rows and 14/29 columns.
        Sample Name% DuplicationM Reads After FilteringGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqsGenome coverageM Genome readsM MT genome reads
        18408_CL_RNA12_P5_plusTA_A52
        32.7%
        44.5
        50.2%
        94.6%
        0.0%
        214 bp
        39.1%
        100.0%
        43.4
        100.0%
        38.5
        0.8 X
        58.9
        0.6
        18409_CL_RNA11_P5_minusTA_A51
        30.6%
        46.4
        50.6%
        94.2%
        0.1%
        208 bp
        37.0%
        100.0%
        45.2
        100.0%
        39.6
        0.9 X
        64.1
        0.7
        18410_CL_RNA10_P4_plusTA_A50
        27.3%
        44.4
        50.4%
        95.3%
        0.0%
        213 bp
        32.8%
        100.0%
        43.2
        100.0%
        38.5
        0.8 X
        58.6
        0.5
        18411_CL_RNA9_P4_minusTA_A49
        35.8%
        46.6
        49.4%
        97.2%
        0.0%
        214 bp
        41.6%
        100.0%
        45.4
        100.0%
        40.5
        0.9 X
        63.1
        0.5
        18412_CL_RNA8_P3_plusTA_A48
        47.2%
        40.4
        50.6%
        95.1%
        0.0%
        200 bp
        54.9%
        100.0%
        39.1
        100.0%
        34.0
        0.8 X
        58.6
        0.5
        18413_CL_RNA7_P3_minusTA_A47
        41.2%
        39.4
        51.4%
        94.5%
        0.0%
        196 bp
        48.6%
        100.0%
        38.2
        100.0%
        33.1
        0.8 X
        56.3
        0.5
        18414_CL_RNA6_P2_plusTA_A46
        38.1%
        41.7
        50.6%
        94.3%
        0.0%
        200 bp
        45.1%
        100.0%
        40.6
        100.0%
        35.6
        0.8 X
        58.4
        0.6
        18415_CL_RNA5_P2_minusTA_A45
        27.4%
        41.7
        49.1%
        95.5%
        0.0%
        212 bp
        32.7%
        100.0%
        40.5
        100.0%
        36.4
        0.7 X
        53.8
        0.7
        18416_CL_RNA4_P1_plusTA_A44
        47.3%
        47.0
        49.6%
        95.7%
        0.0%
        196 bp
        54.8%
        100.0%
        45.7
        100.0%
        41.1
        0.8 X
        59.6
        0.7
        18417_CL_RNA3_P1_minusTA_A43
        31.7%
        45.8
        49.4%
        95.0%
        0.0%
        207 bp
        37.9%
        100.0%
        44.5
        100.0%
        39.9
        0.8 X
        58.4
        0.7
        18418_CL_RNA2_PBMC_plusTA_A42
        37.4%
        40.8
        48.7%
        94.0%
        0.0%
        205 bp
        45.0%
        100.0%
        39.7
        100.0%
        35.1
        0.8 X
        57.9
        0.7
        18419_CL_RNA1_PBMC_minusTA_A41
        25.3%
        43.3
        47.0%
        96.8%
        0.0%
        209 bp
        29.5%
        100.0%
        42.1
        100.0%
        38.6
        0.7 X
        54.3
        0.6
        44115_Exp188_MvdB_RNA_1_DMSO_A10
        35.3%
        61.5
        46.9%
        98.8%
        0.1%
        261 bp
        41.8%
        100.0%
        60.0
        100.0%
        54.9
        1.5 X
        78.4
        0.4
        44116_Exp188_MvdB_RNA_2_TA_1um_A11
        49.1%
        50.9
        49.7%
        98.7%
        0.1%
        291 bp
        57.8%
        100.0%
        49.4
        100.0%
        44.2
        1.3 X
        69.2
        0.5
        44117_Exp188_MvdB_RNA_3_LPS_10ngml_A12
        49.3%
        50.0
        47.6%
        98.5%
        0.1%
        251 bp
        57.9%
        100.0%
        48.6
        100.0%
        44.3
        1.2 X
        63.8
        0.4
        44118_Exp188_MvdB_RNA_4_LPSTA_A14
        42.8%
        53.3
        46.7%
        98.9%
        0.1%
        280 bp
        51.3%
        100.0%
        51.8
        100.0%
        46.7
        1.4 X
        72.2
        0.5
        44119_Exp188_MvdB_RNA_5_4h_ctrl_A20
        47.4%
        41.5
        49.6%
        98.6%
        0.1%
        276 bp
        56.2%
        100.0%
        40.4
        100.0%
        36.0
        1.1 X
        57.1
        0.7
        44120_Exp188_MvdB_RNA_6_4h_triam_A23
        44.5%
        48.4
        48.9%
        98.8%
        0.1%
        261 bp
        52.5%
        100.0%
        47.3
        100.0%
        42.7
        1.2 X
        64.1
        0.7
        44121_Exp188_MvdB_RNA_7_4h_LPS_A24
        52.6%
        58.6
        49.6%
        98.9%
        0.1%
        234 bp
        61.4%
        100.0%
        57.5
        100.0%
        52.4
        1.4 X
        71.6
        1.2
        44122_Exp188_MvdB_RNA_8_4h_triam__LPS_A25
        50.5%
        51.2
        51.4%
        98.6%
        0.1%
        237 bp
        60.3%
        100.0%
        50.1
        100.0%
        41.9
        1.6 X
        85.8
        1.0
        44123_Exp188_MvdB_RNA_9_3d_Ctrl_A27
        51.5%
        58.5
        50.0%
        98.5%
        0.1%
        232 bp
        60.7%
        100.0%
        57.3
        100.0%
        48.4
        1.8 X
        96.4
        1.2
        44124_Exp188_MvdB_RNA_10_3d_triam_A28
        61.3%
        54.4
        49.3%
        98.9%
        0.1%
        291 bp
        71.1%
        100.0%
        53.3
        100.0%
        48.3
        1.4 X
        71.3
        0.9
        44125_Exp188_MvdB_RNA_11_5d_Ctrl_A29
        61.3%
        37.8
        49.9%
        98.6%
        0.1%
        246 bp
        71.1%
        100.0%
        36.9
        100.0%
        33.1
        1.0 X
        52.3
        0.5
        44126_Exp188_MvdB_RNA_12_5d_triam_A30
        60.3%
        47.1
        50.5%
        98.6%
        0.1%
        301 bp
        70.4%
        100.0%
        46.2
        100.0%
        40.9
        1.3 X
        67.6
        0.7
        45611_Exp207_MvdB_A_MedCtrl_DMSO_EtOH_A76
        33.1%
        44.8
        51.8%
        98.9%
        0.1%
        219 bp
        39.2%
        100.0%
        43.7
        100.0%
        38.7
        1.2 X
        60.6
        0.9
        45612_Exp207_MvdB_B_uMTA_EthOH_A77
        40.5%
        51.2
        51.3%
        99.1%
        0.1%
        204 bp
        47.2%
        100.0%
        50.2
        100.0%
        44.9
        1.3 X
        67.3
        1.1
        45613_Exp307_FvdC_Corsk_OH_A78
        37.4%
        43.5
        50.4%
        99.1%
        0.1%
        212 bp
        43.8%
        100.0%
        42.7
        100.0%
        38.6
        1.1 X
        55.5
        0.9
        45614_Exp407_MvdD_Dorsk_1umOH_A79
        33.9%
        39.3
        51.0%
        98.8%
        0.1%
        204 bp
        39.9%
        100.0%
        38.5
        100.0%
        34.7
        1.0 X
        50.0
        0.8
        45615_Exp207_MvdB_1_4hr_stim_4hr_DMSO_A80
        38.8%
        38.7
        51.8%
        98.9%
        0.1%
        218 bp
        45.7%
        100.0%
        38.0
        100.0%
        33.8
        1.0 X
        52.2
        0.7
        45616_Exp207_MvdB_2_4hr_stim_4hr_1umTA_A82
        34.8%
        47.3
        52.0%
        99.1%
        0.1%
        212 bp
        41.2%
        100.0%
        46.5
        100.0%
        41.5
        1.2 X
        61.6
        1.0
        45617_Exp207_MvdB_3_28hr_after24hr_4hr_DMSO_A83
        51.8%
        45.9
        52.5%
        99.0%
        0.1%
        189 bp
        59.4%
        100.0%
        45.1
        100.0%
        40.5
        1.1 X
        58.7
        1.1
        45618_Exp207_MvdB_4_28hr_after24hr_4hr_1uMTA_A90
        58.3%
        55.0
        51.5%
        99.1%
        0.1%
        220 bp
        68.2%
        100.0%
        54.0
        100.0%
        49.0
        1.3 X
        68.6
        1.5
        45619_Exp207_MvdB_5_76hr_after72hr_4hr_DMSO_A91
        43.2%
        33.2
        49.5%
        98.8%
        0.1%
        210 bp
        55.2%
        100.0%
        32.4
        100.0%
        29.5
        0.8 X
        41.5
        0.6
        45620_Exp207_MvdB_6_76hr_after72hr_4hr_1uMTA_A92
        47.6%
        64.4
        51.7%
        99.2%
        0.1%
        228 bp
        60.7%
        100.0%
        63.4
        100.0%
        57.8
        1.6 X
        81.7
        1.4
        45621_Exp207_MvdB_7_76hr_norest_76hr_DMSO_A73
        24.0%
        51.1
        50.6%
        98.9%
        0.1%
        241 bp
        29.8%
        100.0%
        50.3
        100.0%
        46.4
        1.2 X
        61.5
        1.1
        45622_Exp207_MvdB_8_76hr_norest_76hr_1uMTA_A74
        36.6%
        70.1
        50.9%
        98.6%
        0.1%
        225 bp
        44.5%
        100.0%
        69.0
        100.0%
        63.1
        1.6 X
        83.0
        1.8
        46164_Exp222_MvdB_RNA_1_medium_PBS_DMSO_PBS_A43
        36.3%
        18.9
        56.2%
        98.4%
        0.1%
        215 bp
        50.2%
        100.0%
        18.6
        100.0%
        8.5
        1.5 X
        76.9
        0.3
        46165_Exp222_MvdB_RNA_2_medium_PBS_TA1uM_PBS_A44
        11.3%
        15.6
        49.8%
        99.2%
        0.1%
        223 bp
        16.0%
        100.0%
        15.4
        100.0%
        14.3
        0.3 X
        18.3
        0.3
        46166_Exp222_MvdB_RNA_3_medium_PBS_LPS10ngml_DMSO_A45
        33.8%
        62.6
        48.2%
        99.3%
        0.1%
        225 bp
        38.2%
        100.0%
        61.7
        100.0%
        57.8
        1.4 X
        73.2
        1.1
        46167_Exp222_MvdB_RNA_4_medium_PBS_TA1uM__LPS10ngml_A62
        13.6%
        21.5
        47.4%
        99.3%
        0.1%
        231 bp
        19.1%
        100.0%
        21.2
        100.0%
        19.9
        0.5 X
        24.9
        0.4
        46168_Exp222_MvdB_RNA_5_medium_1ugmlBGluc__DMSO_A63
        13.2%
        18.6
        47.8%
        99.0%
        0.1%
        225 bp
        18.5%
        100.0%
        18.3
        100.0%
        17.1
        0.4 X
        21.2
        0.4
        46169_Exp222_MvdB_RNA_6_medium_1ugmlBGluc_TA1uM__PBS_A78
        13.7%
        17.3
        48.8%
        98.9%
        0.1%
        242 bp
        19.4%
        100.0%
        17.1
        100.0%
        15.7
        0.4 X
        21.6
        0.4
        46170_Exp222_MvdB_RNA_7_medium_1ugmlBGluc_LPS10ngml__DMSO_A79
        12.0%
        18.8
        47.7%
        98.9%
        0.1%
        220 bp
        16.8%
        100.0%
        18.5
        100.0%
        17.3
        0.4 X
        21.7
        0.3
        46171_Exp222_MvdB_RNA_8_medium_1ugmlBGluc_TA1uM__LPS10ngml_A75
        15.4%
        16.6
        46.5%
        99.2%
        0.1%
        224 bp
        21.2%
        100.0%
        16.4
        100.0%
        15.5
        0.4 X
        18.6
        0.3

        Workflow explanation

        Preprocessing of reads was done automatically by seq2science v1.2.2 using the rna-seq workflow. Paired-end reads were trimmed with fastp v0.23.2 with default options. Genome assembly hg38 was downloaded with genomepy 0.16.1. Reads were aligned with STAR v2.7.10b with default options. Afterwards, duplicate reads were marked with Picard MarkDuplicates v3.0.0. General alignment statistics were collected by samtools stats v1.16. Sample sequencing strandedness was inferred using RSeQC v5.0.1 in order to improve quantification accuracy. Deeptools v3.5.1 was used for the fingerprint, profile, correlation and dendrogram/heatmap plots, where the heatmap was made with options '--distanceBetweenBins 9000 --binSize 1000'. Read counting and summarizing to gene-level was performed on filtered bam using HTSeq-count v2.0.2. RNA-seq read duplication types were analyzed using dupRadar v1.28.0. The UCSC genome browser was used to visualize and inspect alignment. Differential gene expression analysis was performed using DESeq2 v1.34. To adjust for multiple testing the (default) Benjamini-Hochberg procedure was performed with an FDR cutoff of 0.1 (default is 0.1). Counts were log transformed using the (default) shrinkage estimator apeglm v1.16. TPM normalized gene counts were generated using genomepy based on longest transcript lengths. Quality control metrics were aggregated by MultiQC v1.14.

        Assembly stats

        Genome assembly hg38 contains of 194 contigs, with a GC-content of 40.87%, and 4.88% consists of the letter N. The N50-L50 stats are 145138636-9 and the N75-L75 stats are 114364328-14. The genome annotation contains 42555 genes.

        DESeq2 - MA plot for contrast 4h-Mf-CD14-only_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - MA plot for contrast 4h-Mf-CD14-extra-compound_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for 3days_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - PCA plot for 4h-Mf-CD14-only_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast 3days_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for 3or5days-no-BG_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast 3or5days_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for 3or5days_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast 4h-PBMC_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for 4h-PBMC_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - PCA plot for 4h-Mf-CD14-extra-compound_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - PCA plot for 3or5days-with-BG_TA_DMSO

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast 3or5days-no-BG_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - MA plot for contrast 3or5days-with-BG_TA_DMSO

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.

        Filtered Reads

        Filtering statistics of sampled reads.

        loading..

        Insert Sizes

        Insert size estimation of sampled reads.

        loading..

        Sequence Quality

        Average sequencing quality over each base of all reads.

        loading..

        GC Content

        Average GC content over each base of all reads.

        loading..

        N content

        Average N content over each base of all reads.

        loading..

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        loading..

        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
        loading..

        SamTools pre-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        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).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        SamTools post-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        The post-sieve statistics are quality metrics measured after applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, 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).

        loading..

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        loading..

        deepTools

        deepTools is a suite of tools to process and analyze deep sequencing data.DOI: 10.1093/nar/gkw257.

        PCA plot

        PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads

        loading..

        Fingerprint plot

        Signal fingerprint according to plotFingerprint

        loading..

        Strandedness

        Strandedness package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.DOI: 10.1093/bioinformatics/bts356.

        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).

        loading..

        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.

        DESeq2 - Sample distance cluster heatmap of counts

        Euclidean distance between samples, based on variance stabilizing transformed counts (RNA: expressed genes, ChIP: bound regions, ATAC: accessible regions). Gives us an overview of similarities and dissimilarities between samples.

        DESeq2 - Spearman correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        DESeq2 - Pearson correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.

        DESeq2 - MA plot for contrast day5-LPSany-4h_LPS_NoLPS

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for day5-LPSany-4h_LPS_NoLPS

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast Forskolin-P1-P8_Forskolin_DMSOany

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for Forskolin-P1-P8_Forskolin_DMSOany

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast PBMC-LPSany-4h_LPS_NoLPS

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for PBMC-LPSany-4h_LPS_NoLPS

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast celltype_PBMC_Mf.CD14

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for celltype_PBMC_Mf.CD14

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast time_76_120

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for time_76_120

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast time_4_120

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for time_4_120

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast time_4_76

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for time_4_76

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast BG-noLPS-5days_BG_NoBG

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for BG-noLPS-5days_BG_NoBG

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast day1-LPSonly-4h_LPS_NoLPS

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for day1-LPSonly-4h_LPS_NoLPS

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast BG-any-5days_BG_NoBG

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for BG-any-5days_BG_NoBG

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast Forskolin-P8_Forskolin_DMSO.TA

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for Forskolin-P8_Forskolin_DMSO.TA

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast time_4_28

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for time_4_28

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast day1-LPSany-4h_LPS_NoLPS

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for day1-LPSany-4h_LPS_NoLPS

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        DESeq2 - MA plot for contrast day5-LPSonly-4h_LPS_NoLPS

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for day5-LPSonly-4h_LPS_NoLPS

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        Samples & Config

        The samples file used for this run:

        sample descriptive_name Forskolin-P8 Forskolin-P1-P8 Ionomycin-P7 Ionomycin-P1-P8 PMA10-P7 PMA50-P7 PMA-P7 PMA-DMSO-P1-P8 PMA10-P1-P8 PMA50-P1-P8 PMA-P1-P8 PMA-TA-P1-P8 PMA-any-P1-P8 BG-noLPS-5days BG-any-5days day1-LPSonly-4h day1-LPSany-4h day5-LPSonly-4h day5-LPSany-4h PBMC-LPSany-4h 4h-Mf-CD14-only 4h-Mf-CD14-PMA 4h-Mf-CD14-extra-compound 4h-PBMC 3days 3or5days 3or5days-no-BG 3or5days-with-BG celltype time assembly
        18417_CL_RNA3_P1_minusTA_A43 OA_P1_day1 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        18416_CL_RNA4_P1_plusTA_A44 OA_P1_day1_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        18415_CL_RNA5_P2_minusTA_A45 OA_P2_day1 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        18414_CL_RNA6_P2_plusTA_A46 OA_P2_day1_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        18413_CL_RNA7_P3_minusTA_A47 OA_P3_day1 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        18412_CL_RNA8_P3_plusTA_A48 OA_P3_day1_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        18411_CL_RNA9_P4_minusTA_A49 OA_P4_day1 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        18410_CL_RNA10_P4_plusTA_A50 OA_P4_day1_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        18409_CL_RNA11_P5_minusTA_A51 OA_P5_day1 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        18408_CL_RNA12_P5_plusTA_A52 OA_P5_day1_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        44119_Exp188_MvdB_RNA_5_4h_ctrl_A20 OA_P6 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        44120_Exp188_MvdB_RNA_6_4h_triam_A23 OA_P6_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        44121_Exp188_MvdB_RNA_7_4h_LPS_A24 OA_P6_LPS4h DMSOany LPS LPS DMSO Mf.CD14 4.0 hg38
        44122_Exp188_MvdB_RNA_8_4h_triam__LPS_A25 OA_P6_TA_LPS4h DMSOany LPS TA Mf.CD14 4.0 hg38
        45611_Exp207_MvdB_A_MedCtrl_DMSO_EtOH_A76 OA_P8 DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        45612_Exp207_MvdB_B_uMTA_EthOH_A77 OA_P8_TA4h DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        45613_Exp307_FvdC_Corsk_OH_A78 OA_P8_forsk4h Forskolin Forskolin DMSOany NoLPS Mf.CD14 4.0 hg38
        45614_Exp407_MvdD_Dorsk_1umOH_A79 OA_P8_TA_forsk4h Forskolin Forskolin DMSOany NoLPS TA Mf.CD14 4.0 hg38
        45615_Exp207_MvdB_1_4hr_stim_4hr_DMSO_A80 OA_P8_4h DMSO.TA DMSOany NoIonomycin DMSO.TA DMSOany DMSOany DMSOany DMSOany NoLPS NoLPS DMSO DMSO Mf.CD14 4.0 hg38
        45616_Exp207_MvdB_2_4hr_stim_4hr_1umTA_A82 OA_P8_4h_TA4h DMSO.TA DMSOany NoIonomycin DMSOany DMSOany DMSOany DMSOany DMSOany NoLPS TA TA Mf.CD14 4.0 hg38
        45617_Exp207_MvdB_3_28hr_after24hr_4hr_DMSO_A83 OA_P8_24h DMSO Mf.CD14 28.0 hg38
        45618_Exp207_MvdB_4_28hr_after24hr_4hr_1uMTA_A90 OA_P8_24h_TA4h Mf.CD14 28.0 hg38
        44123_Exp188_MvdB_RNA_9_3d_Ctrl_A27 OA_P6_day3 DMSO DMSO Mf.CD14 76.0 hg38
        44124_Exp188_MvdB_RNA_10_3d_triam_A28 OA_P6_day3_TA72h TA TA Mf.CD14 76.0 hg38
        45619_Exp207_MvdB_5_76hr_after72hr_4hr_DMSO_A91 OA_P8_72h DMSO DMSO Mf.CD14 76.0 hg38
        45620_Exp207_MvdB_6_76hr_after72hr_4hr_1uMTA_A92 OA_P8_72h_TA4h TA TA Mf.CD14 76.0 hg38
        45621_Exp207_MvdB_7_76hr_norest_76hr_DMSO_A73 OA_P8_76h DMSO DMSO Mf.CD14 76.0 hg38
        45622_Exp207_MvdB_8_76hr_norest_76hr_1uMTA_A74 OA_P8_TA76h TA TA Mf.CD14 76.0 hg38
        44125_Exp188_MvdB_RNA_11_5d_Ctrl_A29 OA_P6_day5 DMSO DMSO DMSO Mf.CD14 120.0 hg38
        44126_Exp188_MvdB_RNA_12_5d_triam_A30 OA_P6_day5_TA124h TA TA TA Mf.CD14 120.0 hg38
        46164_Exp222_MvdB_RNA_1_medium_PBS_DMSO_PBS_A43 OA_P9_120h NoBG NoBG NoLPS NoLPS DMSO DMSO Mf.CD14 120.0 hg38
        46165_Exp222_MvdB_RNA_2_medium_PBS_TA1uM_PBS_A44 OA_P9_120h_TA4h NoBG NoBG NoLPS TA TA Mf.CD14 120.0 hg38
        46166_Exp222_MvdB_RNA_3_medium_PBS_LPS10ngml_DMSO_A45 OA_P9_120h_LPS4h NoBG LPS LPS DMSO Mf.CD14 120.0 hg38
        46167_Exp222_MvdB_RNA_4_medium_PBS_TA1uM__LPS10ngml_A62 OA_P9_120h_TALPS4h NoBG LPS TA Mf.CD14 120.0 hg38
        46168_Exp222_MvdB_RNA_5_medium_1ugmlBGluc__DMSO_A63 OA_P9_120hBG BG BG NoLPS NoLPS DMSO Mf.CD14 120.0 hg38
        46169_Exp222_MvdB_RNA_6_medium_1ugmlBGluc_TA1uM__PBS_A78 OA_P9_120hBG_TA4h BG BG NoLPS TA Mf.CD14 120.0 hg38
        46170_Exp222_MvdB_RNA_7_medium_1ugmlBGluc_LPS10ngml__DMSO_A79 OA_P9_120hBG_LPS4h BG LPS LPS DMSO Mf.CD14 120.0 hg38
        46171_Exp222_MvdB_RNA_8_medium_1ugmlBGluc_TA1uM__LPS10ngml_A75 OA_P9_120hBG_TALPS4h BG LPS TA Mf.CD14 120.0 hg38
        18419_CL_RNA1_PBMC_minusTA_A41 PBMC1 DMSO PBMC hg38
        18418_CL_RNA2_PBMC_plusTA_A42 PBMC1_TA4h TA PBMC hg38
        44115_Exp188_MvdB_RNA_1_DMSO_A10 PMBC2 NoLPS DMSO PBMC hg38
        44116_Exp188_MvdB_RNA_2_TA_1um_A11 PMBC2_TA4h NoLPS TA PBMC hg38
        44117_Exp188_MvdB_RNA_3_LPS_10ngml_A12 PMBC2_LPS4h LPS DMSO PBMC hg38
        44118_Exp188_MvdB_RNA_4_LPSTA_A14 PMBC2_TA_LPS4h LPS TA PBMC hg38

        The config file used for this run: 
        samples: samples.tsv
result_dir: .
genome_dir: ../genomes
fastq_dir: ../fastqs


# contact info for multiqc report and trackhub
email: slrinzema@science.ru.nl
create_trackhub: true


technical_replicates: merge
trimmer: fastp
quantifier: htseq  # or salmon or featurecounts
aligner: star
bam_sorter:
  samtools:
    coordinate

# filtering after alignment (not used for the gene counts matrix if the quantifier is Salmon)
remove_blacklist: true
min_mapping_quality: 255
only_primary_align: true
remove_dups: false

contrasts:
 - Forskolin-P8_Forskolin_DMSO.TA
 - Forskolin-P1-P8_Forskolin_DMSOany
 - BG-noLPS-5days_BG_NoBG
 - BG-any-5days_BG_NoBG
 - day1-LPSonly-4h_LPS_NoLPS
 - day1-LPSany-4h_LPS_NoLPS
 - day5-LPSonly-4h_LPS_NoLPS
 - day5-LPSany-4h_LPS_NoLPS
 - PBMC-LPSany-4h_LPS_NoLPS
 - 4h-Mf-CD14-only_TA_DMSO
 - 4h-Mf-CD14-extra-compound_TA_DMSO
 - 4h-PBMC_TA_DMSO
 - 3days_TA_DMSO
 - 3or5days_TA_DMSO
 - 3or5days-no-BG_TA_DMSO
 - 3or5days-with-BG_TA_DMSO
 - celltype_PBMC_Mf.CD14
 - time_4_28
 - time_4_76
 - time_4_120
 - time_76_120

        MultiQC v1.14 - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.