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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_mm10_mcherry_ERCC_reporter_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 1.11

        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 v0.5.4, 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
        scrna-seq
        Date
        August 13, 2021
        Project
        Klaas_kalisto
        Contact E-mail
        jsmits@science.ru.nl

        Report generated on 2021-08-13, 18:01 based on data in:

        Change sample names:

        General Statistics

        Showing 3/3 rows and 7/12 columns.
        Sample Name% DuplicationGC content% PF% AdapterM Bus RecordsM Distinct UMIs% Whitelisted reads
        LW35_merged
        76.2%
        42.5%
        99.1%
        7.64
        0.07
        81.7%
        LW52-Friedl-plate1CTCs_Z44_S1
        61.1%
        53.5%
        98.3%
        3.24
        0.07
        73.1%
        LW52-Friedl-plate2-CTCs_Z45_S2
        69.6%
        55.2%
        97.9%
        0.8%
        1.21
        0.07
        65.9%

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.5.4. Single-end reads were trimmed with fastp v0.20.1 with default options. Genome assembly mm10 was downloaded with genomepy 0.9.3. The genome and gene annotations was extended with custom regions. Reads were aligned and transformed to bus format with kb-python v0.26.3, a python wrapper for kallisto and bustools. Quality control metrics were aggregated by MultiQC v1.11.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)

        Filtered Reads

        Filtering statistics of sampled reads.

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        Duplication Rates

        Duplication rates of sampled reads.

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        Sequence Quality

        Average sequencing quality over each base of all reads.

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        GC Content

        Average GC content over each base of all reads.

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        N content

        Average N content over each base of all reads.

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        Bustools

        Bustools works with BUS files - a file format for single-cell RNA-seq data designed to facilitate the development of modular workflows for data processing.

        Summary table

        This is a table of the complete output of bustools inspect. Note that some columns are hidden by default (click Configure Columns to show).

        Showing 3/3 rows and 10/16 columns.
        Sample NameM Bus RecordsM ReadsBarcodesMean reads per barcodeM Distinct UMIsM Distinct barcode-UMIMean UMIs per barcodeM 2xdepth records% Whitelisted barcodes% Whitelisted reads
        LW35_merged
        7.64
        159.20
        62157
        2561.24
        0.07
        4.40
        70.74
        4.34
        0.6%
        81.7%
        LW52-Friedl-plate1CTCs_Z44_S1
        3.24
        18.83
        55727
        337.82
        0.07
        2.30
        41.27
        1.30
        0.7%
        73.1%
        LW52-Friedl-plate2-CTCs_Z45_S2
        1.21
        7.09
        45712
        155.15
        0.07
        0.93
        20.30
        0.49
        0.8%
        65.9%

        Mean number of UMIs per barcode

        Average number of UMIs (unique molecular identifiers) per barcode

        Each unique barcode represents a cell and each Unique Molecular Identifier (UMI) represents a unique transcript molecule. By counting the mean number of UMIs per barcode, you effectively calculate the average number of unique transcripts per cell.

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        Percentage in whitelist

        The whitelist is a list of unique barcodes used in your protocol, either provided or inferred from the data.

        Each unique barcode from the whitelist represents a cell. The percentage of reads with barcode / barcodes in the whitelist is a measure of percentage of reads that could be asigned to a cell.

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        Samples & Config

        The samples file used for this run:

        sample assembly descriptive_name
        LW52-Friedl-plate1CTCs_Z44_S1 mm10 LW52_plate1
        LW52-Friedl-plate2-CTCs_Z45_S2 mm10 LW52_plate2
        LW35_merged mm10 LW35_merged

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples.tsv

# pipeline file locations
result_dir: ./results  # where to store results
genome_dir: /ceph/rimlsfnwi/data/moldevbio/zhou/jsmits/genomepy_genomes/  # where to look for or download the genomes
fastq_dir: ./fastq # where to look for fastqs
# contact info for multiqc report and trackhub
email: jsmits@science.ru.nl

fqext1: R1_001
fqext2: R2_001

# how to handle replicates
technical_replicates: merge

# scRNA options
# seq2science does currently not support scrna-seq platforms that generate more than two fastq files, such as 10xv1.
quantifier:
  kallistobus:
    ## Velocity example ##
    #ref: '--workflow lamanno'
    #count: '-x 1,8,16:1,0,8:0,0,0 --verbose --workflow lamanno --loom'

    ## Quantification example ##
    #count: '-x 0,8,16:0,0,8:1,0,0 --h5ad --verbose'
    #count: '-x 10XV3 --h5ad --verbose'

    ## Velocity example ##
    #ref: '--workflow lamanno'
    #count: '-x 0,8,16:0,0,8:1,0,0 --h5ad --verbose --workflow lamanno'
    #count: '-x 10XV3 --verbose --h5ad --workflow lamanno'
    ref: '--workflow lamanno'
    #count: '-x 0,8,16:0,0,8:1,0,0 --h5ad --verbose --workflow lamanno'
    count: '-x 1,8,16:1,0,8:0,0,0 --verbose --workflow lamanno --loom'



custom_annotation_extension: /ceph/rimlsfnwi/data/moldevbio/zhou/jsmits/sabine_Friedl/mcherry.gtf
custom_genome_extension: /ceph/rimlsfnwi/data/moldevbio/zhou/jsmits/sabine_Friedl/ERCC_mCherry.fastq
custom_assembly_suffix: "_mcherry_ERCC_reporter"
barcodefile: "/ceph/rimlsfnwi/data/moldevbio/zhou/jsmits/scRNAseq/1col_barcode_384.tab"

        MultiQC v1.11 - Written by Phil Ewels, available on GitHub.

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