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multiqc_config.yml
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218 lines (215 loc) · 10.9 KB
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report_comment: >
This report has been generated by the <a href="https://github.com/nf-core/nanostring/tree/dev" target="_blank">nf-core/nanostring</a> analysis pipeline. For information about how to interpret these results, please see the <a href="https://nf-co.re/nanostring/dev/docs/output" target="_blank">documentation</a>.
report_section_order:
BD:
order: 970
FOV:
order: 950
Posctrl_linearity:
order: 930
LOD:
order: 920
Pos:
order: 910
Neg:
order: 900
HK:
order: 890
Pos_vs_neg:
order: 880
POSF_vs_NEGF:
order: 870
AVG_vs_MED:
order: 860
AVG_vs_BD:
order: 850
PCA:
order: 840
PCA1_vs_PCA2:
order: 830
PCAi:
order: 820
HKF:
order: 810
plot_normf:
order: 800
nf-core-nanostring-hk-genes:
order: 790
normalized_qc:
order: 780
NSolver_Pathway_Heatmap:
order: 770
NSolver_CellType_Heatmap:
order: 760
Norm_GEX_HK:
order: 750
Norm_GEX_ENDO:
order: 740
No_HK_Norm_GEX_HK:
order: 730
No_HK_Norm_GEX_ENDO:
order: 720
gene_heatmap:
order: 710
wo_HKnorm_gene_heatmap:
order: 700
"nf-core-nanostring-methods-description":
order: -1000
software_versions:
order: -1001
"nf-core-nanostring-summary":
order: -1002
export_plots: true
disable_version_detection: true
# Run only these modules
run_modules:
- custom_content
custom_data:
BD:
id: BD
section_name: "Binding Density"
description: "The imaging unit only counts the codes that are unambiguously distinguishable. It simply will not count codes that overlap within an image. This provides increased confidence that the molecular counts you receive are from truly recognisable codes. Under most conditions, forgoing the few barcodes that do overlap will not impact your data.<br> Too many overlapping codes in the image, however, will create a condition called image saturation in which significant data loss could occur (critical data loss from saturation is uncommon).<br> To determine the level of image saturation, the nCounter instrument calculates the number of optical features per square micron for each lane as it processes the images. This is called the Binding Density (BD). The Binding Density is useful for determining whether data collection has been compromised due to image saturation. The acceptable range for Binding Density is:<br><br> <b>0.1 - 2.25</b> for MAX/FLEX instruments<br> <b>0.1 - 1.8</b> for SPRINT instruments<br><br> Within these ranges, relatively few reporters on the slide surface will overlap, enabling the instrument to accurately tabulate counts for each reporter species. A Binding Density significantly greater than the upper limit in either range is indicative of overlapping reporters on the slide surface. The counts observed in lanes with a Binding Density at this level may have had significant numbers of codes ignored, which could potentially affect quantification and linearity of the assay."
plot_normf:
id: plot_normf
section_name: "Normalization Result"
description: "This plot describes the normalization result obtained."
POSF_vs_NEGF:
id: POSF_vs_NEGF
section_name: "Positive Factor vs. Negative Factor"
description: "Positive factor plotted against the negative factor (background threshold). Values should be in the grey area of the plot, otherwise be investigated for potential failure. Red dots mark outliers."
AVG_vs_MED:
id: AVG_vs_MED
section_name: "Average Counts vs. Median Counts"
description: "The average counts plotted against the median counts."
PCAi:
id: PCAi
section_name: "Principal Component Analysis Inertia"
description: "Inertia of the analyzed data."
Pos:
id: Pos
section_name: "Positive Controls"
description: "Positive control spike in boxplots. Red dots mark outliers."
Neg:
id: Neg
section_name: "Negative Controls"
description: "Negative control spike in boxplots. Red dots mark outliers."
HK:
id: HK
section_name: "Housekeeping Genes"
description: "Box plots showing the housekeeping gene expression per cartridge. Red dots mark outliers."
Pos_vs_neg:
id: Pos_vs_neg
section_name: "Positive Counts vs. Negative Counts"
description: "Line plot showing the number of positive counts against the number of negative counts. Red dots mark outliers."
AVG_vs_BD:
id: AVG_vs_BD
section_name: "Average Counts vs. Binding Density"
description: "This plot shows the average counts vs the respective binding density and can be used to determine outliers that show very low average counts and suffer from too high binding density simultaneously. Red dots mark outliers."
PCA:
id: PCA
section_name: "Principal Component Analysis"
description: "Principal components planes."
PCA1_vs_PCA2:
id: PCA1_vs_PCA2
section_name: "PC1 vs PC2 (PCA)"
description: "Principal component 1 vs Principal component 2 plot."
HKF:
id: HKF
section_name: "Housekeeping Factors"
description: "Housekeeping factors. Red dots mark outliers."
FOV:
id: FOV
section_name: "FOV (Imaging)"
description: "Each individual lane scanned on an nCounter system is divided into a few hundred imaging sections, called Fields of View (FOV), the exact number of which will depend on the system being used (i.e., MAX/FLEX or SPRINT), and the scanner settings selected by the user. <br> The system images these FOVs separately, and sums the barcode counts of all FOVs from a single lane to form the final raw data count for each unique barcode target. Finally, the system reports the number of FOVs successfully imaged as FOV Counted. <br><br> Significant discrepancy between the number of FOV for which imaging was attempted (FOV Count) and for which imaging was successful (FOV Counted) may indicate an issue with imaging performance.<br> Recommended percentage of registered FOVs (i.e., FOV Counted over FOV Count) is <b>75%.</b>"
Posctrl_linearity:
id: Posctrl_linearity
section_name: "Positive Control Linearity"
description: "Six synthetic DNA control targets are included with every nCounter Gene Expression assay. <br> Their concentrations range linearly (in codeset) from <b>128</b><it>fM</it> to <b>0.125</b><it>fM</it>, and they are referred to as <b>POS_A</b> to <b>POS_F</b>, respectively.<br> These Positive Controls are typically used to measure the efficiency of the hybridization reaction, and their step-wise concentrations also make them useful in checking the linearity performance of the assay.<br><br> Since the known concentrations of the Positive Controls increase in a linear fashion, the resulting counts should, as well."
LOD:
id: LOD
section_name: "Limit Of Detection (LOD)"
description: "The Limit Of Detection (LoD) is determined by measuring the ability to detect <b>POS_E</b>, the <b>0.5 fM</b> positive control probe, which corresponds to about 10,000 copies of this target within each sample tube. <br> On a FLEX/MAX system, the standard input of 100<it>ng</it> of total RNA will roughly correspond to about 10,000 cell equivalents (assuming one cell contains 10 <it>pg</it> total RNA on average). <br> An nCounter assay run on the FLEX/MAX system should thus conservatively be able to detect roughly one transcript copy per cell for each target (or 10,000 total transcript copies). <br> In most (codeset) assays, you will observe that even the <b>POS_F</b> probe (equivalent to <b>0.25</b> copies per cell) is detectable above background. Red dots mark outliers."
normalized_qc:
section_name: "Quality Control Metrics"
description: "QC metrics for the data provided by NACHO. Samples that pass the required thresholds are marked as <code>PASS</code>.<br><br>\n<b>Outlier Thresholds and Metrics:</b> <br><br> Binding Density (BD) < 0.1 and > 2.25 <br> Field of View (FoV) < 75 <br> Positive Control Linearity (PCL) < 0.95 <br> Limit of Detection (LoD) < 2 <br> Positive normalisation factor (Positive_factor) < 0.25 and > 4 <br> Housekeeping normalisation factor (house_factor) < 0.091 and > 11 <br> Mean Count (MC) <br> Median Count (MedC) <br>"
plot_type: "table"
headers:
"BD":
format: "{:,.3f}"
placement: 200
"BD QC":
description: "Binding Density QC"
placement: 300
"FoV":
format: "{:,.2f}"
placement: 400
"FoV QC":
description: "Field of View QC"
placement: 500
"PCL":
format: "{:,.3f}"
placement: 600
"PCL QC":
description: "Positive Control Linearity QC"
placement: 700
"LoD":
format: "{:,.3f}"
placement: 800
"LoD QC":
description: "Limit of Detection QC"
placement: 900
"MC":
format: "{:,.1f}"
placement: 1500
"MedC":
format: "{:,.1f}"
placement: 1600
"Positive Factor":
format: "{:,.3f}"
placement: 1000
"PNF QC":
description: "Positive Normalisation Factor QC"
placement: 1100
"House Factor":
format: "{:,.3f}"
placement: 1200
"HKNF QC":
description: "Housekeeping Normalization Factor QC"
placement: 1300
"Negative Factor":
format: "{:,.3f}"
placement: 1400
NSolver_Pathway_Heatmap:
section_name: "Pathway Score Heatmap (nSolver)"
description: "Pathway Score Heatmap based on nSolver generated scores."
NSolver_CellType_Heatmap:
section_name: "Celltype Score Heatmap (nSolver)"
description: "Celltype Score Heatmap based on nSolver generated scores."
Norm_GEX_HK:
id: Norm_GEX_HK
section_name: "Normalized count values for housekeeping genes."
description: "Normalized Housekeeping gene count table, including available metadata."
Norm_GEX_ENDO:
id: Norm_GEX_ENDO
section_name: "Normalized count values for endogenous genes."
description: "Normalized Endogenous gene count table, including available metadata."
No_HK_Norm_GEX_HK:
id: No_HK_Norm_GEX_HK
section_name: "Non-HK-Normalized count values for housekeeping genes."
description: "Non-Housekeeping-Normalized Housekeeping gene count table, including available metadata."
No_HK_Norm_GEX_ENDO:
id: No_HK_Norm_GEX_ENDO
section_name: "Non-HK-Normalized count values for endogenous genes."
description: "Non-Housekeeping-Normalized Endogenous gene count table, including available metadata."
gene_heatmap:
section_name: "Count-Heatmap for all or selected set of target genes"
description: "Normalized and log2+1 transformed gene expression for all or selected set of target genes."
wo_HKnorm_gene_heatmap:
section_name: "Count-Heatmap for all or selected set of target genes (Non-HK-normalized)"
description: "Normalized and log2+1 transformed gene expression for all or selected set of target genes based on the Non-HK-normalized results."
genescore_plage:
section_name: "GeneScores"
description: "Genescores computed with the PLAGE algorithm using the normalized gene expression values for all samples."
plot_type: "table"
pconfig:
format: "{:.3f}"