LogoLogo
Illumina KnowledgeIllumina SupportSign In
Partek
  • Home
Partek
  • Overview
  • Partek Flow
    • Frequently Asked Questions
      • General
      • Visualization
      • Statistics
      • Biological Interpretation
      • How to cite Partek software
    • Quick Start Guide
    • Installation Guide
      • Minimum System Requirements
      • Single Cell Toolkit System Requirements
      • Single Node Installation
      • Single Node Amazon Web Services Deployment
      • Multi-Node Cluster Installation
      • Creating Restricted User Folders within the Partek Flow server
      • Updating Partek Flow
      • Uninstalling Partek Flow
      • Dependencies
      • Docker and Docker-compose
      • Java KeyStore and Certificates
      • Kubernetes
    • Live Training Event Recordings
      • Bulk RNA-Seq Analysis Training
      • Basic scRNA-Seq Analysis & Visualization Training
      • Advanced scRNA-Seq Data Analysis Training
      • Bulk RNA-Seq and ATAC-Seq Integration Training
      • Spatial Transcriptomics Data Analysis Training
      • scRNA and scATAC Data Integration Training
    • Tutorials
      • Creating and Analyzing a Project
        • Creating a New Project
        • The Metadata Tab
        • The Analyses Tab
        • The Log Tab
        • The Project Settings Tab
        • The Attachments Tab
        • Project Management
        • Importing a GEO / ENA project
      • Bulk RNA-Seq
        • Importing the tutorial data set
        • Adding sample attributes
        • Running pre-alignment QA/QC
        • Trimming bases and filtering reads
        • Aligning to a reference genome
        • Running post-alignment QA/QC
        • Quantifying to an annotation model
        • Filtering features
        • Normalizing counts
        • Exploring the data set with PCA
        • Performing differential expression analysis with DESeq2
        • Viewing DESeq2 results and creating a gene list
        • Viewing a dot plot for a gene
        • Visualizing gene expression in Chromosome view
        • Generating a hierarchical clustering heatmap
        • Performing biological interpretation
        • Saving and running a pipeline
      • Analyzing Single Cell RNA-Seq Data
      • Analyzing CITE-Seq Data
        • Importing Feature Barcoding Data
        • Data Processing
        • Dimensionality Reduction and Clustering
        • Classifying Cells
        • Differentially Expressed Proteins and Genes
      • 10x Genomics Visium Spatial Data Analysis
        • Start with pre-processed Space Ranger output files
        • Start with 10x Genomics Visium fastq files
        • Spatial data analysis steps
        • View tissue images
      • 10x Genomics Xenium Data Analysis
        • Import 10x Genomics Xenium Analyzer output
        • Process Xenium data
        • Perform Exploratory analysis
        • Make comparisons using Compute biomarkers and Biological interpretation
      • Single Cell RNA-Seq Analysis (Multiple Samples)
        • Getting started with the tutorial data set
        • Classify cells from multiple samples using t-SNE
        • Compare expression between cell types with multiple samples
      • Analyzing Single Cell ATAC-Seq data
      • Analyzing Illumina Infinium Methylation array data
      • NanoString CosMx Tutorial
        • Importing CosMx data
        • QA/QC, data processing, and dimension reduction
        • Cell typing
        • Classify subpopulations & differential expression analysis
    • User Manual
      • Interface
      • Importing Data
        • SFTP File Transfer Instructions
        • Import single cell data
        • Importing 10x Genomics Matrix Files
        • Importing and Demultiplexing Illumina BCL Files
        • Partek Flow Uploader for Ion Torrent
        • Importing 10x Genomics .bcl Files
        • Import a GEO / ENA project
      • Task Menu
        • Task actions
        • Data summary report
        • QA/QC
          • Pre-alignment QA/QC
          • ERCC Assessment
          • Post-alignment QA/QC
          • Coverage Report
          • Validate Variants
          • Feature distribution
          • Single-cell QA/QC
          • Cell barcode QA/QC
        • Pre-alignment tools
          • Trim bases
          • Trim adapters
          • Filter reads
          • Trim tags
        • Post-alignment tools
          • Filter alignments
          • Convert alignments to unaligned reads
          • Combine alignments
          • Deduplicate UMIs
          • Downscale alignments
        • Annotation/Metadata
          • Annotate cells
          • Annotation report
          • Publish cell attributes to project
          • Attribute report
          • Annotate Visium image
        • Pre-analysis tools
          • Generate group cell counts
          • Pool cells
          • Split matrix
          • Hashtag demultiplexing
          • Merge matrices
          • Descriptive statistics
          • Spot clean
        • Aligners
        • Quantification
          • Quantify to annotation model (Partek E/M)
          • Quantify to transcriptome (Cufflinks)
          • Quantify to reference (Partek E/M)
          • Quantify regions
          • HTSeq
          • Count feature barcodes
          • Salmon
        • Filtering
          • Filter features
          • Filter groups (samples or cells)
          • Filter barcodes
          • Split by attribute
          • Downsample Cells
        • Normalization and scaling
          • Impute low expression
          • Impute missing values
          • Normalization
          • Normalize to baseline
          • Normalize to housekeeping genes
          • Scran deconvolution
          • SCTransform
          • TF-IDF normalization
        • Batch removal
          • General linear model
          • Harmony
          • Seurat3 integration
        • Differential Analysis
          • GSA
          • ANOVA/LIMMA-trend/LIMMA-voom
          • Kruskal-Wallis
          • Detect alt-splicing (ANOVA)
          • DESeq2(R) vs DESeq2
          • Hurdle model
          • Compute biomarkers
          • Transcript Expression Analysis - Cuffdiff
          • Troubleshooting
        • Survival Analysis with Cox regression and Kaplan-Meier analysis - Partek Flow
        • Exploratory Analysis
          • Graph-based Clustering
          • K-means Clustering
          • Compare Clusters
          • PCA
          • t-SNE
          • UMAP
          • Hierarchical Clustering
          • AUCell
          • Find multimodal neighbors
          • SVD
          • CellPhoneDB
        • Trajectory Analysis
          • Trajectory Analysis (Monocle 2)
          • Trajectory Analysis (Monocle 3)
        • Variant Callers
          • SAMtools
          • FreeBayes
          • LoFreq
        • Variant Analysis
          • Fusion Gene Detection
          • Annotate Variants
          • Annotate Variants (SnpEff)
          • Annotate Variants (VEP)
          • Filter Variants
          • Summarize Cohort Mutations
          • Combine Variants
        • Copy Number Analysis (CNVkit)
        • Peak Callers (MACS2)
        • Peak analysis
          • Annotate Peaks
          • Filter peaks
          • Promoter sum matrix
        • Motif Detection
        • Metagenomics
          • Kraken
          • Alpha & beta diversity
          • Choose taxonomic level
        • 10x Genomics
          • Cell Ranger - Gene Expression
          • Cell Ranger - ATAC
          • Space Ranger
          • STARsolo
        • V(D)J Analysis
        • Biological Interpretation
          • Gene Set Enrichment
          • GSEA
        • Correlation
          • Correlation analysis
          • Sample Correlation
          • Similarity matrix
        • Export
        • Classification
        • Feature linkage analysis
      • Data Viewer
      • Visualizations
        • Chromosome View
          • Launching the Chromosome View
          • Navigating Through the View
          • Selecting Data Tracks for Visualization
          • Visualizing the Results Using Data Tracks
          • Annotating the Results
          • Customizing the View
        • Dot Plot
        • Volcano Plot
        • List Generator (Venn Diagram)
        • Sankey Plot
        • Transcription Start Site (TSS) Plot
        • Sources of variation plot
        • Interaction Plots
        • Correlation Plot
        • Pie Chart
        • Histograms
        • Heatmaps
        • PCA, UMAP and tSNE scatter plots
        • Stacked Violin Plot
      • Pipelines
        • Making a Pipeline
        • Running a Pipeline
        • Downloading and Sharing a Pipeline
        • Previewing a Pipeline
        • Deleting a Pipeline
        • Importing a Pipeline
      • Large File Viewer
      • Settings
        • Personal
          • My Profile
          • My Preferences
          • Forgot Password
        • System
          • System Information
          • System Preferences
          • LDAP Configuration
        • Components
          • Filter Management
          • Library File Management
            • Library File Management Settings
            • Library File Management Page
            • Selecting an Assembly
            • Library Files
            • Update Library Index
            • Creating an Assembly on the Library File Management Page
            • Adding Library Files on the Library File Management Page
            • Adding a Reference Sequence
            • Adding a Cytoband
            • Adding Reference Aligner Indexes
            • Adding a Gene Set
            • Adding a Variant Annotation Database
            • Adding a SnpEff Variant Database
            • Adding a Variant Effect Predictor (VEP) Database
            • Adding an Annotation Model
            • Adding Aligner Indexes Based on an Annotation Model
            • Adding Library Files from Within a Project
            • Microarray Library Files
            • Adding Prep kit
            • Removing Library Files
          • Option Set Management
          • Task Management
          • Pipeline managment
          • Lists
        • Access
          • User Management
          • Group Management
          • Licensing
          • Directory Permissions
          • Access Control Log
          • Failed Logins
          • Orphaned files
        • Usage
          • System Queue
          • System Resources
          • Usage Report
      • Server Management
        • Backing Up the Database
        • System Administrator Guide (Linux)
        • Diagnosing Issues
        • Moving Data
        • Partek Flow Worker Allocator
      • Enterprise Features and Toolkits
        • REST API
          • REST API Command List
      • Microarray Toolkit
        • Importing Custom Microarrays
      • Glossary
    • Webinars
    • Blog Posts
      • How to select the best single cell quality control thresholds
      • Cellular Differentiation Using Trajectory Analysis & Single Cell RNA-Seq Data
      • Spatial transcriptomics—what’s the big deal and why you should do it
      • Detecting differential gene expression in single cell RNA-Seq analysis
      • Batch remover for single cell data
      • How to perform single cell RNA sequencing: exploratory analysis
      • Single Cell Multiomics Analysis: Strategies for Integration
      • Pathway Analysis: ANOVA vs. Enrichment Analysis
      • Studying Immunotherapy with Multiomics: Simultaneous Measurement of Gene and Protein
      • How to Integrate ChIP-Seq and RNA-Seq Data
      • Enjoy Responsibly!
      • To Boldly Go…
      • Get to Know Your Cell
      • Aliens Among Us: How I Analyzed Non-Model Organism Data in Partek Flow
    • White Papers
      • Understanding Reads in RNA-Seq Analysis
      • RNA-Seq Quantification
      • Gene-specific Analysis
      • Gene Set ANOVA
      • Partek Flow Security
      • Single Cell Scaling
      • UMI Deduplication in Partek Flow
      • Mapping error statistics
    • Release Notes
      • Release Notes Archive - Partek Flow 10
  • Partek Genomics Suite
    • Installation Guide
      • Minimum System Requirements
      • Computer Host ID Retrieval
      • Node Locked Installation
        • Windows Installation
        • Macintosh Installation
      • Floating/Locked Floating Installation
        • Linux Installation
          • FlexNet Installation on Linux
        • Installing FlexNet on Windows
        • License Server FAQ's
        • Client Computer Connection to License Server
      • Uninstalling Partek Genomics Suite
      • Updating to Version 7.0
      • License Types
      • Installation FAQs
    • User Manual
      • Lists
        • Importing a text file list
        • Adding annotations to a gene list
        • Tasks available for a gene list
        • Starting with a list of genomic regions
        • Starting with a list of SNPs
        • Importing a BED file
        • Additional options for lists
      • Annotation
      • Hierarchical Clustering Analysis
      • Gene Ontology ANOVA
        • Implementation Details
        • Configuring the GO ANOVA Dialog
        • Performing GO ANOVA
        • GO ANOVA Output
        • GO ANOVA Visualisations
        • Recommended Filters
      • Visualizations
        • Dot Plot
        • Profile Plot
        • XY Plot / Bar Chart
        • Volcano Plot
        • Scatter Plot and MA Plot
        • Sort Rows by Prototype
        • Manhattan Plot
        • Violin Plot
      • Visualizing NGS Data
      • Chromosome View
      • Methylation Workflows
      • Trio/Duo Analysis
      • Association Analysis
      • LOH detection with an allele ratio spreadsheet
      • Import data from Agilent feature extraction software
      • Illumina GenomeStudio Plugin
        • Import gene expression data
        • Import Genotype Data
        • Export CNV data to Illumina GenomeStudio using Partek report plug-in
        • Import data from Illumina GenomeStudio using Partek plug-in
        • Export methylation data to Illumina GenomeStudio using Partek report plug-in
    • Tutorials
      • Gene Expression Analysis
        • Importing Affymetrix CEL files
        • Adding sample information
        • Exploring gene expression data
        • Identifying differentially expressed genes using ANOVA
        • Creating gene lists from ANOVA results
        • Performing hierarchical clustering
        • Adding gene annotations
      • Gene Expression Analysis with Batch Effects
        • Importing the data set
        • Adding an annotation link
        • Exploring the data set with PCA
        • Detect differentially expressed genes with ANOVA
        • Removing batch effects
        • Creating a gene list using the Venn Diagram
        • Hierarchical clustering using a gene list
        • GO enrichment using a gene list
      • Differential Methylation Analysis
        • Import and normalize methylation data
        • Annotate samples
        • Perform data quality analysis and quality control
        • Detect differentially methylated loci
        • Create a marker list
        • Filter loci with the interactive filter
        • Obtain methylation signatures
        • Visualize methylation at each locus
        • Perform gene set and pathway analysis
        • Detect differentially methylated CpG islands
        • Optional: Add UCSC CpG island annotations
        • Optional: Use MethylationEPIC for CNV analysis
        • Optional: Import a Partek Project from Genome Studio
      • Partek Pathway
        • Performing pathway enrichment
        • Analyzing pathway enrichment in Partek Genomics Suite
        • Analyzing pathway enrichment in Partek Pathway
      • Gene Ontology Enrichment
        • Open a zipped project
        • Perform GO enrichment analysis
      • RNA-Seq Analysis
        • Importing aligned reads
        • Adding sample attributes
        • RNA-Seq mRNA quantification
        • Detecting differential expression in RNA-Seq data
        • Creating a gene list with advanced options
        • Visualizing mapped reads with Chromosome View
        • Visualizing differential isoform expression
        • Gene Ontology (GO) Enrichment
        • Analyzing the unexplained regions spreadsheet
      • ChIP-Seq Analysis
        • Importing ChIP-Seq data
        • Quality control for ChIP-Seq samples
        • Detecting peaks and enriched regions in ChIP-Seq data
        • Creating a list of enriched regions
        • Identifying novel and known motifs
        • Finding nearest genomic features
        • Visualizing reads and enriched regions
      • Survival Analysis
        • Kaplan-Meier Survival Analysis
        • Cox Regression Analysis
      • Model Selection Tool
      • Copy Number Analysis
        • Importing Copy Number Data
        • Exploring the data with PCA
        • Creating Copy Number from Allele Intensities
        • Detecting regions with copy number variation
        • Creating a list of regions
        • Finding genes with copy number variation
        • Optional: Additional options for annotating regions
        • Optional: GC wave correction for Affymetrix CEL files
        • Optional: Integrating copy number with LOH and AsCN
      • Loss of Heterozygosity
      • Allele Specific Copy Number
      • Gene Expression - Aging Study
      • miRNA Expression and Integration with Gene Expression
        • Analyze differentially expressed miRNAs
        • Integrate miRNA and Gene Expression data
      • Promoter Tiling Array
      • Human Exon Array
        • Importing Human Exon Array
        • Gene-level Analysis of Exon Array
        • Alt-Splicing Analysis of Exon Array
      • NCBI GEO Importer
    • Webinars
    • White Papers
      • Allele Intensity Import
      • Allele-Specific Copy Number
      • Calculating Genotype Likelihoods
      • ChIP-Seq Peak Detection
      • Detect Regions of Significance
      • Genomic Segmentation
      • Loss of Heterozygosity Analysis
      • Motif Discovery Methods
      • Partek Genomics Suite Security
      • Reads in RNA-Seq
      • RNA-Seq Methods
      • Unpaired Copy Number Estimation
    • Release Notes
    • Version Updates
    • TeamViewer Instructions
  • Getting Help
    • TeamViewer Instructions
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Partek Flow
  2. User Manual
  3. Task Menu
  4. QA/QC

Single-cell QA/QC

PreviousFeature distributionNextCell barcode QA/QC

Last updated 7 months ago

Was this helpful?

The Single-cell QA/QC task in Partek Flow enables you to visualize several useful metrics that will help you include only high-quality cells. To invoke the Single-cell QA/QC task:

  • Click a Single cell counts data node

  • Click the QA/QC section of the task menu

  • Click Single cell QA/QC

By default, all samples are used to perform QA/QC. You can choose to split the sample and perform QA/QC separately for each sample.

If your Single cell counts data node has been annotated with a gene/transcript annotation, the task will run without a task configuration dialog. However, if you imported a single cell counts matrix without specifying a gene/transcript annotation file, you will be prompted to choose the genome assembly and annotation file by the Single cell QA/QC configuration dialog (Figure 1). Note, it is still possible to run the task without specifying an annotation file. If you choose not to specify an annotation file, the detection of mitochondrial counts will not be possible.

The Single cell QA/QC task report opens in a new data viewer session. Four dot and violin plots showing the value of every cell in the project are displayed on the canvas: counts per cell, detected features per cell, the percentage of mitochondrial counts per cell, and the percentage of ribosomal counts per cell (Figure 2).

If your cells do not express any mitochondrial genes or an appropriate annotation file was not specified, the plot for the percentage of mitochondrial counts per cell will be non-informative (Figure 3).

Mitochondrial genes are defined as genes located on a mitochondrial chromosome in the gene annotation file. The mitochondrial chromosome is identified in the gene annotation file by having "M" or "MT" in its chromosome name. If the gene annotation file does not follow this naming convention for the mitochondrial chromosome, Partek Flow will not be able to identify any mitochondrial genes. If your single cell RNA-Seq data was processed in another program and the count matrix was imported into Partek Flow, be sure that the annotation field that matches your feature IDs was chosen during import; Partek Flow will be unable to identify any mitochondrial genes if the gene symbols in the imported single cell data and the chosen gene/feature annotation do not match.

Total counts are calculated as the sum of the counts for all features in each cell from the input data node. The number of detected features is calculated as the number of features in each cell with greater than zero counts. The percentage of mitochondrial counts is calculated as the sum of counts for known mitochondrial genes divided by the sum of counts for all features and multiplied by 100. The percentage of ribosomal counts are calculated as the sum of counts for known ribosomal genes divided by the sum of counts for all features and multiplied by 100.

Each point on the plots is a cell. All cells from all samples are shown on the plots. The overlaid violins illustrate the distribution of cell values for the y-axis metric.

The appearance of a plot can be configured by selecting a plot and adjusting the Configure settings in the panel on the left (Figure 4). Here are some suggestions, but feel free to explore the other options available:

  • Open Axes and change the Y-axis scale to Logarithmic. This can be helpful to view the range of values better, although it is usually better to keep the Ribosomal counts plot in linear scale.

  • Open Style and reduce the Color Opacity using the slider. For data sets with very many cells, it may be helpful to decrease the dot opacity to better visualize the plot density.

  • Within Style switch on Summary Box & Whiskers. Inspecting the median, Q1, Q3, upper 90%, and lower 10% quantiles of the distributions can be helpful in deciding appropriate thresholds.

High-quality cells can be selected using Select & Filter, which is pre-loaded with the selection criteria, one for each quality metric (Figure 5).

Hovering the mouse over one of the selection criteria reveals a histogram showing you the frequency distribution of the respective quality metric. The minimum and maximum thresholds can be adjusted by clicking and dragging the sliders or by typing directly into the text boxes for each selection criteria (Figure 6).

Alternatively, Pin histogram to view all of the distributions at one time to determine thresholds with ease (Figure 7).

Adjusting the selection criteria will select and deselect cells in all three plots simultaneously. Depending on your settings, the deselected points will either be dimmed or gray. The filters are additive. Combining multiple filters will include the intersection of the three filters. The number of cells selected is shown in the figure legend of each plot (Figure 8).

Select the input data node for the filtering task and click Select (Figure 10).

A new data node, Filtered counts, will be generated under the Analyses tab (Figure 11).

Double click the Filtered counts data node to view the task report. The report includes a summary of the count distribution across all features for each sample; a detailed breakdown of the number of cells included in the filter for each sample; and the minimum and maximum values for each quality metric (expressed genes, total counts, etc) across the included cells for each sample (Figure 12).

Additional Assistance

Ribosomal genes are defined as genes that code for proteins in the large and small ribosomal subunits. Ribosomal genes are identified by searching their gene symbol against a list of 89 L & S ribosomal genes taken from . The search is case-insensitive and includes all known gene name aliases from HGNC. Identifying ribosomal genes is performed independent of the gene annotation file specified.

To filter the high-quality cells, click the include selected cells icon in Filter in the top right of Select & Filter, and click Apply observation filter... (Figure 9).

If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.

HGNC
our support page
Figure 1. If an annotation was not specified on import, you can choose the assembly and annotation after invoking Single-cell QA/QC
Figure 2. Dot & violin plots showing values of each cell for several quality measures
Figure 3. A non-informative percentage of mitochondrial counts plot
Figure 4. Use the Configuration options on the left customize the appearance of plots
Figure 5. Select & Filter criteria
Figure 6. Use the sliders and histograms to adjust the selection criteria
Figure 7. Pin the histogram to view all of the distributions at one time
Figure 8. The filters are additive and deselected cells are dimmed
Figure 9. Include the selected points and apply the filter
Figure 10. After the Apply filter button is selected, you will be presented with a preview of your pipeline. You need to select the appropriate data node to apply the filtering to
Figure 11. Filter cells task runs from the Single cell QA/QC report
Figure 12. Filtered counts task report