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Obtain and add files to the project
The project includes Human Breast Cancer (Block A Section 1) and Human Breast Cancer (Block A Section 2) output files in one project.
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- Navigate the options to select 10x Genomics Visium Space Ranger output as the file format for input.
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- Click Transfer files on the homepage, under settings, or during import (Figure 3).
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- Navigate to the appropriate files for each sample (Figure 4). Please note that the 10x Genomics Space Ranger output can be count matrix data as 1 filtered .h5 file per sample or sparse matrix files for each sample as 3 files (two .csv with one .mtx or two .tsv with one .mtx for each sample). The spatial output files should be in compressed format (.zip). The high resolution image can be uploaded and is optional.
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Once the download completes, the sample table will appear in the Metadata tab, with one row per sample (Figure 5).
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The sample table is pre-populated with two sample attributes: # Cells and Subtype. Sample attributes can be added and edited manually by clicking Manage in the Sample attributes menu on the left. If a new attribute is added, click Assign values to assign samples to different groups. Alternatively, you can use the Assign values from a file option to assign sample attributes using a tab-delimited text file. For more information about sample attributes, see here.
For this tutorial, we do not need to edit or change any sample attributes.
Visualize the annotated image
With samples imported and annotated, we can begin analysis.
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A task node, Single cell QA/QC, is produced. Initially, the node will be semi-transparent to indicate that it has been queued, but not completed. A progress bar will appear on the Single cell QA/QC task node to indicate that the task is running.
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The Single cell QA/QC report opens in a new data viewer session. There are interactive violin plots showing the most commonly used quality metrics for each cell from all samples combined (Figure 8). For this data set, there are two relevant plots: the total count per cell and the number of detected genes per cell. Each point on the plots is a cell and the violins illustrate the distribution of values for the y-axis metric. Typically, there is a third plot showing the percentage of mitochondrial counts per cell, but mitochondrial transcripts were not included in the data set by the study authors, so this plot is not informative for this data set.
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The plots are highly customizable and can be used to explore the quality of cells in different samples.
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The cells are now separated into different samples along the x-axis (Figure 10)
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Note how both plots were modified at the same time.
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Because this data set was already filtered by the study authors to include only high-quality cells, this count filter is sufficient.
- Click under Filter to include the selected cells
- Click Apply observation filter
- Click the Single cell counts data node in the pipeline preview (Figure 12)
- Click Select
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A new task, Filter counts, is added to the Analyses tab. This task produces a new Filter counts data node (Figure 13).
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Most tasks can be queued up on data nodes that have not yet been generated, so you can wait for filtering step to complete, or proceed to the next section.
Filtering genes in single cell RNA-Seq data
A common task in bulk and single-cell RNA-Seq analysis is to filter the data to include only informative genes. Because there is no gold standard for what makes a gene informative or not, ideal gene filtering criteria depends on your experimental design and research question. Thus, Partek Flow has a wide variety of flexible filtering options.
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There are four categories of filter available - noise reduction, statistics based, feature metadata, and feature list (Figure 15).
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The noise reduction filter allows you to exclude genes considered background noise based on a variety of criteria. The statistics based filter is useful for focusing on a certain number or percentile of genes based on a variety of metrics, such as variance. The feature list filter allows you to filter your data set to include or exclude particular genes.
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This produces a Filtered counts data node. This will be the starting point for the next stage of analysis - identifying cell types in the data using the interactive t-SNE plot.
Normalizing single cell RNA-Seq data
We are omitting normalization in this tutorial because the data has already been normalized.
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