An important step in analyzing single cell RNA-Seq data is to filter out low quality cells. A few examples of low-quality cells are doublets, cells damaged during cell isolation, or cells with too few reads to be analyzed.
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.
The Single cell QA/QC report includes interactive violin plots showing the value of every cell in the project on several quality measures.
There are three plots: number of UMI counts per cell, number of detected genes per cell, and the percentage of mitochondrial counts per cell.
Each point on the plots is a cell and the violins illustrate the distribution of values for the y-axis metric. Cells can be filtered either by clicking and dragging to select a region on one of the plots or by setting thresholds using the filters below the plots. Here, we will apply a filter for the number of read counts.
The plot will be shaded to reflect the filter. Cells that are excluded will be shown as black dots on both plots.
The UMI counts per cell and number of detected genes per cell are typically used to filter out potential doublets - if a cell as an unusually high number of total UMIs or detected genes, it may be a doublet. The mitochondrial counts percentage can be used to identify cells damaged during cell isolation - if a cell has a high percentage of mitochondrial counts, it is likely damaged or dying and may need to be excluded.
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 and ideal gene filtering criteria depend on your experimental design and research question, Partek Flow has a wide variety of flexible filtering options.
There are three categories of filter available - noise reduction, statistics based, and feature list.
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.
We will use a noise reduction filter to exclude genes that are not expressed by any cell in the data set, but were included in the matrix file.
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.
Because different cells will have a different number of total UMIs, it is important to normalize the data prior to downstream analysis. For droplet-based single cell isolation and library preparation methods that use a 3' counting strategy, where only the 3' end of each transcript is captured and sequenced, we recommend the following normalization - 1. CPM (counts per million), 2. Add 1, 3. Log2. This accounts for differences in total UMI counts per cell and log transforms the data, which makes the data easier to visualize.
to add the recommended normalization scheme This adds CPM (counts per million), Add 1, and Log2 to the Normalization order panel. Normalization steps are performed in descending order.
A new Normalized counts data node will be produced.
For more information on normalizing data in Partek Flow, please see the Normalize Counts section of the user manual.
For some data sets, it may be necessary to remove technical artifacts or batch effects. To do this, you can use the Scaling task in the Normalization and Scaling section. The scaling task is detailed in our Single Cell Scaling white paper. We will not perform scaling for this data set.
Principal components (PC) analysis (PCA) is an exploratory technique that is used to describe the structure of high dimensional data by reducing its dimensionality. Because PCA is used to reduce the dimensionality of the data prior to clustering as part of a standard single cell analysis workflow, it is useful to examine the results of PCA for your data set prior to clustering.
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