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.
You can choose Features contribute equally to standardize the genes prior to PCA or allow more variable genes to have a larger effect on the PCA by choosing by variance. By default, we take variance into account and focus on the most variable genes.
If you have multiple samples, you can choose to run PCA for each sample individually or for all samples together using the Split cells by sample option.
This will generate a Scree plot and PC component loadings table, which are useful for determining how many PCs to use for downstream analysis tasks.
A new PCA task node will be produced.
The PCA task report includes the PCA plot, the Scree plot, the component loadings table, and the PC projections table. To switch between these elements, use the buttons in the upper right-hand corner of the task report 
. Each cell is shown as a dot on the PCA scatter plot.
to open the Scree plotThe Scree plot lists PCs on the x-axis and the amount of variance explained by each PC on the y-axis, measured in Eigenvalue. The higher the Eigenvalue, the more variance is explained by the PC. Typically, after an initial set of highly informative PCs, the amount of variance explained by analyzing additional PCs is minimal. By identifying the point where the Scree plot levels off, you can choose an optimal number of PCs to use in downstream analysis steps like graph-based clustering and t-SNE.
In this data set, a reasonable cut-off could be set anywhere between 8 and 25 PCs. We will use 10 in downstream steps.
Viewing the genes highly correlated with each PC can be useful when choosing how many PCs to include.
to open the Component loadings tableThis table lists genes on rows and PCs on columns.
In this case, we see a few known marker genes are highly correlated with PC1.
Graph-based clustering identifies groups of similar cells using PC scores as the input. By including only the most informative PCs, noise in the data set is excluded, improving the results of clustering.
Clustering can be performed on each sample individually or on all samples together. Here, we are working with a single sample.
The Number of principal components should be set based on the your examination of the Scree plot and component loadings table. The default value of 100 is likely exhaustive for most data sets, but may introduce noise that reduces the resolution of clustering (fewer clusters identified).
A new Graph-based clustering task node and a Clustering result data node will be generated.
The Graph-based clustering task report lists the number of clusters and what proportion of cells fall into each cluster. It also includes a cluster biomarkers table. This lists the top-10 genes that distinguish each cluster from the others.
We will use t-SNE to visualize the results of Graph-based clustering.
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a dimensional reduction technique that prioritizes local relationships to build a low-dimensional representation of the high-dimensional data that places objects that are similar in high-dimensional space close together in the low-dimensional representation. This makes t-SNE well suited for analyzing high-dimensional data when the goal is to identify groups of similar objects, such as types of cells in single cell RNA-Seq data.
If you have multiple samples, you can choose to run t-SNE for each sample individually or for all samples together using the Split cells by sample option. Please note that this option will not be present if you are running t-SNE on a clustering result. For clarity, clustering results run with all samples together must be viewed together and clustering results run individually must be viewed individually.
A new t-SNE task node will be produced.
The t-SNE scatter plot is interactive and can be viewed for 2D or 3D (calculated separately).
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