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An important step in analyzing single cell RNA-Seq data is to filer out low quality cells. Low A few examples of low-quality cells can be are doublets, cells damaged during cell isolation, and or cells with too few reads to be analyzed.
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A task node, Single cell QA/QC, is produced. Initially, the node will be semi-transparaent 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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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 criterea criteria depend on your experimental design and research question, Partek Flow has a wide variety of flexible filtering options.
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There are three categories of filter available - Noise noise reduction filters, Statistics statistics based filters, and Feature feature list filters (Figure 16).
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We will use a Noise noise reduction filter to exclude genes that are not expresed expressed by any cell in the data set, but were included in the matrix file.
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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.
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We are omitting normalization in ths this tutorial because the data has already been normalized.
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