Removing low-quality cells is one of the essential quality control steps in the single-cell data analysis workflow. Hence, as the first step in setting up the analysis, we will set filtering thresholds across various parameters to filter out low-quality cells.

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Steps

Step 1. The violin plots here show the value distribution of cells across different parameters. The shaded regions indicate that the cells in those value ranges have been filtered out.

Step 2. You can change the values by using the sliders or edit the number to input a specific number of your choice.

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Transcripts per cell

Transcripts/UMI/Counts per cell indicate the total number of reads/UMI counts detected in a cell across all the genes. This can be regarded as "sequencing depth" for each cell.

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Genes per cell

The number of “detected” genes per cell. A gene is detected if at least one read/UMI is attributed to that gene in a given cell. This metric lets you access the general quality of the underlying profiling technology. Usually, plate-based technologies like SMART-Seq provide higher gene coverage over droplet-based technologies.

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Mitochondrial counts

Percentage of all the UMIs/read counts that belong to mitochondrial genes. Usually, high mitochondrial content is a signature of poor library preparation or starting material.

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Ribosomal counts

Percentage of all the UMIs/read counts that belong to ribosomal genes. Generally, high ribosomal content doesn't necessarily reflect poor quality prep like mitochondrial contamination. Depending on their metabolic activity, this statistic can range from 5%-70%.

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