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.
Steps
- 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.
- You can change the values by using the sliders or edit the number to input a specific number of your choice.
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.
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.
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.
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%.
