# Is it important to filter out poor quality cells before performing an integration analysis on single cell RNA sequencing data?

In order to perform an integration analysis of single cell RNA seq data, is it important to check the percentage expression of mitochondrial genes of cells as well as the feature counts to exclude dying or dead cells as well as multiplets from further analyses?

I ask because the standard Seurat work flow to perform integration as outlined in Integration and Label Transfer does not mention filtering steps prior to integration.

In this publication, Eleanor Alenzi et al. 2018 also did a canonical correlation analysis with Seurat but did not mention steps to filter out poor quality cells before doing the integrated analysis. This second publication does same. Both publications do not mention if their visualisations or differential gene expression analyses are done on integrated (discouraged) or raw uncorrected RNA data (recommended).

Following the styles of the publications linked above (many more do same), is it also efficient to perform integration without filtering out poor quality cells?

It is absolutely necessary to remove low quality cells: In the case of CCA (and this applies to other "integration" or "data alignment" methods as well), one would need to use "anchors", basically same type/state/kind of cells from the samples to be "integrated" and are used to "align" the different samples. In a scenario where you have a set of cells whose distinguished feature is high mitochondrial content due to apoptosis irrespective of their origin (epithelial, T cell, ...) and such cells are picked up as so called anchors, the whole integration effort will be off. Even when not using integration methods, bad quality cells quite often form their own clusters (highly skewed biology) not contributing anything to analyses and are often removed at the downstream steps anyway.