Single-cell analysis has become a key driver of scientific discovery. The move from studying populations of cells to studying single-cells has powered a more detailed and accurate understanding of cellular processes – and fueled numerous advances in immunology, oncology, and neuroscience.
However, there are 3 pitfalls to single-cell analysis that can lead to incorrect biological conclusions, stalled research, and wasted resources – a true research nightmare.
1. Samples with low viability can lead to wasted sequencing reads and fewer cells sequenced
2. Samples with low cell numbers may result in unusable data
3. Samples with excess dead cells and contaminants can produce data that is not statistically [or bioinformatically] sound, adversely affecting your findings