Hello everybody!
I'm trying to analyse some pyrosequencing data that some colleges sent me. I could get all experiment replicas for each experimental condition. The problem is that for the different genes I can have a different number of replicas. Even more, in some cases some results are missing. I think i should refill those values with 0's or N/A. what should i do in those cases that i do not have value? Working with averages do not provides good statistics
example of data
HEK293-Nach A2 64.09 - 62.06 64.2 -
HEK293-Nach B2 69.97 - 69.3 71.36 -
HEK293-Nach B2 62.22 - 62.26 61.89 -
HEK293-Nach B3 71.59 - 71.64 68.18 -
HEK293-Nach B3 57.61 - 59.55 60.71 -
HEK293-Nach B4 58.39 - 60.47 58.35 -
HEK293-Nach B4 66.21 - 65.46 66.13 -
HEK293-Nach B5 54.93 - 57.03 57.47 -
HEK293-Nach B5 61.58 - 66.07 65.93 -
HEK293-Nach B6 50 - 56.69 55.01 -
HEK293-Nach B6 58.57 - 65.68 63.94 -
HEK293 K 45.1 46.4 42.15 1.9 41.37
HEK293 K 45.92 44.88 42.2 1.74 41.16
HEK293 K 62.49 67.06 59.34 2.35 58.34
HEK293 K 59.21 67.21 58.54 3.75 56.7
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Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
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