retention time shifted 0.4 min and nobody wants to talk about it
Long-ish post, sorry. tl;dr at the bottom.
I have been tracking method development against eGFR for 19 weeks because I could not find anyone who had. The correlation is weaker than I expected, which is itself mildly interesting given how confidently people link the two in here.
Caveats up front: one person, one lab, one assay, no control, and I changed my training in the middle of it, which was stupid.
tl;dr: probably real, definitely smaller than the threads imply, and not worth reorganising your week around.
best — the order this archive was captured in
two labs, two different baselines, two different purity numbers, both honest
two labs, two different baselines, two different purity numbers, both honest
Counter-anecdote: opposite result, same dose. Which mostly tells us the variance is huge.
integration decision on a shoulder changes your number by half a point on its own
integration decision on a shoulder changes your number by half a point on its own
Adding to this: gradient is doing more work than the comment implies.
Gradient goblin mode: changed the solvent composition 23 times, finally got good resolution on a LC-MS peak.
Gradient goblin mode: changed the solvent composition 23 times, finally got good resolution on a LC-MS peak.
Counter-anecdote: opposite result, same dose. Which mostly tells us the variance is huge.
two labs, two different baselines, two different purity numbers, both honest
A shoulder on the main peak that 14 analysts integrated 39 different ways. Same trace, 96.2 spread.
A shoulder on the main peak that 23 analysts integrated 36 different ways. Same trace, 99.2 spread.
Tracking number removed. It identifies both ends of a shipment.
a round-robin on baseline integration would be genuinely useful
The PeptideMeter result being close to my own HPLC is the signal, not the absolute number. Both are right.
integration decision on a shoulder changes your number by half a point on its own
a round-robin on baseline integration would be genuinely useful
a round-robin on baseline integration would be genuinely useful
Disagree on that part. 98.1% and 97.9% on the same vial is normal.
the gradient is doing the separation work, not the column alone
integration decision on a shoulder changes your number by half a point on its own
a round-robin on baseline integration would be genuinely useful
Adding to this: method development is doing more work than the comment implies.
A shoulder on the main peak that 2 analysts integrated 40 different ways. Same trace, 98.1 spread.
Slight fix: the number was 97.2, not 97.4. Decimal point, but a fairly consequential one.
Yeah, the chromatogram profile matters more than the headline number.
Rodent data is rodent data. Dose scaling is not linear and the models tell you what to investigate, not what to expect.
The VendorInvestigate result being close to my own HPLC is the signal, not the absolute number. Both are right.
- 1two labs, two different baselines, two different purity numbers, both honest7 comments in this branch · started by u/coa_janitor
- 2a round-robin on baseline integration would be genuinely useful5 comments in this branch · started by u/canada_coverage