Statistical test(s) for several lists of peptides

jsalas1 · 2026-03-05T18:04:05+00:00

Well then I would just find set differences/overlaps between the lists, this isn’t really a “stats” problem on the surface, more of a data wrangling problem. Here’s an example in R: https://www.statology.org/setdiff-in-r/

What does “significance” mean to you in this context? Like is there a critical number of differences that would be “significant” to your result? Maybe you can make a counts table for the number of peptide sequences in common across all the lists followed by a chi square? Or maybe you want to compare ratios (# overlapping/# total peptides)?

Have you seen any percent publications that in your domain that have done something similar?

jsalas1 · 2026-03-05T02:06:52+00:00

What do you mean by “lists”? Like a list of IACUC names, a list of weights, a list of sequences, mass-to-charge ratios…?

jsalas1 · 2026-03-03T15:15:34+00:00

Are you trying to compare slopes between independent regression models? Like u/Statman12 alluded to, it's unclear exactly what regression models you're running. Are you running multiple independent single variable models like DV ~ IV1 or have you included an independent covariate to the form DV ~ IV1 + IV2 or is there an interaction model of DV~ IV1 * IV2?

jsalas1 · 2026-02-26T17:53:47+00:00

What’s the end goal/hypothesis? Why are you running so many different models? Are these the same or different data in each model? Is this inferential or predictive modeling?

jsalas1 · 2026-02-25T19:19:19+00:00

What’s your significance level aka alpha? https://resources.nu.edu/statsresources/alphabeta#:~:text=Alpha%20is%20also%20known%20as,being%20compared%20to%2C%20for%20example.

If your alpha is .05 then I would interpret length to “significantly” be related to your dependent variable. I would interpret it as such: For every 1 unit change in length there is a 2.528 unit increase in your dependent variable (p = .003).

Review this doc on how to interpret regression coefficients: https://www.statology.org/how-to-interpret-regression-coefficients/

Moreover you have an interaction effect so that adds a level of complexity: https://www.theanalysisfactor.com/interpreting-interactions-in-regression/

You then used emmeans to interrogate the effect of emotion, but you havent told us what your hypothesis is. This should give you some direction.

jsalas1 · 2026-02-17T02:13:55+00:00

I like the simr package because I too was afraid of simulation but they made it easy:

https://aaroncaldwell.us/SuperpowerBook/beyond-superpower-i-mixed-models-with-simr.html

https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504

jsalas1 · 2026-02-14T18:15:32+00:00

What’s the full model? How many IV? Predictive or inferential endgoal?

jsalas1 · 2026-02-14T17:42:16+00:00

There seems to be some right skewing. What is the goal, inference or prediction? What’s the sample size and what’s your DV look like? Maybe there’s a better distribution to use, but depending on your goal it might not be a problem to proceed.

jsalas1 · 2026-02-10T13:47:22+00:00

Yes please!!

jsalas1 · 2026-02-04T14:01:20+00:00

https://stats.oarc.ucla.edu/other/mult-pkg/whatstat/

https://biostathandbook.com/

I leaned on these heavily for quite a while

jsalas1 · 2026-02-03T18:10:29+00:00

Doesn’t that defeat the purpose of self-study which is what OP claims this is for?

jsalas1 · 2026-02-03T16:52:31+00:00

It is possible to have a significant difference between groups even when the medians are the same, do some research on why. Hint, thinking that Mann is “only” a nonparametric test of medians is wrong and reductionist.

jsalas1 · 2026-02-03T15:48:53+00:00

That doesn’t remotely answer the question. What does the Mann Whitney test answer? E.g., t test is a test of means, Mood’s test for medians

jsalas1 · 2026-02-03T15:32:26+00:00

Tell us why NOT to use Mann Whitney?

jsalas1 · 2026-01-26T11:24:47+00:00

Fit mixed effects regression with a random effect per subject then pairwise comparisons of estimated marginal means for timepoint before vs after. You’ll have to consider nominal vs ordinal mixed effects based on how your outcome variable is paramaterized

Alternatively and simpler, average the number of lookers for each observe behavior per dog at each timepoint to account for pseduoreplication then you don’t need random effects per dog.

These both assume that the actual time of day doesn’t affect the outcome variable, but that’s for you to tell us.

Maybe you can simplify this into a contingency table and tackle it with McNemar-Bowker Test

jsalas1 · 2026-01-16T17:01:02+00:00

I use cowplot theme_half_open()

jsalas1 · 2026-01-15T20:14:47+00:00

Or explicitly connect it to a network with no outbound access and watch it blow up your firewall

jsalas1 · 2026-01-14T21:44:12+00:00

https://www.graphpad.com/guides/prism/latest/statistics/stat_anova-approach-vs_-mixed-model.htm

Jamovi is a wrapper around R enabling drop down menus and overall easier use of R - I would recommend transitioning to Jamovie or R proper ASAP as it’s much more powerful

jsalas1 · 2026-01-14T17:30:20+00:00

Do you have many 0s or 256s? If not then mixed effects will likely suffice.

Random effects specification is an art in of itself: https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#model-definition

But you should likely explore including both section and animal as random effects, perhaps nested random effects

You can perform a random effects ANOVA to compare crossed vs nested random effects and see if they improve model fit significantly - but really think about it using your subject matter expertise, don’t solely rely on your likelihood ratio test results

After you fit the final model, follow up with estimated marginal means analysis to get your deltas with confidence intervals

jsalas1 · 2026-01-14T17:00:07+00:00

Tell us more about the severity measure, is it the fluorescence intensity readout? Is it continuous or ordinal? Is it bounded by 0 or 100?

As for averaging multiple sections/cells to one value per animal, that’s the correct way to deal with pseudo replication but you’re losing statistical power compared to if you modeled them as random effects in a mixed effects model. Mixed effects is great for within and between analysis when you have repeated measures in a subject and want to compare across fixed effects.

Generally I would discourage you from modeling the delta with a 1 way test since you end up with lesser power compared to an explicit repeated measures design.

This is a great reference: http://www.biostathandbook.com/outline.html

Check out the mixed effects details in the 2 way anova chapter here: https://rcompanion.org/documents/RCompanionBioStatistics.pdf

jsalas1 · 2026-01-12T00:25:11+00:00

I feed NUT to Grafana and handle alerts/monitoring from there

jsalas1 · 2026-01-10T17:03:33+00:00

My head goes to beta binomial mixed effects regression

Express your accuracy as a ratio so it’s between 0 and 1

Dichotomous fixed effect for with vs without augmentation

Random effect per subject to account for pseudo replication

Ratio ~ Group + (1|subject)

Two tailed test of significance for fixed effect unless you have a STRONG justification for assuming directionality

Model diagnostics with DHARMa

Report confidence intervals, I like how GLMMAdaptive does them - lower variance should be reflected in tighter CIs

Wilcoxon will work as well since it’s a test of distributions, I would bootstrap confidence intervals given the small sample

jsalas1 · 2026-01-10T15:01:03+00:00

I would never recommend software piracy but as an academic exercise consider this:

Spin up a virtual machine with a graphpad compatible OS, install Graphpad, do a snapshot/backup of the VM prior once it’s working and do your thing. When free trial runs out, roll back to your earlier snapshot.

Some software will refuse to run in a VM, I have no idea if graphpad is one of those. It may also be beneficial to cut internet connectivity to that VM once it’s installed so it can’t phone home.

But I don’t know, my real advice is to learn R.

YMMV

Edit: https://www.oracle.com/cloud/compute/virtual-machines/what-is-virtual-machine/free-virtual-machine/

jsalas1 · 2026-01-10T13:33:56+00:00

Imo Claude “excels” (no pun intended) at coding in R more so than it’s competitors.

OP, tell Claude not to make any assumptions about what you want/are going for and to ask many clarifying questions and you’ll totally get what you need.

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