Case Studies in Irreproducibility

Last updated on 2025-06-24 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • What are some common reasons for irreproducibility?

Objectives

  • Describe a conceptual framework for reproducibility.
  • Explain how and why adopting reproducible practices benefits research.

Problems in irreproducible research

From here we will look at some common problems in irreproducible research. We will also discuss ways to overcome these problems. Recall that our definition of reproducible research means that authors provide all data and code to run the analysis again, re-creating the results.

Methods Same Data Different Data
Same methods Reproducibility Replicability
Different methods Robustness Generalizability

We will also limit these examples to those that we can examine as analysts. The system of rewards and incentives is one that we must work within for the time being, with an eye toward improving the culture of science. It is not something that we can change in a day however. As analysts we also can’t fix some of the human or technical issues that impact an experiment. We can investigate study design and statistics, however, in data analysis. We will focus on issues with study design, misused methods, and batch effects.

Factors Examples
Technical Bad reagents or cell lines, natural variability
Study design & statistics Design flaws, misused methods, batch effects
Human Poor record keeping or sharing, confirmation bias
Rewards & incentives Fraud, paywalls, perverse incentives

Case 1: The gene set that characterizes early Alzheimer’s disease

K.Q. Watkins and coauthors describe a unique gene set characteristic of early onset Alzheimer’s Disease. The gene expression heatmap from their paper clearly delineates Alzheimer’s patients from a neurotypical control group.

heatmap of expression values
heatmap of expression values

Watkins, K. Q., et al. (2022). A unique gene expression signature characterizes early Alzheimer’s disease. Nature Alzheimer’s, 33(3), 737-753.

Use the R script, the data, and the metadata to reproduce this plot.

Can you find other ways to present the (meta)data in the heatmap? What do alternate ways of presenting the data show you?

This is a simulated study and publication. Any resemblance to real persons or real studies is purely coincidental.

  1. You can replace Diagnosis with Batch in the call the pheatmap.

R 

pheatmap(expr_matrix, 
         annotation_col = metadata["Batch"], 
         fontsize_row   = 5)

This will show the same heatmap, though in this one the legend shows batch number instead of disease state. This is an example of complete confounding between batch and diagnosis. All of the Alzheimer’s samples were run in the first batch and all of the controls in the second. There is no way to disentangle disease state from batch.

The graphic below shows an example of complete confounding at right under Can't Be Corrected. Batch 1 contains only controls and batch 2 only disease samples. There is no method that will be able to discern the effect of the batch from the effect of disease or control.

Batch effects are common and can be corrected if samples are randomized to batch. At left beneath Can Be Corrected, control and disease samples were randomized to each of three batches. There can still be batch effects, however, there are methods available (e.g. ComBat) that can correct for these effects.

Batch effect and complete confounding
Batch effect and complete confounding

Case 2: Hippocampal volume increase in mild cognitive impairment (MCI)

K.Z. Smith and coauthors describe higher mean hippocampal volume in subjects with mild cognitive impairment (MCI). The boxplots below show a clear difference in hippocampal volume between the MCI and control groups.

boxplots of hippocampal volume
boxplots of hippocampal volume

Smith, K. Z., et al. (2023). Hippocampal volume increase in mild cognitive impairment. Science Progress, 3(14), 37-53.

A t-test gave a p-value of less than 0.05 to reject the null hypothesis of no difference in means between the two groups. The boxplots appear to back up this assertion.

  1. Use the R script and the data to reproduce the boxplot and t-test.

  2. Create a scatterplot of the data by group to get further insight. You can also look at the entire dataset to get a sense of it.

  3. Calculate the effect size between the two groups.

R 

# Estimate effect size (Cohen's d for hippocampal volume)
library(effsize)
d_result <- cohen.d(HippocampalVolume ~ Group, data = hippocampus)
print(d_result)
  1. Use the effect size to calculate statistical power.

R 

# Estimate power for hippocampal volume
# Using observed effect size 
library(pwr)
power_result <- pwr.t.test(
  d = d_result$estimate,
  n = n_per_group,
  sig.level = 0.05,
  type = "two.sample",
  alternative = "two.sided"
)
print(power_result)
  1. What sample size (n_per_group) would have resulted in 80% statistical power for this experiment?

This is a simulated study and publication. Any resemblance to real persons or real studies is purely coincidental.

R 

t_test_result <- t.test(HippocampalVolume ~ Group, data = hippocampus)
print(t_test_result)

boxplot(HippocampalVolume ~ Group, data = hippocampus, 
        main = "Hippocampal Volume", 
        ylab = "Volume (cm³)")

R 

hippocampus %>% ggplot(aes(Group, HippocampalVolume)) + geom_point()

R 

# Estimate effect size (Cohen's d for hippocampal volume)
library(effsize)
d_result <- cohen.d(HippocampalVolume ~ Group, data = hippocampus)
print(d_result)

OUTPUT 

Cohen's d

d estimate: -1.558649 (large)
95 percent confidence interval:
     lower      upper
-3.2238802  0.1065818

R 

# Estimate power for hippocampal volume
# Using observed effect size 
library(pwr)
power_result <- pwr.t.test(
  d = d_result$estimate,
  n = n_per_group,
  sig.level = 0.05,
  type = "two.sample",
  alternative = "two.sided"
)
print(power_result)

OUTPUT 

     Two-sample t test power calculation

              n = 5
              d = 1.558649
      sig.level = 0.05
          power = 0.581064
    alternative = two.sided

NOTE: n is number in *each* group

R 

# Update sample size to 8
n_per_group <- 8

# Estimate power for hippocampal volume
# Using observed effect size 
power_result <- pwr.t.test(
  d = d_result$estimate,
  n = n_per_group,
  sig.level = 0.05,
  type = "two.sample",
  alternative = "two.sided"
)
print(power_result)

OUTPUT 

     Two-sample t test power calculation

              n = 8
              d = 1.558649
      sig.level = 0.05
          power = 0.8258431
    alternative = two.sided

NOTE: n is number in *each* group

Case 3: A novel biomarker for diagnosis of Alzheimer’s Disease

Your colleague J Mackerel discovered a novel biomarker for Alzheimer’s Disease and provided you with data and an analysis script to review the finding. Run the script to load the data and reproduce the result.

One of the biomarkers had a p-value of less than .05 in a t-test comparing means of the control and Alzheimer’s groups. Your colleague is very excited and ready to go to press with this!

What do you recommend? How would you proceed? Are you convinced of the finding?

P-values are no longer useful to interpret when working with high-dimensional data because we are testing many features at the same time. This is called the multiple comparison or multiple testing problem. When we test many hypotheses simultaneously, a list of p-values can result in many false positives. This example had relatively few features, however, enough t-tests were performed to reasonably expect that a p-value less than .05 would occur.

The solution to the multiple testing problem is correction using a method such as the Bonferroni correction.

Key Points

  • Reproducibility has many definitions.
  • We define reproducibility here as using the same data and methods as the original study.
  • Adopting reproducible practices strengthens science and makes it more rigorous.