Random forests

PSTAT197A Fall 2026

Dr. Coburn

UCSB

Announcements/reminders

Make your final commits for the first group assignment by Friday 10/16 11:59pm PST

• should include an updated report.qmd and report.html with your write-up

Next group assignment to be distributed this weekend.

• Task: replicate and redesign proteomic analysis

Last time

• introduced ASD proteomic dataset

• used multiple testing corrections to identify proteins whose serum levels differ significantly between ASD/TD groups

• discussed the difference between controlling familywise error vs. false discovery

Today

We’ll talk about two more approaches to identifying proteins of interest:

• correlation-based identification of proteins

• random forest classifier

Correlation with ADOS

ADOS score

Autism Diagnostic Observation Schedule (ADOS) scores are determined by psychological assessment and measure ASD severity.

asd %>% 
  select(group, ados) %>% 
  group_by(group) %>% 
  sample_n(size = 2)

# A tibble: 4 × 2
# Groups:   group [2]
  group  ados
  <chr> <dbl>
1 ASD      15
2 ASD      17
3 TD       NA
4 TD       NA

• ados is only measured for the ASD group

• numerical score between 6 and 23 (at least in this sample)

Correlation approach

So here’s an idea:

• compute correlations of each protein with ADOS;

• pick the 10 proteins with the strongest correlation

Computation

This is a simple aggregation operation and can be executed in R with summarize() :

# compute correlations
asd_clean %>%
  pivot_longer(cols = -ados,
               names_to = 'protein',
               values_to = 'level') %>%
  group_by(protein) %>%
  summarize(correlation = cor(ados, level))

# A tibble: 1,317 × 2
   protein                   correlation
   <chr>                           <dbl>
 1 14-3-3                         0.0970
 2 14-3-3 protein beta/alpha      0.0481
 3 14-3-3 protein theta           0.122 
 4 14-3-3 protein zeta/delta      0.0735
 5 14-3-3E                       -0.127 
 6 17-beta-HSD 1                  0.0969
 7 3HAO                           0.0773
 8 3HIDH                          0.0452
 9 4-1BB                          0.0290
10 4-1BB ligand                   0.0799
# ℹ 1,307 more rows

Visual assessment

Sort and slice

• List
• Visual

# A tibble: 10 × 4
   protein          correlation abs.corr  rank
   <chr>                  <dbl>    <dbl> <int>
 1 CO8A1                  0.362    0.362  1317
 2 C5b, 6 Complex        -0.337    0.337     1
 3 Thrombospondin-1       0.310    0.310  1316
 4 ILT-2                 -0.309    0.309     2
 5 TRAIL R4               0.296    0.296  1315
 6 HCE000414             -0.296    0.296     3
 7 C1r                   -0.290    0.290     4
 8 GM-CSF                 0.290    0.290  1314
 9 Angiogenin            -0.284    0.284     5
10 HCG                    0.278    0.278  1313

SLR coefficients instead?

Fact: the simple linear regression coefficient estimate is proportional to the correlation coefficient.

So it should give similar results to sort the SLR coefficients by significance.

# A tibble: 1,317 × 3
   protein          estimate p.value
   <chr>               <dbl>   <dbl>
 1 CO8A1               0.391 0.00131
 2 C5b, 6 Complex     -0.401 0.00290
 3 Thrombospondin-1    0.317 0.00635
 4 ILT-2              -0.546 0.00664
 5 TRAIL R4            0.353 0.00933
 6 HCE000414          -0.278 0.00950
 7 C1r                -0.341 0.0110 
 8 GM-CSF              0.345 0.0110 
 9 Angiogenin         -0.314 0.0130 
10 HCG                 0.306 0.0149 
# ℹ 1,307 more rows

FDR control

If we do the correlation analysis this way, do the identified proteins pass multiple testing significance thresholds?

# A tibble: 10 × 5
   protein          estimate p.value  p.BH  p.BY
   <chr>               <dbl>   <dbl> <dbl> <dbl>
 1 CO8A1               0.391 0.00131 0.990     1
 2 C5b, 6 Complex     -0.401 0.00290 0.990     1
 3 Thrombospondin-1    0.317 0.00635 0.990     1
 4 ILT-2              -0.546 0.00664 0.990     1
 5 TRAIL R4            0.353 0.00933 0.990     1
 6 HCE000414          -0.278 0.00950 0.990     1
 7 C1r                -0.341 0.0110  0.990     1
 8 GM-CSF              0.345 0.0110  0.990     1
 9 Angiogenin         -0.314 0.0130  0.990     1
10 HCG                 0.306 0.0149  0.990     1

• probably just introducing selection noise

• this result diverges considerably from the paper

Aside: correlation test

The SLR approach is equivalent to sorting the correlations. (Take a moment to check the results.)

We could use inference on the population correlation to obtain a \(p\)-value associated with each sample correlation coefficient. These match the ones from SLR.

cor_test <- function(x, y){
  cor_out <- cor.test(x, y)
  tibble(estimate = cor_out$estimate,
         p.value = cor_out$p.value)
}

asd_clean %>%
  pivot_longer(cols = -ados,
               names_to = 'protein',
               values_to = 'level') %>%
  group_by(protein) %>%
  summarize(correlation = cor_test(ados, level)) %>%
  unnest(correlation) %>%
  arrange(p.value)

# A tibble: 1,317 × 3
   protein          estimate p.value
   <chr>               <dbl>   <dbl>
 1 CO8A1               0.362 0.00131
 2 C5b, 6 Complex     -0.337 0.00290
 3 Thrombospondin-1    0.310 0.00635
 4 ILT-2              -0.309 0.00664
 5 TRAIL R4            0.296 0.00933
 6 HCE000414          -0.296 0.00950
 7 C1r                -0.290 0.0110 
 8 GM-CSF              0.290 0.0110 
 9 Angiogenin         -0.284 0.0130 
10 HCG                 0.278 0.0149 
# ℹ 1,307 more rows

Random forest activity

Background

A binary tree is a directed graph in which:

• there is at most one path between any two nodes

• each node has at most two outward-directed edges

Classification tree

A classification tree is a binary tree in which the paths represent classification rules.

A goofy classification tree.

Example: classifying high earners

Say we want to predict income based on captial gains and education level using census data.

# A tibble: 6 × 3
# Groups:   income [2]
  income educ     capital_gain
  <fct>  <fct>           <dbl>
1 <=50K  hs                  0
2 <=50K  advanced            0
3 <=50K  college             0
4 >50K   hs               7688
5 >50K   hs               7688
6 >50K   college          5178

We could construct a classification tree by ‘splitting’ based on the values of predictor variables.

Activity: building trees

To get a sense of the process of tree construction, we’ll do an activity in groups: each group will build a tree ‘by hand’.

• first let’s look at the instructions together

• then take about 15-20 minutes to do it

Random forests

A random forest is a classifier based on many trees. It is constructed by:

• building some large number of \(T\) trees using bootstrap samples and random subsets of predictors (what you just did, repeated many times)

• taking a majority vote across all trees to determine the classification

So let’s take a vote using your trees!

Variable importance scores

If the number of trees \(T\) is large (as it should be):

• trees are built using lots of random subsets of predictors

• can keep track of which ones are used most often to define splits

Variable importance scores provide a measure of how influential each predictor is in a random forest.

Results

Back to the proteomics data, the variable importance scores from a random forest provide another means of identifying proteins.

# grow RF
set.seed(101222)
rf_out <- randomForest(x = asd_preds, y = asd_resp,
                       mtry = 100, ntree = 1000, 
                       importance = T)

# variable importance
rf_out$importance %>% 
  as_tibble() %>%
  mutate(protein = rownames(rf_out$importance)) %>%
  slice_max(MeanDecreaseGini, n = 10) %>%
  select(protein)

# A tibble: 10 × 1
   protein    
   <chr>      
 1 DERM       
 2 IgD        
 3 MAPK14     
 4 Notch 1    
 5 RELT       
 6 ALCAM      
 7 ERBB1      
 8 eIF-4H     
 9 CK-MB      
10 TGF-b R III

Errors

But how accurate is the predictor?

	ASD	TD	class.error
ASD	53	23	0.3026316
TD	19	59	0.2435897

Next week

• logistic regression

• variable selection

• a design view of the proteomic analysis