Global Mean

• Partially-methylated sites: Let \(x_{i,t}\) and \(y_{j,t}\) be the frequency of methylation state \(p\) in island \(i \in\{1, \cdots, I\}\) and non-island \(j \in\{1, \cdots, J\}\) at tip \(t\), respectively. We define the mean global frequency of \(x\) and \(y\) as:

\[ \overline{x}=\frac1I\sum_{i=1}^{I}\frac1T\sum_{t=1}^{T}x_{i,t} \\ \overline{y}=\frac1J\sum_{j=1}^{J}\frac1T\sum_{t=1}^{T}y_{j,t} \]

where \(x_{i,t}\) and \(y_{j,t}\) represent the respective frequencies at each tip.

• Methylated sites: Similarly, let \(w_{i,t}\) and \(z_{j,t}\) be the frequency of methylation state \(m\) in islands and non-islands. The mean global frequencies are:

\[ \overline{w}=\frac1I\sum_{i=1}^{I}\frac1T\sum_{t=1}^{T}w_{i,t} \\ \overline{z}=\frac1J\sum_{j=1}^{J}\frac1T\sum_{t=1}^{T}z_{j,t} \]

These values can be computed using the following functions:

# 1 tip / sample / replicate
sample_n <- 1
index_islands <- c(1, 3)
index_nonislands <- c(2, 4)
data <- list(c(.5, .5, 0, 0, 0, .5), c(.5, 0, 0, .5), c(.5, .5, 0), c(0, 1, .5)) # tip 1
get_islandMeanFreqP(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.5833333
get_nonislandMeanFreqP(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.4166667
get_islandMeanFreqM(index_islands, data, categorized_data = T, sample_n)
#> [1] 0
get_nonislandMeanFreqM(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.1666667

# 2 tip / sample / replicate
sample_n <- 2
index_islands <- c(1, 3)
index_nonislands <- c(2, 4)
data <- list(
  list(c(.5, .5, 0, 0, 0, .5), c(.5, 0, 0, .5), c(.5, .5, 0), c(0, 0, .5)), # tip 1
  list(c(0, .5, .5, 1, 1, .5), c(1, .5, 1, .5), c(0, .5, .5), c(1, .5, 1)) # tip 2
  )
get_islandMeanFreqP(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.5833333
get_nonislandMeanFreqP(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.4166667
get_islandMeanFreqM(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.08333333
get_nonislandMeanFreqM(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.2916667

Note that in the calculation of the means the relative frequencies \(x_{i, t}\), \(y_{j, t}\), \(w_{i, t}\) and \(z_{j, t}\) are not weighted by the lengths of island \(i\) or non-island \(j\).

Mean SD of Observed Frequencies per Structure Type at Tree Tips

• Partially-methylated sites: We define the standard deviation of \(x\) and \(y\) at tip \(t\) as:

\[ \sigma_{t}(x)=\sqrt{\frac{1}{I-1}\sum_{i=1}^{I}(x_{i,t}-\overline{x_{.,t}})^2} \\ \sigma_{t}(y)=\sqrt{\frac{1}{J-1}\sum_{j=1}^{J}(y_{j,t}-\overline{y_{.,t}})^2} \]

where \(\overline{x_{.,t}}\) and \(\overline{y_{.,t}}\) represent mean frequencies at tip \(t\). The mean standard deviation across tips is:

\[ \hat{\sigma}(x)=\frac{1}{T}\sum_{t=1}^{T}\sigma_t(x) \\ \hat{\sigma}(y)=\frac{1}{T}\sum_{t=1}^{T}\sigma_t(y) \]

• Methylated sites: Similarly, for \(w\) and \(z\):

\[ \hat{\sigma}(w)=\frac{1}{T}\sum_{t=1}^{T}\sigma_t(w) \\ \hat{\sigma}(z)=\frac{1}{T}\sum_{t=1}^{T}\sigma_t(z) \]

These values can be computed using:

# 1 tip / sample / replicate
sample_n <- 1
index_islands <- c(1, 3)
index_nonislands <- c(2, 4)
data <- list(c(.5, .5, 0, 1, 1, .5), c(.5, 0, 1, .5), c(.5, .5, 0), c(0, 0, .5))
get_islandSDFreqP(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.1178511
get_nonislandSDFreqP(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.1178511
get_islandSDFreqM(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.2357023
get_nonislandSDFreqM(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.1767767

# 2 tip / sample / replicate
sample_n <- 2
index_islands <- c(1, 3)
index_nonislands <- c(2, 4)
data <- list(
  list(c(.5, .5, 0, 0, 0, 1), c(.5, 0, 0, .5), c(1, .5, 0), c(0, 0, .5)), # tip 1
  list(c(0, .5, .5, 1, 1, .5), c(1, .5, 1, .5), c(0, .5, .5), c(1, .5, 1)) # tip 2
  )
get_islandSDFreqP(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.05892557
get_nonislandSDFreqP(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.1178511
get_islandSDFreqM(index_islands, data, categorized_data = T, sample_n)
#> [1] 0.1767767
get_nonislandSDFreqM(index_nonislands, data, categorized_data = T, sample_n)
#> [1] 0.05892557

Note that we average across tips as there can be correlations between the mean standard deviation of tips (e.g. under the case of an event affecting the states at two tips).

Mean Frequency of Methylation State Changes at Single Sites per Structure Type

Let \(S_k\) be the set of sites in the genomic structure indexed by \(k\). For each site \(s \in S_k\), let the methylation state at a given tip \(t\) be denoted as \(m_{k,s,t}\), where \(m_{k,s,t} \in \{0, 0.5, 1\}\) represents the unmethylated, partially-methylated, or methylated state, respectively.

For a given cherry \((t_1, t_2)\), that is two tips \(t_1\) and \(t_2\) that are direct offspring of the same internal node, we compute the proportion of sites with differing methylation states between the two tips.

Define the indicator variable: \[ \delta_{k,s,t_1,t_2} = \begin{cases} 1, & \text{if } m_{k,s,t_1} \neq m_{k,s,t_2}, \\ 0, & \text{if } m_{k,s,t_1} = m_{k,s,t_2}. \end{cases} \]

Then, the proportion of sites with different methylation states in structure \(k\) for the cherry \((t_1, t_2)\) is given by:

\[ F_k(t_1, t_2) = \frac{1}{|S_k|} \sum_{s \in S_k} \delta_{k,s,t_1,t_2} \]

where \(|S_k|\) is the total number of sites in structure \(k\), and the sum counts the number of sites where the methylation state differs between tips \(t_1\) and \(t_2\).

This statistic quantifies the proportion of differing methylation states for a given genomic structure in a cherry.

To compute the weighted mean frequency of methylation changes across all structures in a cherry, we separate island and non-island structures.

For a given cherry \((t_1, t_2)\), let \(\mathcal{I}\) be the set of indices for island structures and \(\mathcal{N}\) be the set of indices for non-island structures. Each structure \(k\) has a total number of sites \(|S_k|\), which we use as weights.

The weighted mean frequency of methylation changes for island structures is:

\[ \bar{F}_{\text{island}}(t_1, t_2) = \frac{\sum_{k \in \mathcal{I}} |S_k| F_k(t_1, t_2)}{\sum_{k \in \mathcal{I}} |S_k|} \]

Similarly, the weighted mean frequency for non-island structures is:

\[ \bar{F}_{\text{non-island}}(t_1, t_2) = \frac{\sum_{k \in \mathcal{N}} |S_k| F_k(t_1, t_2)}{\sum_{k \in \mathcal{N}} |S_k|} \]

These expressions compute the mean frequency of methylation state differences across all island and non-island structures, weighted by the number of sites in each structure.

Functions:

• get_cherryDist(tree) to get the distances between the tips of each cherry. It identifies each cherry by the tip names and the tip indices. The tip indices correspond to (a) the index from left to right on the newick string, (b) the order of the tip label in the phylo_object$tip.label, and (c) the index in the methylation data list data[[tip]][[structure]] as obtained with the function simulate_evolData() when the given tree has several tips.

• countSites_cherryMethDiff(cherryDist, data) to get for each cherry the counts of sites with half-methylation and full-methylation changes per genomic structure (island or non-island).

• freqSites_cherryMethDiff(tree,data). The function first validates the tree structure and extracts pairwise distances between cherry tips with get_cherryDist. It then validates the input data and counts half and full methylation differences for each cherry at each structure using countSites_cherryMethDiff. Finally, it normalizes these (half and full) counts by the number of sites per structure to compute frequencies.

• get_siteFChange_cherry(tree,data) uses freqSites_cherryMethDiff(tree,data) and then computes the frequency of sites with any change of methylation state (full or half) for each cherry at each genomic structure.

• MeanSiteFChange_cherry(tree, data, index_islands, index_nonislands). The function computes the per-cherry frequency of sites with different methylation states at each structure (islands and non-islands) using get_siteFChange_cherry. Then it calculates the weighted mean site frequency of methylation changes for each cherry separately for islands and non-islands.

# Set example tree and methylation data
tree <- "((a:1.5,b:1.5):2,(c:2,d:2):1.5);"
data <- list(
    list(rep(1,10), rep(0,5), rep(1,8)), # tip a
    list(rep(1,10), rep(0.5,5), rep(0,8)), # tip b
    list(rep(1,10), rep(0.5,5), rep(0,8)), # tip c
    list(c(rep(0,5), rep(0.5, 5)), c(0, 0, 1, 1, 1), c(0.5, 1, rep(0, 6)))) # d 

# Set the index for islands and non-island structures
index_islands <- c(2)
index_nonislands <- c(1, 3)

MeanSiteFChange_cherry(data = data, 
                       categorized_data = T, 
                       tree = tree, 
                       index_islands = index_islands, 
                       index_nonislands = index_nonislands)
#>   tip_names tip_indices dist nonisland_meanFChange island_meanFChange
#> 1       a-b         1-2    3             0.4444444                  1
#> 2       c-d         3-4    4             0.6666667                  1

Fitch Parsimony Estimate for Minimum Global Methylation State Changes at CpG Islands

Let \(I\) be the set of CpG islands in a given genomic region For each island \(i \in I\), the mean methylation level across sites is computed with get_meanMeth_islands(index_islands, data) and categorized into one of three global methylation states—unmethylated (\(u\)), partially methylated (\(p\)), or methylated (\(m\))—based on user-defined thresholds \(u_{\text{thresh}}\) and \(m_{\text{thresh}}\). This categorization is given by the function:

\[ \operatorname{categorize\_islandGlbSt}(\mu_i, u_{\text{thresh}}, m_{\text{thresh}}) = \begin{cases} u, & \text{if } \mu_i \leq u_{\text{thresh}}, \\ m, & \text{if } \mu_i \geq m_{\text{thresh}}, \\ p, & \text{otherwise}. \end{cases} \]

Given a phylogenetic tree \(T\) with tips corresponding to sampled species or individuals, we aim to estimate the minimum number of state changes required to explain the observed distribution of island methylation states across the tips. This is achieved using the Fitch algorithm (Fitch 1971) as implemented in compute_fitch(meth, tree).

The function computeFitch_islandGlbSt(T, M), where \(M\) is the matrix of categorized methylation states across tips, returns a vector \(C\) where each entry \(C_i\) represents the minimum number of changes required for island \(i\) under the Fitch parsimony criterion.

# Example with data from a single island structure 
# and three tips

tree <- "((bla:1,bah:1):2,booh:2);"

data <- list(
  #Tip 1
  list(c(rep(1,9), rep(0,1))), # m
  #Tip 2
  list(c(rep(0.5,10))), # p
  #Tip 3
  list(c(rep(0.5,9), rep(0.5,1)))) # p
  
index_islands <- c(1)
  
computeFitch_islandGlbSt(index_islands, data, tree, 
                         u_threshold = 0.1, m_threshold = 0.9)
#> [1] 1

# Example: data from a genomic region consisting on 3 structures with 10 sites each
# one island, one non-island, one island
# and a tree with 8 tips
tree <- "(((a:1,b:1):1,(c:1,d:1):1):1,((e:1,f:1):1,(g:1,h:1):1):1);"
data <- list(
  #Tip 1
  list(c(rep(1,5), rep(0,5)), # p
       c(rep(0,9), 1), 
        c(rep(1,8), rep(0.5,2))), # m
  #Tip 2
  list(c(rep(0.5,9), rep(0.5,1)), # p
        c(rep(0.5,9), 1), 
       c(rep(0,8), rep(0.5,2))), # u
  #Tip 3
  list(c(rep(1,9), rep(0.5,1)), # m
       c(rep(0.5,9), 1), 
       c(rep(0.5,8), rep(0.5,2))), # p
  #Tip 4
  list(c(rep(1,9), rep(0.5,1)), # m
       c(rep(1,9), 0), 
       c(rep(0.5,8), rep(0.5,2))), # p
  #Tip 5
  list(c(rep(0,5), rep(0,5)), # u
       c(rep(0,9), 1), 
       c(rep(0.5,8), rep(0.5,2))), # p
  #Tip 6
  list(c(rep(0,9), rep(0.5,1)), # u
       c(rep(0.5,9), 1), 
       c(rep(1,8), rep(0.5,2))), # m
  #Tip 7
  list(c(rep(0,9), rep(0.5,1)), # u
       c(rep(0.5,9), 1), 
       c(rep(0,8), rep(0.5,2))), # u
  #Tip 8
  list(c(rep(0,9), rep(0.5,1)), # u
       c(rep(1,9), 0), 
       c(rep(0,9), rep(0.5,1)))) # u
  
index_islands <- c(1,3)
  
computeFitch_islandGlbSt(index_islands, data, tree, 
                         u_threshold = 0.1, m_threshold = 0.9)
#> [1] 2 4

For genomic regions containing multiple CpG islands, we can summarize the per-island estimates using the mean to obtain an overall measure of the minimum state changes.

mean(computeFitch_islandGlbSt(index_islands, data, tree, 
                              u_threshold = 0.1, m_threshold = 0.9))
#> [1] 3

Mean Number of Significant Methylation Frequency Changes per Island and Cherry

A cherry \(c\) is defined as a pair of direct offspring (tips \(t_1\) and \(t_2\)) of the same internal node in the phylogenetic tree. For a given cherry \(c\), let \(\mathcal{I}\) be the set of indices for island structures. For each island structure \(i \in \mathcal{I}\) at each cherry tip \(t_c \in \{t_{c,1}, t_{c,2}\}\) in each phylogenetic tree, the number of sites in each methylation state (unmethylated \(0\), partially-methylated \(0.5\), methylated \(1\)) is counted. This count is represented as:

\[ \text{count}_{\text{UPM}}(c,t_c, i) = \left( n_0, n_{0.5}, n_1 \right) \]

where \(n_0\), \(n_{0.5}\), and \(n_1\) are the counts of unmethylated, partially-methylated, and methylated sites at tip \(t_c\), respectively.

The distribution of methylation states at each island between the two tips \(t_1\) and \(t_2\) of a given cherry is compared using a chi-squared test. The null hypothesis of the test is that the frequency distributions of states in the two islands follow the same multinomial distribution. Note that not only IWEs can lead to deviations from this null hypothesis but also neighbor-dependent SSEs can lead to slight deviations from the assumption of multinomial distributions. The test statistic is calculated using the contingency table \(T_{c,i}\) for tips \(t_{c,1}\) and \(t_{c,2}\):

\[ T_{c,i} = \begin{pmatrix} n_0^{t_{c,1}} & n_{0.5}^{t_{c,1}} & n_1^{t_{c,1}} \\ n_0^{t_{c,2}} & n_{0.5}^{t_{c,2}} & n_1^{t_{c,2}} \end{pmatrix} \] For each cherry \(c\) and each island \(i\), the methylation frequencies at each cherry tip are compared using the chi-squared test. The p-value for the chi-squared test is calculated via Monte Carlo simulation to improve reliability, even when the expected frequencies do not meet the assumptions of the chi-squared approximation (i.e., expected counts of at least 5 in each category). The obtained p-values are stored in a dataframe for each cherry and for each island.

The significance of the methylation frequency changes is determined based on a user-defined threshold \(p_{\text{threshold}}\). If the p-value is smaller than the threshold, the change is considered significant. This is done for each cherry and each island \(i \in \mathcal{I}\) by comparing each p-value to the threshold:

\[ \text{Significant Change}_{c,i} = \begin{cases} 1, & \text{if } p_{\text{value}}(c,i) \lt p_{\text{threshold}} \\ 0, & \text{if } p_{\text{value}}(c,i) \ge p_{\text{threshold}} \end{cases} \]

For each cherry , the mean number of significant changes across all islands \(i \in \mathcal{I}\) is computed as the average of the significant changes for each island:

\[ \text{Mean Number of Significant Changes per Island}_{c} = \frac{1}{|\mathcal{I}|} \sum_{i \in \mathcal{I}} \text{Significant Change}_{c,i} \]

where \(|\mathcal{I}|\) is the total number of islands.

Functions:

• compare_CherryFreqs(tip1, tip2) to perform a chi-squared test to compare the distribution of methylation states between two cherry tips.

• pValue_CherryFreqsChange_i(data, index_islands, tree) uses compare_CherryFreqs(tip1, tip2) to get for each cherry and each island the chi-square p-value of the comparison of the distribution of methylation states between two cherry tips.

• mean_CherryFreqsChange_i(data, categorized_data = FALSE, index_islands, tree, pValue_threshold). The function uses pValue_CherryFreqsChange_i(data, index_islands, tree) and counts the mean number of significant changes per island at each cherry.

# Set example tree and methylation data
  tree <- "((a:1,b:1):2,c:2);"
  data <- list(
    #Tip a
    list(c(rep(1,9), rep(0,1)), # Structure 1: island
         c(rep(0,9), 1), # Structure 2: non-island
         c(rep(0,9), rep(0.5,1))),  # Structure 3: island
    #Tip b
    list(c(rep(0,9), rep(0.5,1)),  # Structure 1: island
         c(rep(0.5,9), 1), # Structure 2: non-island
         c(rep(0,9), rep(0,1))), # Structure 3: island
    #Tip c
    list(c(rep(1,9), rep(0.5,1)),  # Structure 1: island
         c(rep(0.5,9), 1), # Structure 2: non-island
         c(rep(0,9), rep(0.5,1)))) # Structure 3: island
  
  
  index_islands <- c(1,3)
  
  mean_CherryFreqsChange_i(data, categorized_data = T,
                           index_islands, tree,
                           pValue_threshold = 0.05)
#>   first_tip_name second_tip_name first_tip_index second_tip_index dist island_1
#> 1              a               b               1                2    2     TRUE
#>   island_3 FreqsChange
#> 1    FALSE         0.5

Mean Number of Significant Methylation Frequency Changes per Island Across All Tree Tips

For a given phylogenetic tree with a number of tips \(N\), let \(t_n \in \{t_1, t_2, \cdots, t_N\) represent the tree tips. For a given genomic region, let \(\mathcal{I}\) be the set of indices for island structures. For each island structure \(i \in \mathcal{I}\) at each tip \(t_n\) in the phylogenetic tree, the number of sites in each methylation state (unmethylated \(0\), partially-methylated \(0.5\), methylated \(1\)) is counted. This count is represented as:

\[ \text{count}_{\text{UPM}}(t_n, i) = \left( n_0, n_{0.5}, n_1 \right) \]

where \(n_0\), \(n_{0.5}\), and \(n_1\) are the counts of unmethylated, partially-methylated, and methylated sites at tip \(t_n\), respectively.

The distribution of methylation states at each island across all tips is compared using a chi-squared test. The null hypothesis of the test is that the frequency distributions of states in the two islands follow the same multinomial distribution. Note that not only IWEs can lead to deviations from this null hypothesis but also neighbor-dependent SSEs can lead to slight deviations from the assumption of multinomial distributions. The test statistic is calculated using the contingency table \(T_{i}\) for all tree tips:

\[ T_{i} = \begin{pmatrix} n_0^{t_{1}} & n_{0.5}^{t_{1}} & n_1^{t_{1}} \\ n_0^{t_{2}} & n_{0.5}^{t_{2}} & n_1^{t_{2}} \\ \cdots & \cdots & \cdots \\ n_0^{t_{N}} & n_{0.5}^{t_{N}} & n_1^{t_{N}} \end{pmatrix} \]

For each island \(i\), the methylation frequencies are compared across tips using the chi-squared test. The p-value for the chi-squared test is calculated via Monte Carlo simulation to improve reliability, even when the expected frequencies do not meet the assumptions of the chi-squared approximation (i.e., expected counts of at least 5 in each category).

The significance of the methylation frequency changes for each island \(i \in \mathcal{I}\) across tips is determined based on a user-defined threshold \(p_{\text{threshold}}\). If the p-value is smaller than the threshold, the change is considered significant.

\[ \text{Significant Change}_{i} = \begin{cases} 1, & \text{if } p_{\text{value}}(i) < p_{\text{threshold}} \\ 0, & \text{if } p_{\text{value}}(i) \ge p_{\text{threshold}} \end{cases} \]

For the given tree, the mean number of significant changes per island across all tips is computed as:

\[ \text{Mean Number of Significant Changes per Island} = \frac{1}{|\mathcal{I}|} \sum_{i \in \mathcal{I}} \text{Significant Change}_{i} \]

where \(|\mathcal{I}|\) is the total number of islands.

Function:

• mean_TreeFreqsChange_i(tree, data, categorized_data = FALSE, index_islands, pValue_threshold).

# Set example tree and methylation data
tree <- "((a:1,b:1):2,(c:2,d:2):1);"
  data <- list(
    #Tip a
    list(c(rep(1,9), rep(0,1)), # Structure 1: island
         c(rep(0,9), 1), # Structure 2: non-island
         c(rep(0,9), rep(0,1))), # Structure 3: island
    #Tip b
    list(c(rep(0,9), rep(0.5,1)), # Structure 1: island
         c(rep(0.5,9), 1), # Structure 2: non-island
         c(rep(0,9), rep(0,1))),# Structure 3: island
    #Tip c
    list(c(rep(0,9), rep(0.5,1)), # Structure 1: island
         c(rep(0.5,9), 1), # Structure 2: non-island
         c(rep(1,9), rep(0,1))),# Structure 3: island
    #Tip d
    list(c(rep(0,9), rep(0.5,1)), # Structure 1: island
         c(rep(0.5,9), 1), # Structure 2: non-island
         c(rep(1,8), rep(0.5,2)))) # Structure 3: island
  
  
  index_islands <- c(1,3)
  
  mean_TreeFreqsChange_i(tree, data, categorized_data = T,
                           index_islands,
                           pValue_threshold = 0.05)
#> [1] 1