slideimp

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slideimp

Comparing Methods Using a Shared Cross-Validation Split with `tune_imp()`

Simulate some data

# 20 rows, 1000 columns, all columns have at least some NA
sim_obj <- sim_mat(n = 20, p = 1000, perc_col_na = 1)
obj <- sim_obj$input
obj[1:4, 1:4]
#>          feature1  feature2  feature3  feature4
#> sample1 0.3486482 0.7385414 0.4077444 0.1607935
#> sample2 0.5338935 0.4724364 0.9663621 0.3722729
#> sample3 0.7185848 0.7351035 0.6724479 0.3162537
#> sample4 0.1734418        NA 0.0000000 0.0000000

Instead of random NA sampling inside tune_imp(), which internally calls sample_na_loc(), we can generate the NA locations up front and pass them to tune_imp().
Here, we randomly select 5 rows from each of 200 random columns (features) for 5 repetitions using sample_na_loc(). To specify just certain columns (i.e., clock CpGs), provide the na_col_subset argument.
- na_loc has 5 elements, one for each repeat. Each element is a matrix whose rows store the row and column index of a missing value.

na_loc <- sample_na_loc(obj, n_cols = 200, n_rows = 5, n_reps = 5)
length(na_loc)
#> [1] 5
na_loc[[1]][1:6, ]
#>      row col
#> [1,]  18 661
#> [2,]  17 661
#> [3,]  20 661
#> [4,]  15 661
#> [5,]   6 661
#> [6,]  10 293

Then we can compare 1) PCA, 2) K-NN imputation, and 3) a custom method, since the cross-validation missing values are the same for all methods.
Note: The custom function requires the first argument to be obj, must return an object with the same dimensions as obj, and all subsequent arguments must match the column names of the parameters data frame.

# This custom function imputes missing values with random normal values and takes
# `mean` and `sd` as params
rnorm_imp <- function(obj, mean, sd) {
  na <- is.na(obj)
  obj[na] <- rnorm(sum(na), mean = mean, sd = sd) # <- impute values with rnorm
  return(obj) # <- return an imputed object with the same dim as obj
}

pca_tune <- tune_imp(
  obj,
  .f = "pca_imp",
  na_loc = na_loc,
  parameters = data.frame(ncp = 10)
)
#> Tuning `pca_imp()`
#> Step 1/2: Resolving NA locations
#> Running mode: sequential
#> Step 2/2: Tuning

knn_tune <- tune_imp(
  obj,
  .f = "knn_imp",
  na_loc = na_loc,
  parameters = data.frame(k = 10)
)
#> Tuning `knn_imp()`
#> Step 1/2: Resolving NA locations
#> Running mode: sequential
#> Step 2/2: Tuning

rnorm_tune <- tune_imp(
  obj,
  .f = rnorm_imp,
  na_loc = na_loc,
  parameters = data.frame(mean = 0, sd = 1) # must match with arguments of `rnorm_imp`
)
#> Tuning custom function
#> Step 1/2: Resolving NA locations
#> Running mode: sequential
#> Step 2/2: Tuning

Pick the method with the lowest cross-validation error:

mean(compute_metrics(pca_tune, metrics = "rmse")$.estimate)
#> [1] 0.2045949
mean(compute_metrics(knn_tune, metrics = "rmse")$.estimate)
#> [1] 0.1962824
mean(compute_metrics(rnorm_tune, metrics = "rmse")$.estimate)
#> [1] 1.108204

Group-Wise Imputation with Small-Group Padding and Group-Wise Parameters with `group_imp()`

group_imp() allows imputation to be performed separately within defined groups (e.g., by chromosome), which significantly reduces run time and can increase accuracy for both K-NN and PCA imputation.
group_imp() requires the group argument, which maps colnames(obj) to groups. This can be created up front with prep_groups() for advanced features such as group-wise parameters and padding of small groups with random features from other groups. prep_groups() returns a list-column data frame with:
- features: required - a list-column where each element is a character vector of variable names to be imputed together.
- aux: optional - auxiliary variables to include in each group. These are only used to augment the imputation quality of features and are not imputed themselves. If one group is too small, e.g. chrM, aux is used to pad the group by randomly drawing features from other groups to meet min_group_size.
- parameters: optional - group-specific imputation parameters.
First we simulate data from 2 groups. We then create group3 with only 1 feature to show how min_group_size pads it using the aux list column.

sim_obj <- sim_mat(n = 20, p = 50, n_col_groups = 2)

# Matrix to be imputed
obj <- sim_obj$input
obj[1:5, 1:4]
#>           feature1  feature2  feature3  feature4
#> sample1 0.03031619 0.1352710 0.4653579 0.1143298
#> sample2 0.50962966 0.2313022 0.9737881 0.5198844
#> sample3 0.69068459 0.5020337 0.7863144 0.9997625
#> sample4 0.40710170 0.4094603 0.3228294 0.2529592
#> sample5 0.73113251 0.4108405 0.6385945        NA

# Metadata, i.e., which features belong to which group
meta <- sim_obj$col_group
meta[1:5, ]
#>    feature  group
#> 1 feature1 group1
#> 2 feature2 group1
#> 3 feature3 group1
#> 4 feature4 group2
#> 5 feature5 group2

# We put feature 1 in `group3`
meta[1, 2] <- "group3"
meta[1:5, ]
#>    feature  group
#> 1 feature1 group3
#> 2 feature2 group1
#> 3 feature3 group1
#> 4 feature4 group2
#> 5 feature5 group2

Then we generate the group parameter for group_imp() up front. We can see that group3 has been padded to have 10 columns.
For each group, we also assign a different number of nearest neighbors as a demonstration.

set.seed(1234)
group_imp_df <- prep_groups(colnames(obj), group = meta, min_group_size = 10)
group_imp_df$parameters <- list(list(k = 3), list(k = 4), list(k = 5))
group_imp_df
#> # slideimp table: 3 x 4
#>   group          feature             aux parameters
#>  group1 <character [26]> <character [0]> <list [1]>
#>  group2 <character [23]> <character [0]> <list [1]>
#>  group3  <character [1]> <character [9]> <list [1]>

Finally, we impute obj using the modified group_imp_df. The k = 10 passed to group_imp() is ignored since all groups have group-wise k specified.

knn_results <- group_imp(obj, group = group_imp_df, cores = 4, k = 10)
#> Imputing 3 groups using KNN.
#> Running mode: threaded (4 cores)
print(knn_results, p = 4)
#> Method: group_imp (KNN imputation)
#> Dimensions: 20 x 50
#> 
#>           feature1  feature2  feature3  feature4
#> sample1 0.03031619 0.1352710 0.4653579 0.1143298
#> sample2 0.50962966 0.2313022 0.9737881 0.5198844
#> sample3 0.69068459 0.5020337 0.7863144 0.9997625
#> sample4 0.40710170 0.4094603 0.3228294 0.2529592
#> sample5 0.73113251 0.4108405 0.6385945 0.4472338
#> sample6 0.48993078 0.4950764 0.6553896 0.6737998
#> # Showing 6 of 20 rows and 4 of 50 columns

Sliding Window Imputation for WGBS/EM-seq Data with `slide_imp()`

Select `window_size`, `overlap_size`, and PCA/K-NN Parameters

We simulate the output of the {methylKit} package.
- Note: WGBS/EM-seq data should be grouped by chromosome before performing sliding window imputation.
- Here, we simulate 1000 sites.
Importantly, we simulate the location vector of each feature (genomic position) with varying spacing of 50 to 500 bp apart from each other.
The locations vector contains the genomic position of each feature (column). It is used to determine which columns are grouped together given a window size.

set.seed(1234)
sample_names <- paste0("S", 1:10)
n_sites <- 1000

# Simulate positions with 50–500 bp between each site
distances_between <- sample(50:500, size = n_sites, replace = TRUE)
locations <- cumsum(distances_between) # <- important, location vector

methyl <- data.frame(
  chr = "chr1",
  start = locations,
  end = locations,
  strand = "+"
)

for (i in seq_along(sample_names)) {
  methyl[[paste0("numCs", i)]] <- sample.int(100, size = n_sites, replace = TRUE)
  methyl[[paste0("numTs", i)]] <- sample.int(100, size = n_sites, replace = TRUE)
  methyl[[paste0("coverage", i)]] <- methyl[[paste0("numCs", i)]] + methyl[[paste0("numTs", i)]]
}

methyl[1:5, 1:10]
#>    chr start  end strand numCs1 numTs1 coverage1 numCs2 numTs2 coverage2
#> 1 chr1   333  333      +     83     68       151     84     36       120
#> 2 chr1   718  718      +     82      2        84     54     99       153
#> 3 chr1  1173 1173      +     48      2        50     44     16        60
#> 4 chr1  1323 1323      +     70     48       118     48     56       104
#> 5 chr1  1483 1483      +     88     15       103     83     19       102

First, we convert the data into a beta matrix with features in the columns.

numCs_matrix <- as.matrix(methyl[, paste0("numCs", seq_along(sample_names))])
cov_matrix <- as.matrix(methyl[, paste0("coverage", seq_along(sample_names))])
beta_matrix <- numCs_matrix / cov_matrix

colnames(beta_matrix) <- sample_names
rownames(beta_matrix) <- methyl$start

beta_matrix <- t(beta_matrix)
# Set 10% of the data to missing
set.seed(1234)
beta_matrix[sample.int(length(beta_matrix), floor(length(beta_matrix) * 0.1))] <- NA
beta_matrix[1:4, 1:4]
#>           333       718       1173      1323
#> S1 0.54966887 0.9761905 0.96000000 0.5932203
#> S2 0.70000000 0.3529412 0.73333333 0.4615385
#> S3 0.06521739 0.7053571         NA 0.6507937
#> S4 0.69444444 0.2846154 0.04347826 0.7636364

Then, in a real dataset, we would tune hyperparameters using chr22. Here, as a demonstration, we use the whole data since the size is small.
We use 2 repetitions of cross-validation (increase to 10–30 in real analyses). We are selecting between:
- ncp (number of principal components) of 2 or 4, indicating that we are performing sliding PCA imputation. Pass k for sliding K-NN imputation.
- window_size of 5,000 or 10,000 bp.
- overlap_size fixed at 1,000 bp (does not affect results much in real analyses).

params <- expand.grid(ncp = c(2, 4), window_size = c(5000, 10000))
params$overlap_size <- 1000
params$min_window_n <- 20 # windows with less than 20 columns are dropped

# Increase n_reps from 2 in actual analyses and use another chromosome (i.e., chr22)
tune_slide_pca <- tune_imp(
  obj = beta_matrix,
  parameters = params,
  .f = "slide_imp",
  n_reps = 2,
  location = locations
)
#> Tuning `slide_imp()`
#> Step 1/2: Resolving NA locations
#> ℹ Using default `num_na` = 500 (~5% of cells).
#>   Increase for more reliability or decrease if missing is dense.
#> Running mode: sequential
#> Step 2/2: Tuning

metrics <- compute_metrics(tune_slide_pca)

aggregate(.estimate ~ .metric + ncp + window_size, data = metrics, FUN = mean)
#>   .metric ncp window_size .estimate
#> 1     mae   2        5000 0.2251033
#> 2    rmse   2        5000 0.2830913
#> 3     mae   4        5000 0.2391354
#> 4    rmse   4        5000 0.2976971
#> 5     mae   2       10000 0.2116169
#> 6    rmse   2       10000 0.2639824
#> 7     mae   4       10000 0.2220744
#> 8    rmse   4       10000 0.2754316

Finally, we can use slide_imp() to impute the full beta_matrix. Use the best parameter combination from the cross-validation metrics.
First, we use dry_run = TRUE to examine the columns to be imputed.
- start and end are window location vectors.
- window_n is the number of features included in the window.

slide_imp(
  obj = beta_matrix,
  location = locations,
  window_size = 5000,
  overlap_size = 1000,
  ncp = 2,
  min_window_n = 20,
  dry_run = TRUE # <- dry_run to inspect the windows
)
#> Dropping 43 window(s) with fewer than 20 columns.
#> # slideimp table: 24 x 4
#>  start end window_n  subset_local
#>     15  34       20 <double [20]>
#>    104 124       21 <double [21]>
#>    188 209       22 <double [22]>
#>    205 225       21 <double [21]>
#>    222 241       20 <double [20]>
#>    239 258       20 <double [20]>
#>    254 273       20 <double [20]>
#>    369 388       20 <double [20]>
#>    385 404       20 <double [20]>
#>    402 424       23 <double [23]>
#> # ... with 14 more rows

Turn off dry_run to impute the data

slide_imp(
  obj = beta_matrix,
  location = locations,
  window_size = 5000,
  overlap_size = 1000,
  ncp = 2,
  min_window_n = 20,
  dry_run = FALSE,
  .progress = FALSE
)
#> Method: slide_imp (PCA imputation)
#> Dimensions: 10 x 1000
#> Note: requested columns still contain NA values
#> 
#>           333       718       1173      1323      1483       1925
#> S1 0.54966887 0.9761905 0.96000000 0.5932203        NA 0.08695652
#> S2 0.70000000 0.3529412 0.73333333 0.4615385 0.8137255 0.45454545
#> S3 0.06521739 0.7053571         NA 0.6507937 0.4393064 0.37735849
#> S4 0.69444444 0.2846154 0.04347826 0.7636364 0.9595960 0.54621849
#> S5 0.83505155 0.5777778 0.47517730        NA 0.7368421 0.21666667
#> S6 0.26612903 0.3451327 0.53072626 0.5000000 0.6465517 0.37288136
#> # Showing 6 of 10 rows and 6 of 1000 columns

Subset and Flanking Mode

Use this mode when you only need to impute specific target features (e.g., clock CpGs) rather than the entire dataset.
- Pass the desired feature names to the subset argument. Only windows containing these features will be imputed.
- Set flank = TRUE to build windows centered on each feature in the subset. Each window will extend window_size bp on either side of the target feature (flanking mode).
- In this mode, the overlap_size argument is ignored.
In this example, we only want to impute the features "1323" and "33810" by creating 5,000 bp flanking windows around each feature:

slide_imp(
  obj = beta_matrix,
  location = locations,
  window_size = 5000,
  ncp = 2,
  min_window_n = 20,
  subset = c("1323", "33810"),
  flank = TRUE,
  dry_run = TRUE
)
#> # slideimp table: 2 x 5
#>  start end window_n target  subset_local
#>      1  22       22      4 <integer [1]>
#>    101 138       38    122 <integer [1]>

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slideimp

Comparing Methods Using a Shared Cross-Validation Split with tune_imp()

Group-Wise Imputation with Small-Group Padding and Group-Wise Parameters with group_imp()

Sliding Window Imputation for WGBS/EM-seq Data with slide_imp()

Select window_size, overlap_size, and PCA/K-NN Parameters

Subset and Flanking Mode

Comparing Methods Using a Shared Cross-Validation Split with `tune_imp()`

Group-Wise Imputation with Small-Group Padding and Group-Wise Parameters with `group_imp()`

Sliding Window Imputation for WGBS/EM-seq Data with `slide_imp()`

Select `window_size`, `overlap_size`, and PCA/K-NN Parameters