Hi,

I'm working with the impute::impute.knn function and noticed that the imputation result changes depending on the order of the input matrix. However, I would expect that rng.seed would produce the same result. Despite the different results, the spearman and pearson correlation between samples is still very high. Is this a bug? Otherwise, I'd appreciate any help understanding this.

Thanks!

#Load the impute library
> library(impute)

#Load the documentation sample data
> data(khanmiss)
> khan.expr <- khanmiss[-1, -(1:2)]

#Add row names for easier sorting later
> rownames(khan.expr) <- paste0("Gene", 1:2308)

#Check that no random seed exists prior to running imputation on the data set as is
> if(exists(".Random.seed")) rm(.Random.seed)

#Run imputation
> Result1_OriginalOrder <- impute.knn(as.matrix(khan.expr), rng.seed = 500)
Cluster size 2308 broken into 1509 799 
Cluster size 1509 broken into 401 1108 
Done cluster 401 
Done cluster 1108 
Done cluster 1509 
Done cluster 799 

#Create a new row order for the expression matrix
> khan.expr_neworder <- khan.expr[c(2308:1),]

#Clear out any random number state that may be stored
> if(exists(".Random.seed")) rm(.Random.seed)

#Run imputation on the new matrix order
> Result2_ChangedOrder <- impute.knn(as.matrix(khan.expr_neworder), rng.seed = 500)
Cluster size 2308 broken into 1458 850 
Done cluster 1458 
Done cluster 850 

#Extract the imputed results and match row-order between the two data frames
> result1_data <- Result1_OriginalOrder$data
> result2 <- Result2_ChangedOrder$data
> result2_data <- result2[rownames(result1_data),]

#Confirm that these results are different; otherwise all.equal would be TRUE
> all.equal(result1_data, result2_data)
[1] "Mean relative difference: 0.1456079"

#Re-imputing changed order with original order
> if(exists(".Random.seed")) rm(.Random.seed)
> khan.expr_originalorder <- khan.expr_neworder[c(2308:1),]
> all.equal(khan.expr, khan.expr_originalorder)
[1] TRUE

#Run imputation on the new matrix order
> Result3_OriginalOrder <- impute.knn(as.matrix(khan.expr_originalorder), rng.seed = 500)
Cluster size 2308 broken into 1509 799 
Cluster size 1509 broken into 401 1108 
Done cluster 401 
Done cluster 1108 
Done cluster 1509 
Done cluster 799 
> result3_data <- Result3_OriginalOrder$data

# Confirm that the original matrix order produces the same result
> all.equal(result3_data, result1_data)
[1] TRUE

> sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] impute_1.76.0

loaded via a namespace (and not attached):
 [1] digest_0.6.33     fastmap_1.1.1     xfun_0.41         lattice_0.22-5    knitr_1.45        parallel_4.3.1   
 [7] htmltools_0.5.7   rmarkdown_2.25    cli_3.6.1         ape_5.7-1         grid_4.3.1        compiler_4.3.1   
[13] rstudioapi_0.15.0 tools_4.3.1       nlme_3.1-164      phylotools_0.2.2  evaluate_0.23     yaml_2.3.8       
[19] Rcpp_1.0.11       rlang_1.1.2

Does this help you to understand?

> Result1_OriginalOrder <- impute.knn(as.matrix(khan.expr), rng.seed = 500, maxp = 2308)
> Result2_ChangedOrder <- impute.knn(as.matrix(khan.expr_neworder), rng.seed = 500, maxp = 2308)
> result1_data <- Result1_OriginalOrder$data
> result2 <- Result2_ChangedOrder$data
> result2_data <- result2[rownames(result1_data),]
> all.equal(result1_data, result2_data)
[1] TRUE