diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 66b38490..5a3a04c2 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-20T02:20:47","documenter_version":"1.1.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.3","generation_timestamp":"2023-10-21T02:18:52","documenter_version":"1.1.1"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 7658ee04..3a0927d0 100644 --- a/dev/index.html +++ b/dev/index.html @@ -3,4 +3,4 @@ pkg"add https://github.com/OpenMendel/SnpArrays.jl" pkg"add https://github.com/OpenMendel/VCFTools.jl" pkg"add https://github.com/OpenMendel/BGEN.jl" -pkg"add https://github.com/OpenMendel/MendelImpute.jl"

This package supports Julia v1.6+.

Manual Outline

+pkg"add https://github.com/OpenMendel/MendelImpute.jl"

This package supports Julia v1.6+.

Manual Outline

diff --git a/dev/man/Phasing_and_Imputation/index.html b/dev/man/Phasing_and_Imputation/index.html index 8ccd9c54..1a04a88f 100644 --- a/dev/man/Phasing_and_Imputation/index.html +++ b/dev/man/Phasing_and_Imputation/index.html @@ -145,4 +145,4 @@ # visualize error distribution histogram(quality[:error], label=:none, xlabel="per-sample quality score", - ylabel="Number of samples")

svg

Conclusion: Most samples are well imputed (e.g. score close to 1), but some samples are indeed poorly imputed. From the histogram, we can safely filtered out samples with score $< 0.999$ as that would remove poorly imputed individuals without reducing sample size too much.

+ ylabel="Number of samples")

svg

Conclusion: Most samples are well imputed (e.g. score close to 1), but some samples are indeed poorly imputed. From the histogram, we can safely filtered out samples with score $< 0.999$ as that would remove poorly imputed individuals without reducing sample size too much.

diff --git a/dev/man/api/index.html b/dev/man/api/index.html index 039d395a..49fb3303 100644 --- a/dev/man/api/index.html +++ b/dev/man/api/index.html @@ -6,4 +6,4 @@ [d::Int], [minwidth::Int], [overlap::Float64])

Cuts a haplotype matrix reffile into windows of variable width so that each window has less than d unique haplotypes. Saves result to outfile as a compressed binary format. All SNPs in tgtfile must be present in reffile. All genotypes in reffile must be phased and non-missing, and all genotype positions must be unique.

Inputs

Optional Inputs

Why is tgtfile required?

The unique haplotypes in each window is computed on the typed SNPs only. A genotype matrix tgtfile is used to identify the typed SNPs. In the future, hopefully we can pre-compute compressed haplotype panels for all genotyping platforms and provide them as downloadable files. But currently, users must run this function by themselves.

source
MendelImpute.convert_compressed — Function
convert_compressed(t<:Real, phaseinfo::String, reffile::String)

Converts phaseinfo into a phased genotype matrix of type t using the full reference haplotype panel H

Inputs

  • t: Type of matrix. If bool, genotypes are converted to a BitMatrix
  • phaseinfo: Vector of HaplotypeMosaicPairs stored in .jlso format
  • reffile: The complete (uncompressed) haplotype reference file

Output

  • X1: allele 1 of the phased genotype. Each column is a sample. X = X1 + X2.
  • X2: allele 2 of the phased genotype. Each column is a sample. X = X1 + X2.
  • phase: the original data structure after phasing and imputation.
  • sampleID: The ID's of each imputed person.
  • H: the complete reference haplotype panel. Columns of H are haplotypes.
source
convert_compressed(t<:Real, phaseinfo::Vector{HaplotypeMosaicPair}, H::AbstractMatrix)

Columns of H are haplotypes.

source
MendelImpute.admixture_global — Function
admixture_global(tgtfile::String, reffile::String, 
     refID_to_population::Dict{String, String}, populations::Vector{String})

Computes global ancestry estimates for each sample in tgtfile using a labeled reference panel reffile.

Inputs

  • tgtfile: VCF or PLINK files. VCF files should end in .vcf or .vcf.gz. PLINK files should exclude .bim/.bed/.fam trailings but the trio must all be present in the same directory.
  • reffile: Reference haplotype file ending in .jlso (compressed binary files). See compress_haplotypes.
  • refID_to_population: A dictionary mapping each sample IDs in the haplotype reference panel to their population origin. For examples, see output of thousand_genome_population_to_superpopulation and thousand_genome_samples_to_super_population
  • populations: A vector of String containing unique populations present in values(refID_to_population).

Optional Inputs

  • Q_outfile: Output file name for the estimated Q matrix. Default Q_outfile="mendelimpute.ancestry.Q".
  • imputed_outfile: Output file name for the imputed genotypes ending in .jlso. Default impute_outfile = "mendelimpute.ancestry.Q.jlso"

Output

  • Q: A DataFrame containing estimated ancestry fractions. Each row is a sample. Matrix will be saved in mendelimpute.ancestry.Q
source
MendelImpute.admixture_local — Function
admixture_local(tgtfile::String, reffile::String, 
     refID_to_population::Dict{String, String}, populations::Vector{String},
-    population_colors::Vector{RGB{FixedPointNumbers.N0f8}})

Computes global ancestry estimates for each sample in tgtfile using a labeled reference panel reffile.

Inputs

  • tgtfile: VCF or PLINK files. VCF files should end in .vcf or .vcf.gz. PLINK files should exclude .bim/.bed/.fam trailings but the trio must all be present in the same directory.
  • reffile: Reference haplotype file ending in .jlso (compressed binary files). See compress_haplotypes.
  • refID_to_population: A dictionary mapping each sample IDs in the haplotype reference panel to their population origin. For examples, see output of thousand_genome_population_to_superpopulation and thousand_genome_samples_to_super_population
  • population: A list String containing unique populations present in values(refID_to_population).
  • population_colors: A vector of colors for each population. typeof(population_colors} should be Vector{RGB{FixedPointNumbers.N0f8}}

Output

  • Q: Matrix containing estimated ancestry fractions. Each row is a haplotype. Sample 1's haplotypes are in rows 1 and 2, sample 2's are in rows 3, 4...etc.
  • pop_colors: Matrix with sample dimension of Q storing colors.
source
MendelImpute.thousand_genome_samples_to_population — Function
thousand_genome_samples_to_population()

Creates a dictionaries mapping sample IDs of 1000 genome project to 26 population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
MendelImpute.thousand_genome_samples_to_super_population — Function
thousand_genome_samples_to_population()

Creates a dictionaries mapping sample IDs of 1000 genome project to 5 super population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
MendelImpute.thousand_genome_population_to_superpopulation — Function
thousand_genome_population_to_superpopulation()

Creates a dictionary mapping population codes of 1000 genome project to their super-population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
+ population_colors::Vector{RGB{FixedPointNumbers.N0f8}})

Computes global ancestry estimates for each sample in tgtfile using a labeled reference panel reffile.

Inputs

Output

source
MendelImpute.thousand_genome_samples_to_population — Function
thousand_genome_samples_to_population()

Creates a dictionaries mapping sample IDs of 1000 genome project to 26 population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
MendelImpute.thousand_genome_samples_to_super_population — Function
thousand_genome_samples_to_population()

Creates a dictionaries mapping sample IDs of 1000 genome project to 5 super population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
MendelImpute.thousand_genome_population_to_superpopulation — Function
thousand_genome_population_to_superpopulation()

Creates a dictionary mapping population codes of 1000 genome project to their super-population codes.

Population code and super population codes are described here: https://www.internationalgenome.org/category/population/

source
diff --git a/dev/man/painting/index.html b/dev/man/painting/index.html index 31b41814..9eb61a16 100644 --- a/dev/man/painting/index.html +++ b/dev/man/painting/index.html @@ -158,4 +158,4 @@ inset = (1, bbox(-0.05, -0.1, 0.05, 1.1, :bottom, :right)), subplot = 2) # save figure -# savefig(local_plt, "local_admixture.png")

svg

Conclusion:

For more details, please refer to our paper, or file an issue on GitHub.

+# savefig(local_plt, "local_admixture.png")

svg

Conclusion:

For more details, please refer to our paper, or file an issue on GitHub.

diff --git a/dev/man/performance/index.html b/dev/man/performance/index.html index 4ba2e182..bfd71f3a 100644 --- a/dev/man/performance/index.html +++ b/dev/man/performance/index.html @@ -1,2 +1,2 @@ -Performance Gotchas · MendelImpute
GitHub

Performance gotchas

First time performance

In a fresh Julia session, the first time any function gets called will take a long time because the code has to be compiled on the spot. For instance, compare

@time using MendelImpute
  6.958706 seconds (16.72 M allocations: 1.148 GiB, 5.00% gc time)
@time using MendelImpute
  0.022658 seconds (32.81 k allocations: 1.886 MiB, 99.49% compilation time)

The first call was 350 times slower than the second time! Fortunately, for large problems, compilation time becomes negligible.

Run MendelImpute in parallel

If Julia is started with multiple threads (e.g. julia --threads 4), MendelImpute.jl will automatically run your code in parallel.

  1. How to start Julia with multiple threads.
  2. Execute Threads.nthreads() within Julia to check if multiple thread is enabled
  3. Set the number of BLAS threads to be 1 by using LinearAlgebra; BLAS.set_num_threads(1). This avoids oversubscription.
Note

We recommend number of threads equal to the number of physical CPU cores on your machine. Number of Julia threads should never exceed number of physical CPU cores!! Hyperthreading is valuable for I/O operations (in our experience), but not for linear algebra routines used throughout MendelImpute.

Compressing haplotype panels is slow

Currently it is recommended to build a new compressed reference haplotype panel for every new set of typed SNPs (although this is not strictly required). The compression routine is slow because reading raw VCF files is slow. Thus, it is highly advised that one try to use the same set of typed SNPs as much as possible.

We are actively developing a new set of functions in SnpArrays.jl to alleviate this problem. Since SnpArrays use memory mapping, read times can be improved dramatically.

max_d too high (or too low)

When you compress the haplotype panels into a .jlso format, you specified max_d which is the maximum number of unique haplotypes per window. We generally recommend using max_d = 1000, BUT 1000 may be too small if you use a reference panel larger than HRC. In that case, you can try larger max_d, which will improve error rate.

Symptoms for max_d too high:

Computing optimal haplotypes is too slow. In particular, the timing for haplopair search is too high.

Symptoms for max_d too low:

Too few typed SNPs per window indicates max_d is set too low. You can calculate the number of typed SNPs per window by dividing the total number of SNPs in the target file by the total windows (a number that will be output after every run). Ideally you want an average of 400 typed SNPs per window, but something as low as 50 still works. Something like 10~20 is too low.

I really want to use a high max_d

A high max_d generally improve error, so it is understandable you want to do so. If a high max_d value runs too slow, try setting stepwise = 100 and max_haplotypes to a number that is close to 1000. This avoids searching the global minimizer of the least-squares problem for windows that have more than max_haplotypes number of unique haplotypes. Setting thinning_factor instead of stepwise have a similar effect. Details for these 2 heuristic searches are explained in the appendix of our paper.

Do you have enough memory (RAM)?

While MendelImpute uses the least RAM compared to competing softwares (as of 2020), it is still possible for large imputation problems to consume all available RAM. If this happens, Julia will first try to use swap before crashing (until all of swap is consumed). Monitor your RAM usage constantly to make sure this doesn't happen. On Mac/Linux machines, the top or htop command will monitor this information. Alternatively, the /usr/bin/time command will automatically records max RAM usage for job and whether any swap had been performed.

Rough estimate for amount of RAM needed

There are 4 things that require lots of memory:

  • The target genotype matrix $\mathbf{X}_{n \times p}$ requires $n \times p \times 8$ bits. If $\mathbf{X}$ is dosage data, then you need instead $n \times p \times 32$ bits
  • The matrix $\mathbf{M}_{d \times d}$ requires $c \times d \times d \times 32$ bits, where $c$ is the number of parallel threads used and $d$ is the number specified in the compress_haplotypes function.
  • The matrix $\mathbf{N}_{n \times d}$ requires $c \times n \times d \times 32$ bits, where $c$ is the number of parallel threads used and $d$ is the number specified in the compress_haplotypes function.
  • The compressed reference haplotype panel produced by the compress_haplotypes function. This typically requires about $3r$ gigabytes of RAM where $r$ is your panel's size in .vcf.gz.

If you do not have the above issues and your code is still running slow, file an issue on GitHub and we will take a look at it ASAP.

+Performance Gotchas · MendelImpute
GitHub

Performance gotchas

First time performance

In a fresh Julia session, the first time any function gets called will take a long time because the code has to be compiled on the spot. For instance, compare

@time using MendelImpute
  6.958706 seconds (16.72 M allocations: 1.148 GiB, 5.00% gc time)
@time using MendelImpute
  0.022658 seconds (32.81 k allocations: 1.886 MiB, 99.49% compilation time)

The first call was 350 times slower than the second time! Fortunately, for large problems, compilation time becomes negligible.

Run MendelImpute in parallel

If Julia is started with multiple threads (e.g. julia --threads 4), MendelImpute.jl will automatically run your code in parallel.

  1. How to start Julia with multiple threads.
  2. Execute Threads.nthreads() within Julia to check if multiple thread is enabled
  3. Set the number of BLAS threads to be 1 by using LinearAlgebra; BLAS.set_num_threads(1). This avoids oversubscription.
Note

We recommend number of threads equal to the number of physical CPU cores on your machine. Number of Julia threads should never exceed number of physical CPU cores!! Hyperthreading is valuable for I/O operations (in our experience), but not for linear algebra routines used throughout MendelImpute.

Compressing haplotype panels is slow

Currently it is recommended to build a new compressed reference haplotype panel for every new set of typed SNPs (although this is not strictly required). The compression routine is slow because reading raw VCF files is slow. Thus, it is highly advised that one try to use the same set of typed SNPs as much as possible.

We are actively developing a new set of functions in SnpArrays.jl to alleviate this problem. Since SnpArrays use memory mapping, read times can be improved dramatically.

max_d too high (or too low)

When you compress the haplotype panels into a .jlso format, you specified max_d which is the maximum number of unique haplotypes per window. We generally recommend using max_d = 1000, BUT 1000 may be too small if you use a reference panel larger than HRC. In that case, you can try larger max_d, which will improve error rate.

Symptoms for max_d too high:

Computing optimal haplotypes is too slow. In particular, the timing for haplopair search is too high.

Symptoms for max_d too low:

Too few typed SNPs per window indicates max_d is set too low. You can calculate the number of typed SNPs per window by dividing the total number of SNPs in the target file by the total windows (a number that will be output after every run). Ideally you want an average of 400 typed SNPs per window, but something as low as 50 still works. Something like 10~20 is too low.

I really want to use a high max_d

A high max_d generally improve error, so it is understandable you want to do so. If a high max_d value runs too slow, try setting stepwise = 100 and max_haplotypes to a number that is close to 1000. This avoids searching the global minimizer of the least-squares problem for windows that have more than max_haplotypes number of unique haplotypes. Setting thinning_factor instead of stepwise have a similar effect. Details for these 2 heuristic searches are explained in the appendix of our paper.

Do you have enough memory (RAM)?

While MendelImpute uses the least RAM compared to competing softwares (as of 2020), it is still possible for large imputation problems to consume all available RAM. If this happens, Julia will first try to use swap before crashing (until all of swap is consumed). Monitor your RAM usage constantly to make sure this doesn't happen. On Mac/Linux machines, the top or htop command will monitor this information. Alternatively, the /usr/bin/time command will automatically records max RAM usage for job and whether any swap had been performed.

Rough estimate for amount of RAM needed

There are 4 things that require lots of memory:

  • The target genotype matrix $\mathbf{X}_{n \times p}$ requires $n \times p \times 8$ bits. If $\mathbf{X}$ is dosage data, then you need instead $n \times p \times 32$ bits
  • The matrix $\mathbf{M}_{d \times d}$ requires $c \times d \times d \times 32$ bits, where $c$ is the number of parallel threads used and $d$ is the number specified in the compress_haplotypes function.
  • The matrix $\mathbf{N}_{n \times d}$ requires $c \times n \times d \times 32$ bits, where $c$ is the number of parallel threads used and $d$ is the number specified in the compress_haplotypes function.
  • The compressed reference haplotype panel produced by the compress_haplotypes function. This typically requires about $3r$ gigabytes of RAM where $r$ is your panel's size in .vcf.gz.

If you do not have the above issues and your code is still running slow, file an issue on GitHub and we will take a look at it ASAP.

diff --git a/dev/man/script/index.html b/dev/man/script/index.html index 7f5c5116..dade7966 100644 --- a/dev/man/script/index.html +++ b/dev/man/script/index.html @@ -9,4 +9,4 @@ BLAS.set_num_threads(1) # set BLAS threads to 1 (see performance gotchas) # run MendelImpute with default options -phase(tgtfile, reffile, outfile)

Then in the terminal/command-prompt, you can do

julia --threads 8 impute.jl ref.jlso target.vcf.gz output.vcf.gz
+phase(tgtfile, reffile, outfile)

Then in the terminal/command-prompt, you can do

julia --threads 8 impute.jl ref.jlso target.vcf.gz output.vcf.gz
diff --git a/dev/man/ultra+compress/index.html b/dev/man/ultra+compress/index.html index eecabf05..3fb03dac 100644 --- a/dev/man/ultra+compress/index.html +++ b/dev/man/ultra+compress/index.html @@ -71,4 +71,4 @@ X1, X2, phaseinfo, sampleID, H = convert_compressed(Float64, tgtfile, reffile);
importing reference data...100%|████████████████████████| Time: 0:01:51

Check this compression protocol exhibit same error rate with standard VCF compression. Note that X1, X2, and H are transposed. 

X_truth  = convert_gt(Float64, "target.chr22.full.vcf.gz") # import true genotypes
 X_mendel = (X1 + X2)' # transpose X1 and X2
 n, p = size(X_mendel)
-println("error overall = $(sum(X_mendel .!= X_truth) / n / p)")
error overall = 0.00527504782243333
+println("error overall = $(sum(X_mendel .!= X_truth) / n / p)")
error overall = 0.00527504782243333