README.md
In hendriau/Summix: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data
Summix2
Summix2 is a suite of methods that detect and leverage substructure in
genetic summary data. This package builds on Summix, a method that
estimates and adjusts for substructure in genetic summary that was
developed by the Hendricks Research Team at the University of Colorado
Denver.
Find more details about Summix in our manuscript published in the
American Journal of Human
Genetics.
For individual function specifics in Summix2:
summix — fast forward to
example
adjAF — fast forward to example
summix_local — fast forward to
example
Package Installation
To install this package, start R (version “4.3”) and run the following
commands:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Summix")
summix
The summix() function estimates mixture proportions of reference
groups within genetic summary (allele frequency) data using sequential
quadratic programming performed with the slsqp()
function
in the nloptr package.
summix() Input
Mandatory parameters are:
-
data: A data frame of the observed and reference group allele
frequencies for N genetic variants.
-
reference: A character vector of the column names for K reference
groups.
-
observed: A character value that is the column name for the
observed group.
Optional parameters are:
-
pi.start: Numeric vector of length K containing the user’s initial
guess for the reference group proportions. If not specified, this
defaults to 1/K where K is the number of reference groups.
-
goodness.of.fit: Default value is TRUE. If set as FALSE, the
user will override the default goodness of fit measure and return the
raw objective loss from slsqp().
-
override_removeSmallRef: Default value is FALSE. If set as
TRUE, the user will override the automatic removal of reference
groups with \<1% global proportions - this is not recommended.
-
network: Default value is FALSE. If set as TRUE, function will
return a network diagram with nodes as estimated substructure
proportions and edges as degree of similarity between the given node
pair.
-
N_reference: numeric vector of the sample sizes for each of the K
reference groups; must be specified if network = “TRUE”.
-
reference_colors: A character vector of length K that specifies
the color each reference group node in the network plot. If not
specified, this defaults to K random colors.
summix() Output
A data frame with the following columns:
-
goodness.of.fit: Scaled objective loss from slsqp() reflecting
the fit of the reference data. Values between 0.5-1.5 are considered
moderate fit and should be used with caution. Values greater than 1.5
indicate poor fit, and users should not perform further analyses using
Summix2.
-
iterations: The number of iterations for the SLSQP algorithm
before best-fit reference group proportion estimates are found.
-
time: The time in seconds before best-fit reference group mixture
proportion estimations are found by the SLSQP algorithm.
-
filtered: The number of genetic variants not used in the reference
group mixture proportion estimation due to missing values.
-
K columns of mixture proportions of reference groups input into
the function.
Additional Output if summix_network = TRUE:
- Summix Network: A network diagram with nodes as estimated
substructure proportions and edges as degree of similarity between the
given node pair; exportable as a png.
adjAF
The adjAF() function adjusts allele frequencies to match reference
group substructure mixture proportions in a given target group or
individual.
adjAF() Input
Mandatory parameters are:
-
data: A data frame containing the unadjusted allele frequency for
the observed group and K reference group allele frequencies for N
genetic variants.
-
reference: A character vector of the column names for K reference
groups.
-
observed: A character value that is the column name for the
observed group.
-
pi.target: A numeric vector of the mixture proportions for K
reference groups in the target group or individual.
-
pi.observed: A numeric vector of the mixture proportions for K
reference groups in the observed group.
-
N_reference: A numeric vector of the sample sizes for each of the
K reference groups that is in the same order as the reference
parameter.
-
N_observed: A numeric value of the sample size of the observed
group.
-
adj_method: User choice of method for the allele frequency
adjustment; options “average” and “leave_one_out” are available.
Defaults to “average”.
-
filter: Sets adjusted allele frequencies equal to 1 if > 1, to 0
if > -.005 and \< 0, and removes adjusted allele frequencies \<
-.005. Default is TRUE.
adjAF() Output
A data frame with the following columns:
-
pi: A table of input reference groups, pi.observed, and pi.target.
-
observed.data: The name of the data column for the observed group
from which the adjusted allele frequencies are estimated.
-
Nsnps: The number of SNPs for which adjusted AF is estimated.
-
adjusted.AF: A data frame of original data with an appended column
of adjusted allele frequencies.
-
effective.sample.size: The sample size of individuals effectively
represented by the adjusted allele frequencies.
summix_local
The summix_local() function estimates local substructure mixture
proportions in genetic summary data using the same slspq()
functionality as summix(). summix_local() also performs a selection
scan (optional) that identifies regions of selection along the given
chromosome.
summix_local() Input
Mandatory parameters are:
-
data: A data frame of the observed group and reference group
allele frequencies for N genetic variants on a single chromosome. Must
contain a column specifying the genetic variant positions.
-
reference: A character vector of the column names for K reference
groups.
-
observed: A character value that is the column name for the
observed group.
-
position_col: A character value that is the column name for the
genetic variants positions. Default is “POS”.
-
maxStepSize: A numeric value that defines the maximum gap in base
pairs between two consecutive genetic variants within a given window.
Default is 1000.
Optional parameters are:
-
algorithm: User choice of algorithm to define local substructure
blocks; options “fastcatch” and “windows” are available.
“windows” uses a fixed window in a sliding windows algorithm.
“fastcatch” allows dynamic window sizes. The “fastcatch” algorithm
is recommended- though it is computationally slower. Default is
“fastcatch”.
-
type: User choice of how to define window size; options
“variants” and “bp” are available where “variants” defines
window size as the number of variants in a given window and “bp”
defines window size as the number of base pairs in a given window.
Default is “variants”.
-
override_fit: Default is FALSE. If set as TRUE, the user will
override the auto-stop of summix_local() that occurs if the global
goodness of fit value is greater than 1.5 (indicating a poor fit of
the reference data to the observed data).
-
override_removeSmallAnc: Default is FALSE. If set as TRUE, the
user will override the automatic removal of reference ancestries with
\<2% global proportions – this is not recommended.
-
selection_scan: User option to perform a selection scan on the
given chromosome. Default is FALSE. If set as TRUE, a test
statistic will be calculated for each local substructure block. Note:
the user can expect extended computation time if this option is set as
TRUE.
Conditional parameters are:
If algorithm = “windows”:
- windowOverlap: A numeric value that defines the number of variants
or the number of base pairs that overlap between the given sliding
windows. Default is 200.
If algorithm = “fastcatch”:
- diffThreshold: A numeric value that defines the percent difference
threshold to mark the end of a local substructure block. Default is
0.02.
If type = “variants”:
- maxVariants: A numeric value that specifies the maximum number of
genetic variants allowed to define a given window.
If type = “bp”:
- maxWindowSize: A numeric value that defines the maximum allowed
window size by the number of base pairs in a given window.
If algorithm = “fastcatch” and type = “variants”:
- minVariants: A numeric value that specifies the minimum number of
genetic variants allowed to define a given window.
If algorithm = “fastcatch” and type = “bp”:
- minWindowSize: A numeric value that specifies the minimum number
of base pairs allowed to define a given window.
If selection_scan = TRUE:
- NSimRef: A numeric vector of the sample sizes for each of the K
reference groups that is in the same order as the reference parameter.
This is used in a simulation framework that calculates within local
block substructure standard error.
summix_local() Output
A data frame with a row for each local substructure block and the
following columns:
-
goodness.of.fit: Scaled objective loss from slsqp() reflecting
the fit of the reference data. Values between 0.5-1.5 are considered
moderate fit and should be used with caution. Values greater than 1.5
indicate poor fit, and users should not perform further analyses using
Summix2.
-
iterations: The number of iterations for the SLSQP algorithm
before best-fit reference group mixture proportion estimations are
found.
-
time: The time in seconds before best-fit reference group mixture
proportion estimations are found by the SLSQP algorithm.
-
filtered: The number of genetic variants not used in the reference
group mixture proportion estimation due to missing values.
-
K columns of mixture proportions of reference group in the given
local substructure block.
-
nSNPs: The number of genetic variants in the given local
substructure block.
Additional Output if selection_scan = TRUE:
-
K columns of local substructure test statistics for each reference
group in the given local substructure block.
-
K columns of p-values for each reference group in the given local
substructure block. P-values calculated using the Student’s
t-distribution with degrees of freedom=(nSNPs in the block)-1.
Examples using toy data in the Summix package
For quick runs of all demos, we suggest using the data saved within the
Summix library called ancestryData.
A quick demo of summix()
The commands:
library(Summix)
# load the data
data("ancestryData")
# Estimate 5 reference group proportion values for the gnomAD African/African American group
# using a starting guess of .2 for each estimated proportion.
summix(data = ancestryData,
reference=c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
observed="gnomad_AF_afr",
pi.start = c(.2, .2, .2, .2, .2),
goodness.of.fit=TRUE)
#> goodness.of.fit iterations time filtered reference_AF_afr
#> 1 0.4853597 20 0.400059 secs 0 0.812142
#> reference_AF_eur reference_AF_iam
#> 1 0.169953 0.017905
Below is an example of creating a Summix Network plot.
Summix_output<- summix(data = ancestryData,
reference=c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
observed="gnomad_AF_afr",
pi.start = c(.2, .2, .2, .2, .2),
goodness.of.fit=TRUE,
network = TRUE,
N_reference = c(704, 787, 741, 47, 545), reference_colors = c("#FDE725FF", "#5DC863FF", "#21908CFF", "#3B528BFF", "#440154FF"))
Summix_Network <- Summix_output[[2]]
Summix_Network
A quick demo of adjAF()
The commands:
library(Summix)
# load the data
data("ancestryData")
adjusted_data<-adjAF(data = ancestryData,
reference = c("reference_AF_afr", "reference_AF_eur"),
observed = "gnomad_AF_afr",
pi.target = c(1, 0),
pi.observed = c(.85, .15),
adj_method = 'average',
N_reference = c(704,741),
N_observed = 20744,
filter = TRUE)
#>
#>
#> [1] "Note: In this AF adjustment, 0 SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. 0 SNPs (with adjusted AF > 1) were rounded to 1, and 0 SNPs (with adjusted AF <= -.005) were removed from the final results."
#>
#> [1] $pi
#> ref.group pi.observed pi.target
#> 1 reference_AF_afr 0.85 1
#> 2 reference_AF_eur 0.15 0
#>
#> [1] $observed.data
#> [1] "observed AF data to update: 'gnomad_AF_afr'"
#>
#> [1] $Nsnps
#> [1] 1000
#>
#>
#> [1] $effective.sample.size
#> [1] 18336
#>
#>
#> [1] "use $adjusted.AF$adjustedAF to see adjusted AF data"
#>
#>
#> [1] "Note: The accuracy of the AF adjustment is likely lower for rare variants (< .5%)."
print(adjusted_data$adjusted.AF[1:5,])
#> POS REF ALT CHROM reference_AF_afr reference_AF_eas reference_AF_eur
#> 1 31652001 T A chr22 0.040925268 0 0.000000000
#> 2 34509945 C G chr22 0.217971527 0 0.000000000
#> 3 34636589 CAA C chr22 0.181117576 0 0.001149425
#> 4 38889885 A AAG chr22 0.007117446 0 0.000000000
#> 5 49160931 G T chr22 0.064056997 0 0.000000000
#> reference_AF_iam reference_AF_sas gnomad_AF_afr adjustedAF
#> 1 0 0 0.04171490 0.045000811
#> 2 0 0 0.18774500 0.219423999
#> 3 0 0 0.15198300 0.179859133
#> 4 0 0 0.00422064 0.006041453
#> 5 0 0 0.05445710 0.064062087
A quick demo of summix_local()
The commands:
library(Summix)
# load the data
data("ancestryData")
results <- summix_local(data = ancestryData,
reference = c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
NSimRef = c(704,787,741,47,545),
observed="gnomad_AF_afr",
goodness.of.fit = T,
type = "variants",
algorithm = "fastcatch",
minVariants = 150,
maxVariants = 250,
maxStepSize = 1000,
diffThreshold = .02,
override_fit = F,
override_removeSmallAnc = TRUE,
selection_scan = T,
position_col = "POS")
#> [1] "Done getting LA proportions"
#> [1] "Running internal simulations for SE"
#> Time difference of 14.6037 mins
#> [1] "Discovered 7 LA blocks"
print(results$results)
#> Start_Pos End_Pos goodness.of.fit iterations time filtered
#> 1 10595784 19258643 1.2555376 10 0.07431006 0
#> 2 19258643 25252606 0.5018649 13 0.06914687 0
#> 3 25252606 30743600 0.2304807 11 0.09435987 0
#> 4 30743600 35846592 0.2933341 14 0.06985497 0
#> 5 35846592 42706228 0.5480859 14 0.08147001 0
#> 6 42706228 47902876 0.2634092 11 0.07047892 0
#> 7 47902876 50791970 0.2891929 10 0.07448006 0
#> reference_AF_afr reference_AF_eas reference_AF_eur reference_AF_iam
#> 1 0.809208 0.000000 0.146185 0.034417
#> 2 0.816933 0.000000 0.161511 0.021556
#> 3 0.805795 0.002730 0.160926 0.000000
#> 4 0.820812 0.002558 0.161353 0.015276
#> 5 0.806428 0.016357 0.157855 0.019360
#> 6 0.810130 0.004046 0.181798 0.004025
#> 7 0.811265 0.000000 0.148492 0.019896
#> reference_AF_sas nSNPs t.reference_AF_afr.avg t.reference_AF_eas.avg
#> 1 0.010189 150 -0.42983402 -0.57246570
#> 2 0.000000 149 1.18423949 -1.12115283
#> 3 0.030550 149 -1.17237028 -0.16709502
#> 4 0.000000 149 2.16714328 -0.25417286
#> 5 0.000000 149 -1.20356079 2.59092514
#> 6 0.000000 149 -0.30990966 0.08074482
#> 7 0.020347 104 -0.04639998 -0.78998535
#> t.reference_AF_eur.avg t.reference_AF_iam.avg t.reference_AF_sas.avg
#> 1 -1.1638315 1.7969415 0.1184779
#> 2 0.2251513 0.7049114 -1.5471141
#> 3 0.1344480 -2.9091082 2.0429083
#> 4 0.2037762 -0.1515260 -1.4744624
#> 5 -0.2099120 0.4182795 -1.0374927
#> 6 2.7308539 -1.7056958 -1.3479503
#> 7 -1.0668582 0.4557509 1.0478613
#> p.reference_AF_afr p.reference_AF_eas p.reference_AF_eur p.reference_AF_iam
#> 1 1.33206678 1.43213645 1.753659817 0.07435691
#> 2 0.23820394 1.73597350 0.822170054 0.48196583
#> 3 1.75708055 1.13247816 0.893229762 1.99581985
#> 4 0.03181271 1.20028756 0.838806442 1.12023427
#> 5 1.76933192 0.01052294 1.165976886 0.67634501
#> 6 1.24293732 0.93575325 0.007079613 1.90985156
#> 7 1.03691957 1.56866612 1.711493802 0.64951928
#> p.reference_AF_sas
#> 1 0.90584749
#> 2 1.87604292
#> 3 0.04282398
#> 4 1.85753312
#> 5 1.69881293
#> 6 1.82027985
#> 7 0.29713165
Below is an example of plotting the reference group proportions
estimated in each block using summix_local(); where asterisks indicate
local substructure blocks that are at least nominally significant
(p-value\<=.05).
hendriau/Summix documentation built on April 25, 2024, 2:41 p.m.
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Summix2
Summix2 is a suite of methods that detect and leverage substructure in genetic summary data. This package builds on Summix, a method that estimates and adjusts for substructure in genetic summary that was developed by the Hendricks Research Team at the University of Colorado Denver.
Find more details about Summix in our manuscript published in the American Journal of Human Genetics.
For individual function specifics in Summix2:
summix — fast forward to example
adjAF — fast forward to example
summix_local — fast forward to example
Package Installation
To install this package, start R (version “4.3”) and run the following commands:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Summix")
summix
The summix() function estimates mixture proportions of reference groups within genetic summary (allele frequency) data using sequential quadratic programming performed with the slsqp() function in the nloptr package.
summix() Input
Mandatory parameters are:
-
data: A data frame of the observed and reference group allele frequencies for N genetic variants.
-
reference: A character vector of the column names for K reference groups.
-
observed: A character value that is the column name for the observed group.
Optional parameters are:
-
pi.start: Numeric vector of length K containing the user’s initial guess for the reference group proportions. If not specified, this defaults to 1/K where K is the number of reference groups.
-
goodness.of.fit: Default value is TRUE. If set as FALSE, the user will override the default goodness of fit measure and return the raw objective loss from slsqp().
-
override_removeSmallRef: Default value is FALSE. If set as TRUE, the user will override the automatic removal of reference groups with \<1% global proportions - this is not recommended.
-
network: Default value is FALSE. If set as TRUE, function will return a network diagram with nodes as estimated substructure proportions and edges as degree of similarity between the given node pair.
-
N_reference: numeric vector of the sample sizes for each of the K reference groups; must be specified if network = “TRUE”.
-
reference_colors: A character vector of length K that specifies the color each reference group node in the network plot. If not specified, this defaults to K random colors.
summix() Output
A data frame with the following columns:
-
goodness.of.fit: Scaled objective loss from slsqp() reflecting the fit of the reference data. Values between 0.5-1.5 are considered moderate fit and should be used with caution. Values greater than 1.5 indicate poor fit, and users should not perform further analyses using Summix2.
-
iterations: The number of iterations for the SLSQP algorithm before best-fit reference group proportion estimates are found.
-
time: The time in seconds before best-fit reference group mixture proportion estimations are found by the SLSQP algorithm.
-
filtered: The number of genetic variants not used in the reference group mixture proportion estimation due to missing values.
-
K columns of mixture proportions of reference groups input into the function.
Additional Output if summix_network = TRUE:
- Summix Network: A network diagram with nodes as estimated substructure proportions and edges as degree of similarity between the given node pair; exportable as a png.
adjAF
The adjAF() function adjusts allele frequencies to match reference group substructure mixture proportions in a given target group or individual.
adjAF() Input
Mandatory parameters are:
-
data: A data frame containing the unadjusted allele frequency for the observed group and K reference group allele frequencies for N genetic variants.
-
reference: A character vector of the column names for K reference groups.
-
observed: A character value that is the column name for the observed group.
-
pi.target: A numeric vector of the mixture proportions for K reference groups in the target group or individual.
-
pi.observed: A numeric vector of the mixture proportions for K reference groups in the observed group.
-
N_reference: A numeric vector of the sample sizes for each of the K reference groups that is in the same order as the reference parameter.
-
N_observed: A numeric value of the sample size of the observed group.
-
adj_method: User choice of method for the allele frequency adjustment; options “average” and “leave_one_out” are available. Defaults to “average”.
-
filter: Sets adjusted allele frequencies equal to 1 if > 1, to 0 if > -.005 and \< 0, and removes adjusted allele frequencies \< -.005. Default is TRUE.
adjAF() Output
A data frame with the following columns:
-
pi: A table of input reference groups, pi.observed, and pi.target.
-
observed.data: The name of the data column for the observed group from which the adjusted allele frequencies are estimated.
-
Nsnps: The number of SNPs for which adjusted AF is estimated.
-
adjusted.AF: A data frame of original data with an appended column of adjusted allele frequencies.
-
effective.sample.size: The sample size of individuals effectively represented by the adjusted allele frequencies.
summix_local
The summix_local() function estimates local substructure mixture proportions in genetic summary data using the same slspq() functionality as summix(). summix_local() also performs a selection scan (optional) that identifies regions of selection along the given chromosome.
summix_local() Input
Mandatory parameters are:
-
data: A data frame of the observed group and reference group allele frequencies for N genetic variants on a single chromosome. Must contain a column specifying the genetic variant positions.
-
reference: A character vector of the column names for K reference groups.
-
observed: A character value that is the column name for the observed group.
-
position_col: A character value that is the column name for the genetic variants positions. Default is “POS”.
-
maxStepSize: A numeric value that defines the maximum gap in base pairs between two consecutive genetic variants within a given window. Default is 1000.
Optional parameters are:
-
algorithm: User choice of algorithm to define local substructure blocks; options “fastcatch” and “windows” are available. “windows” uses a fixed window in a sliding windows algorithm. “fastcatch” allows dynamic window sizes. The “fastcatch” algorithm is recommended- though it is computationally slower. Default is “fastcatch”.
-
type: User choice of how to define window size; options “variants” and “bp” are available where “variants” defines window size as the number of variants in a given window and “bp” defines window size as the number of base pairs in a given window. Default is “variants”.
-
override_fit: Default is FALSE. If set as TRUE, the user will override the auto-stop of summix_local() that occurs if the global goodness of fit value is greater than 1.5 (indicating a poor fit of the reference data to the observed data).
-
override_removeSmallAnc: Default is FALSE. If set as TRUE, the user will override the automatic removal of reference ancestries with \<2% global proportions – this is not recommended.
-
selection_scan: User option to perform a selection scan on the given chromosome. Default is FALSE. If set as TRUE, a test statistic will be calculated for each local substructure block. Note: the user can expect extended computation time if this option is set as TRUE.
Conditional parameters are:
If algorithm = “windows”:
- windowOverlap: A numeric value that defines the number of variants or the number of base pairs that overlap between the given sliding windows. Default is 200.
If algorithm = “fastcatch”:
- diffThreshold: A numeric value that defines the percent difference threshold to mark the end of a local substructure block. Default is 0.02.
If type = “variants”:
- maxVariants: A numeric value that specifies the maximum number of genetic variants allowed to define a given window.
If type = “bp”:
- maxWindowSize: A numeric value that defines the maximum allowed window size by the number of base pairs in a given window.
If algorithm = “fastcatch” and type = “variants”:
- minVariants: A numeric value that specifies the minimum number of genetic variants allowed to define a given window.
If algorithm = “fastcatch” and type = “bp”:
- minWindowSize: A numeric value that specifies the minimum number of base pairs allowed to define a given window.
If selection_scan = TRUE:
- NSimRef: A numeric vector of the sample sizes for each of the K reference groups that is in the same order as the reference parameter. This is used in a simulation framework that calculates within local block substructure standard error.
summix_local() Output
A data frame with a row for each local substructure block and the following columns:
-
goodness.of.fit: Scaled objective loss from slsqp() reflecting the fit of the reference data. Values between 0.5-1.5 are considered moderate fit and should be used with caution. Values greater than 1.5 indicate poor fit, and users should not perform further analyses using Summix2.
-
iterations: The number of iterations for the SLSQP algorithm before best-fit reference group mixture proportion estimations are found.
-
time: The time in seconds before best-fit reference group mixture proportion estimations are found by the SLSQP algorithm.
-
filtered: The number of genetic variants not used in the reference group mixture proportion estimation due to missing values.
-
K columns of mixture proportions of reference group in the given local substructure block.
-
nSNPs: The number of genetic variants in the given local substructure block.
Additional Output if selection_scan = TRUE:
-
K columns of local substructure test statistics for each reference group in the given local substructure block.
-
K columns of p-values for each reference group in the given local substructure block. P-values calculated using the Student’s t-distribution with degrees of freedom=(nSNPs in the block)-1.
Examples using toy data in the Summix package
For quick runs of all demos, we suggest using the data saved within the Summix library called ancestryData.
A quick demo of summix()
The commands:
library(Summix)
# load the data
data("ancestryData")
# Estimate 5 reference group proportion values for the gnomAD African/African American group
# using a starting guess of .2 for each estimated proportion.
summix(data = ancestryData,
reference=c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
observed="gnomad_AF_afr",
pi.start = c(.2, .2, .2, .2, .2),
goodness.of.fit=TRUE)
#> goodness.of.fit iterations time filtered reference_AF_afr
#> 1 0.4853597 20 0.400059 secs 0 0.812142
#> reference_AF_eur reference_AF_iam
#> 1 0.169953 0.017905
Below is an example of creating a Summix Network plot.
Summix_output<- summix(data = ancestryData,
reference=c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
observed="gnomad_AF_afr",
pi.start = c(.2, .2, .2, .2, .2),
goodness.of.fit=TRUE,
network = TRUE,
N_reference = c(704, 787, 741, 47, 545), reference_colors = c("#FDE725FF", "#5DC863FF", "#21908CFF", "#3B528BFF", "#440154FF"))
Summix_Network <- Summix_output[[2]]
Summix_Network
A quick demo of adjAF()
The commands:
library(Summix)
# load the data
data("ancestryData")
adjusted_data<-adjAF(data = ancestryData,
reference = c("reference_AF_afr", "reference_AF_eur"),
observed = "gnomad_AF_afr",
pi.target = c(1, 0),
pi.observed = c(.85, .15),
adj_method = 'average',
N_reference = c(704,741),
N_observed = 20744,
filter = TRUE)
#>
#>
#> [1] "Note: In this AF adjustment, 0 SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. 0 SNPs (with adjusted AF > 1) were rounded to 1, and 0 SNPs (with adjusted AF <= -.005) were removed from the final results."
#>
#> [1] $pi
#> ref.group pi.observed pi.target
#> 1 reference_AF_afr 0.85 1
#> 2 reference_AF_eur 0.15 0
#>
#> [1] $observed.data
#> [1] "observed AF data to update: 'gnomad_AF_afr'"
#>
#> [1] $Nsnps
#> [1] 1000
#>
#>
#> [1] $effective.sample.size
#> [1] 18336
#>
#>
#> [1] "use $adjusted.AF$adjustedAF to see adjusted AF data"
#>
#>
#> [1] "Note: The accuracy of the AF adjustment is likely lower for rare variants (< .5%)."
print(adjusted_data$adjusted.AF[1:5,])
#> POS REF ALT CHROM reference_AF_afr reference_AF_eas reference_AF_eur
#> 1 31652001 T A chr22 0.040925268 0 0.000000000
#> 2 34509945 C G chr22 0.217971527 0 0.000000000
#> 3 34636589 CAA C chr22 0.181117576 0 0.001149425
#> 4 38889885 A AAG chr22 0.007117446 0 0.000000000
#> 5 49160931 G T chr22 0.064056997 0 0.000000000
#> reference_AF_iam reference_AF_sas gnomad_AF_afr adjustedAF
#> 1 0 0 0.04171490 0.045000811
#> 2 0 0 0.18774500 0.219423999
#> 3 0 0 0.15198300 0.179859133
#> 4 0 0 0.00422064 0.006041453
#> 5 0 0 0.05445710 0.064062087
A quick demo of summix_local()
The commands:
library(Summix)
# load the data
data("ancestryData")
results <- summix_local(data = ancestryData,
reference = c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
NSimRef = c(704,787,741,47,545),
observed="gnomad_AF_afr",
goodness.of.fit = T,
type = "variants",
algorithm = "fastcatch",
minVariants = 150,
maxVariants = 250,
maxStepSize = 1000,
diffThreshold = .02,
override_fit = F,
override_removeSmallAnc = TRUE,
selection_scan = T,
position_col = "POS")
#> [1] "Done getting LA proportions"
#> [1] "Running internal simulations for SE"
#> Time difference of 14.6037 mins
#> [1] "Discovered 7 LA blocks"
print(results$results)
#> Start_Pos End_Pos goodness.of.fit iterations time filtered
#> 1 10595784 19258643 1.2555376 10 0.07431006 0
#> 2 19258643 25252606 0.5018649 13 0.06914687 0
#> 3 25252606 30743600 0.2304807 11 0.09435987 0
#> 4 30743600 35846592 0.2933341 14 0.06985497 0
#> 5 35846592 42706228 0.5480859 14 0.08147001 0
#> 6 42706228 47902876 0.2634092 11 0.07047892 0
#> 7 47902876 50791970 0.2891929 10 0.07448006 0
#> reference_AF_afr reference_AF_eas reference_AF_eur reference_AF_iam
#> 1 0.809208 0.000000 0.146185 0.034417
#> 2 0.816933 0.000000 0.161511 0.021556
#> 3 0.805795 0.002730 0.160926 0.000000
#> 4 0.820812 0.002558 0.161353 0.015276
#> 5 0.806428 0.016357 0.157855 0.019360
#> 6 0.810130 0.004046 0.181798 0.004025
#> 7 0.811265 0.000000 0.148492 0.019896
#> reference_AF_sas nSNPs t.reference_AF_afr.avg t.reference_AF_eas.avg
#> 1 0.010189 150 -0.42983402 -0.57246570
#> 2 0.000000 149 1.18423949 -1.12115283
#> 3 0.030550 149 -1.17237028 -0.16709502
#> 4 0.000000 149 2.16714328 -0.25417286
#> 5 0.000000 149 -1.20356079 2.59092514
#> 6 0.000000 149 -0.30990966 0.08074482
#> 7 0.020347 104 -0.04639998 -0.78998535
#> t.reference_AF_eur.avg t.reference_AF_iam.avg t.reference_AF_sas.avg
#> 1 -1.1638315 1.7969415 0.1184779
#> 2 0.2251513 0.7049114 -1.5471141
#> 3 0.1344480 -2.9091082 2.0429083
#> 4 0.2037762 -0.1515260 -1.4744624
#> 5 -0.2099120 0.4182795 -1.0374927
#> 6 2.7308539 -1.7056958 -1.3479503
#> 7 -1.0668582 0.4557509 1.0478613
#> p.reference_AF_afr p.reference_AF_eas p.reference_AF_eur p.reference_AF_iam
#> 1 1.33206678 1.43213645 1.753659817 0.07435691
#> 2 0.23820394 1.73597350 0.822170054 0.48196583
#> 3 1.75708055 1.13247816 0.893229762 1.99581985
#> 4 0.03181271 1.20028756 0.838806442 1.12023427
#> 5 1.76933192 0.01052294 1.165976886 0.67634501
#> 6 1.24293732 0.93575325 0.007079613 1.90985156
#> 7 1.03691957 1.56866612 1.711493802 0.64951928
#> p.reference_AF_sas
#> 1 0.90584749
#> 2 1.87604292
#> 3 0.04282398
#> 4 1.85753312
#> 5 1.69881293
#> 6 1.82027985
#> 7 0.29713165
Below is an example of plotting the reference group proportions estimated in each block using summix_local(); where asterisks indicate local substructure blocks that are at least nominally significant (p-value\<=.05).
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