OutOfAfrica_3G09 [fixed shared factor]
Joseph Marcus
2020-05-18
Last updated: 2020-05-20
Checks: 7 0
Knit directory: drift-workflow/analysis/
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| Rmd | e4cdfd8 | Joseph Marcus | 2020-05-20 | wflow_publish(“OutOfAfrica_3G09_fix.Rmd”) |
| html | 1dde8eb | Joseph Marcus | 2020-05-20 | Build site. |
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| html | 5f94116 | Joseph Marcus | 2020-05-18 | Build site. |
| Rmd | 59c3de9 | Joseph Marcus | 2020-05-18 | wflow_publish(“OutOfAfrica_3G09_fix.Rmd”) |
| html | 484ce56 | Joseph Marcus | 2020-05-18 | Build site. |
| Rmd | 36c05dd | Joseph Marcus | 2020-05-18 | wflow_publish(“analysis/OutOfAfrica_3G09_fix.Rmd”) |
| Rmd | 865cef4 | Joseph Marcus | 2020-05-18 | added fixed shared factor |
| html | 865cef4 | Joseph Marcus | 2020-05-18 | added fixed shared factor |
Here I visualize population structure with simulated data from the OutOfAfrica_3G09 scenario. See Figure 2. from Gutenkunst et al. 2009.
Below, I show a number of EBMF solutions and in each of them I don’t display the first shared factor which is prefixed to the one-vector and scale the loadings by the prior variance. I only describe the loadings that remain after the shared factor.
Imports
Import the required libraries and scripts:
suppressMessages({
library(lfa)
library(flashier)
library(drift.alpha)
library(ggplot2)
library(RColorBrewer)
library(reshape2)
library(tidyverse)
library(alstructure)
source("../code/structure_plot.R")
})Data
data_path <- "../output/simulations/OutOfAfrica_3G09/rep1.txt"
G <- t(as.matrix(read.table(data_path, sep=" ")))
colnames(G) <- NULL
rownames(G) <- NULL
n <- nrow(G)
daf <- colSums(G) / (2 * n)
colors <- brewer.pal(8, "Set2")
# filter out too rare and too common SNPs
Y <- G[,((daf>=.05) & (daf <=.95))]
p <- ncol(Y)
print(n)[1] 120print(p)[1] 29815# sub-population labels from stdpop
labs <- rep(c("YRI", "CEU", "HAN"), each=40)we end up with 120 individuals and ~30000 SNPs. View fitted the sample covariance matrix:
plot_cov((1.0 / p) * Y %*% t(Y), as.is=T)
plot allele frequencies of Africa vs OOA populations:
daf_afr <- colSums(G[1:40,]) / (2*40)
daf_ooa <- colSums(G[41:120,]) / (2*80)
qplot(daf_ooa, daf_afr, alpha=.1)
hist(daf_afr)
hist(daf_ooa)
flash [greedy]
Run the greedy algorithm:
ones <- matrix(1, nrow = n, ncol = 1)
ls.soln <- t(solve(crossprod(ones), crossprod(ones, Y)))
fl <- flash.init(Y) %>%
flash.init.factors(EF = list(ones, ls.soln),
prior.family=c(prior.bimodal(), prior.normal())) %>%
flash.fix.loadings(kset = 1, mode = 1L) %>%
flash.backfit() %>%
flash.add.greedy(Kmax=6, prior.family=c(prior.bimodal(), prior.normal()))Backfitting 1 factors (tolerance: 5.33e-02)...
Difference between iterations is within 1.0e-01...
Wrapping up...
Done.
Adding factor 2 to flash object...
Adding factor 3 to flash object...
Adding factor 4 to flash object...
Adding factor 5 to flash object...
Factor doesn't significantly increase objective and won't be added.
Wrapping up...
Done.sd <- unlist(lapply(fl$fitted.g[[2]], '[[', 3))
L <- fl$flash.fit$EF[[1]]
LDsqrt <- L %*% diag(sd)
K <- ncol(LDsqrt)
plot_loadings(LDsqrt[,2:K], labs) + scale_color_brewer(palette="Set2")
view structure plot:
create_structure_plot(L=LDsqrt[,2:K], labels=labs, colors=colors)
view fitted covariance matrix:
plot_cov(LDsqrt %*% t(LDsqrt), as.is=T)
the greedy algorithm finds 3 population specific factors.
flash [backfit]
Run flash [backfit] initializing from the greedy solution:
flbf <- fl %>%
flash.backfit() %>%
flash.nullcheck(remove=TRUE)Backfitting 4 factors (tolerance: 5.33e-02)...
Difference between iterations is within 1.0e+02...
Difference between iterations is within 1.0e+01...
Difference between iterations is within 1.0e+00...
Difference between iterations is within 1.0e-01...
Difference between iterations is within 1.0e-02...
Wrapping up...
Done.
Nullchecking 4 factors...
Wrapping up...
Done.sd <- unlist(lapply(flbf$fitted.g[[2]], '[[', 3))
L <- flbf$flash.fit$EF[[1]]
LDsqrt <- L %*% diag(sd)
K <- ncol(LDsqrt)
plot_loadings(LDsqrt[,2:K], labs) + scale_color_brewer(palette="Set2")
view structure plot:
create_structure_plot(L=LDsqrt[,2:K], labels=labs, colors=colors)
view fitted covariance matrix:
plot_cov(LDsqrt %*% t(LDsqrt), as.is=T)
the backfitting algorithm represents the data with a sparser solution and finds a factor represented by YRI and a small loading from Han and
drift
Run drift initializing from the greedy solution:
init <- init_from_data(Y, Kmax=6)
dr <- drift(init, miniter=2,
maxiter=1000,
tol=0.01,
verbose=FALSE)
sd <- sqrt(dr$prior_s2)
L <- dr$EL
LDsqrt <- L %*% diag(sd)
K <- ncol(LDsqrt)
plot_loadings(LDsqrt[,2:K], labs) + scale_color_brewer(palette="Set2")
view structure plot:
create_structure_plot(L=LDsqrt[,2:K], labels=labs, colors=colors)
view fitted covariance matrix:
plot_cov(LDsqrt %*% t(LDsqrt), as.is=T)
the drift algorithm finds two population specific factors and a shared factor between HAN CEU. Also notably the population specific factor for Africa has lower total magnitude then for OOA populations which makes senses as the prior variance for OOA should be higher (i.e. more drift).
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] alstructure_0.1.0 forcats_0.5.0 stringr_1.4.0
[4] dplyr_0.8.5 purrr_0.3.4 readr_1.3.1
[7] tidyr_1.0.2 tibble_3.0.1 tidyverse_1.3.0
[10] reshape2_1.4.3 RColorBrewer_1.1-2 ggplot2_3.3.0
[13] drift.alpha_0.0.9 flashier_0.2.4 lfa_1.9.0
loaded via a namespace (and not attached):
[1] httr_1.4.1 jsonlite_1.6 modelr_0.1.6 assertthat_0.2.1
[5] mixsqp_0.3-43 cellranger_1.1.0 yaml_2.2.0 ebnm_0.1-24
[9] pillar_1.4.3 backports_1.1.6 lattice_0.20-38 glue_1.4.0
[13] digest_0.6.25 promises_1.0.1 rvest_0.3.5 colorspace_1.4-1
[17] htmltools_0.3.6 httpuv_1.4.5 Matrix_1.2-15 plyr_1.8.4
[21] pkgconfig_2.0.3 invgamma_1.1 broom_0.5.6 haven_2.2.0
[25] corpcor_1.6.9 scales_1.1.0 whisker_0.3-2 later_0.7.5
[29] git2r_0.26.1 farver_2.0.3 generics_0.0.2 ellipsis_0.3.0
[33] withr_2.2.0 ashr_2.2-50 cli_2.0.2 magrittr_1.5
[37] crayon_1.3.4 readxl_1.3.1 evaluate_0.14 fansi_0.4.1
[41] fs_1.3.1 nlme_3.1-137 xml2_1.3.2 truncnorm_1.0-8
[45] tools_3.5.1 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[49] reprex_0.3.0 irlba_2.3.3 compiler_3.5.1 rlang_0.4.5
[53] grid_3.5.1 rstudioapi_0.11 labeling_0.3 rmarkdown_1.10
[57] gtable_0.3.0 DBI_1.0.0 R6_2.4.1 lubridate_1.7.4
[61] knitr_1.20 workflowr_1.6.1 rprojroot_1.3-2 stringi_1.4.6
[65] parallel_3.5.1 SQUAREM_2020.2 Rcpp_1.0.4.6 vctrs_0.2.4
[69] dbplyr_1.4.3 tidyselect_1.0.0