OutOfAfrica_3G09 [fixed shared factor]
Joseph Marcus
2020-05-18
Last updated: 2020-06-01
Checks: 7 0
Knit directory: drift-workflow/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5b4cd63 | Joseph Marcus | 2020-06-01 | updated to larger sample size for ooa fixed sim |
| html | cb9009e | Joseph Marcus | 2020-05-21 | Build site. |
| Rmd | 3549c6d | Joseph Marcus | 2020-05-21 | wflow_publish(“index.Rmd”) |
| html | f66648a | Joseph Marcus | 2020-05-20 | Build site. |
| Rmd | e4cdfd8 | Joseph Marcus | 2020-05-20 | wflow_publish(“OutOfAfrica_3G09_fix.Rmd”) |
| html | 1dde8eb | Joseph Marcus | 2020-05-20 | Build site. |
| Rmd | e42d06d | Joseph Marcus | 2020-05-20 | wflow_publish(“OutOfAfrica_3G09_fix.Rmd”) |
| 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] 300print(p)[1] 21492# sub-population labels from stdpop
labs <- rep(c("YRI", "CEU", "HAN"), each=100)we end up with 300 individuals and ~20000 SNPs. View fitted the sample covariance matrix:
plot_cov((1.0 / p) * Y %*% t(Y), as.is=T) +
scale_fill_viridis_c() +
labs(fill="Cov")Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
plot allele frequencies of Africa vs OOA populations:
daf_afr <- colSums(G[1:100,]) / (2*100)
daf_ooa <- colSums(G[101:300,]) / (2*200)
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: 9.61e-02)...
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$EF[[1]]
s2 <- fl$residuals.sd^2
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) + s2*diag(n), as.is=T) +
scale_fill_viridis_c() +
labs(fill="Cov") Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
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: 9.61e-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...
Wrapping up...
Done.
Nullchecking 4 factors...
Factor 2 removed, increasing objective by 5.814e-05.
Wrapping up...
Done.sd <- unlist(lapply(flbf$fitted.g[[2]], '[[', 3))
s2 <- flbf$residuals.sd^2
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) + s2*diag(n), as.is=T) +
scale_fill_viridis_c() +
labs(fill="Cov")Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
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=1e-5,
verbose=FALSE)
sd <- sqrt(dr$prior_s2)
L <- dr$EL
LDsqrt <- L %*% diag(sd)
s2 <- dr$resid_s2
K <- ncol(LDsqrt)
plot_loadings(LDsqrt[,2:K], labs) + scale_color_brewer(palette="Set2")
view structure plot:
create_structure_plot(L=LDsqrt[,2:K], labels=labs, colors=colors)
view fitted covariance matrix:
plot_cov(LDsqrt %*% t(LDsqrt) + s2*diag(n), as.is=T) +
scale_fill_viridis_c() +
labs(fill="Cov")Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
the drift algorithm converges quickly and maintains the greedy solution. Lets try adding a factor:
dr2 <- drift(add_factor(dr),
miniter=2,
maxiter=1000,
tol=1e-5,
verbose=FALSE)
sd <- sqrt(dr2$prior_s2)
L <- dr2$EL
LDsqrt <- L %*% diag(sd)
s2 <- dr2$resid_s2
K <- ncol(LDsqrt)
plot_loadings(LDsqrt[,2:K], labs) + scale_color_brewer(palette="Set2")
The additional factor does seem to capture much interesting structure.
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 viridisLite_0.3.0 jsonlite_1.6
[4] modelr_0.1.6 assertthat_0.2.1 mixsqp_0.3-43
[7] cellranger_1.1.0 yaml_2.2.0 ebnm_0.1-24
[10] pillar_1.4.3 backports_1.1.6 lattice_0.20-38
[13] glue_1.4.0 digest_0.6.25 promises_1.0.1
[16] rvest_0.3.5 colorspace_1.4-1 htmltools_0.3.6
[19] httpuv_1.4.5 Matrix_1.2-15 plyr_1.8.4
[22] pkgconfig_2.0.3 invgamma_1.1 broom_0.5.6
[25] haven_2.2.0 corpcor_1.6.9 scales_1.1.0
[28] whisker_0.3-2 later_0.7.5 git2r_0.26.1
[31] farver_2.0.3 generics_0.0.2 ellipsis_0.3.0
[34] withr_2.2.0 ashr_2.2-50 cli_2.0.2
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[40] evaluate_0.14 fansi_0.4.1 fs_1.3.1
[43] nlme_3.1-137 xml2_1.3.2 truncnorm_1.0-8
[46] tools_3.5.1 hms_0.5.3 lifecycle_0.2.0
[49] munsell_0.5.0 reprex_0.3.0 irlba_2.3.3
[52] compiler_3.5.1 rlang_0.4.5 grid_3.5.1
[55] rstudioapi_0.11 labeling_0.3 rmarkdown_1.10
[58] gtable_0.3.0 DBI_1.0.0 R6_2.4.1
[61] lubridate_1.7.4 knitr_1.20 workflowr_1.6.1
[64] rprojroot_1.3-2 stringi_1.4.6 parallel_3.5.1
[67] SQUAREM_2020.2 Rcpp_1.0.4.6 vctrs_0.2.4
[70] dbplyr_1.4.3 tidyselect_1.0.0