OutOfAfrica_3G09
Joseph Marcus
2020-05-12
Last updated: 2020-05-13
Checks: 7 0
Knit directory: drift-workflow/analysis/
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| Rmd | 9df8cdd | Joseph Marcus | 2020-05-12 | wflow_publish(“OutOfAfrica_3G09.Rmd”) |
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| Rmd | 1247ad0 | Joseph Marcus | 2020-05-12 | wflow_publish(“OutOfAfrica_3G09.Rmd”) |
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| Rmd | f62c96c | Joseph Marcus | 2020-05-12 | wflow_publish(“OutOfAfrica_3G09.Rmd”) |
| Rmd | d075c79 | Joseph Marcus | 2020-04-30 | starting drift analysis on hoa global data |
| Rmd | 642f381 | Joseph Marcus | 2020-04-27 | switched from rpy2 to using Rscripts in snkmk |
Here I visualize population structure with simulated data from the OutOfAfrica_3G09 scenario. See Figure 2. from Gutenkunst et al. 2009.
Imports
Import the required libraries and scripts:
suppressMessages({
library(lfa)
library(flashier)
library(drift.alpha)
library(ggplot2)
library(reshape2)
library(tidyverse)
library(alstructure)
})Data
data_path <- "../output/simulations/OutOfAfrica_3G09/rep1.txt"
Y <- t(as.matrix(read.table(data_path, sep=" ")))
n <- nrow(Y)
maf <- colSums(Y) / (2 * n)
# filter out too rare and too common SNPs
Y <- Y[,((maf>=.05) & (maf <=.95))]
p <- ncol(Y)
Z <- scale(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.
PCA
Lets run PCA on the centered and scaled genotype matrix:
svd_res <- lfa:::trunc.svd(Z, 5)
L_hat <- svd_res$u
plot_loadings(L_hat, labs)
| Version | Author | Date |
|---|---|---|
| da3995d | Joseph Marcus | 2020-05-12 |
Plot the first two factors against each other:
qplot(L_hat[,1], L_hat[,2], color=labs) +
xlab("PC1") +
ylab("PC2") +
theme_bw()
| Version | Author | Date |
|---|---|---|
| da3995d | Joseph Marcus | 2020-05-12 |
We certainly detect “clustered” population structure in the top PCs where PC1 represents Out of Africa and PC2 represents the next split between CEU and HAN.
ALStructure
Run ALStructure with \(K=3\):
admix_res <- alstructure::alstructure(t(Y), d_hat=3)
Qhat <- t(admix_res$Q_hat)
plot_loadings(Qhat, labs)
| Version | Author | Date |
|---|---|---|
| da3995d | Joseph Marcus | 2020-05-12 |
the PSD model assigns three distinct clusters.
flash [greedy]
Run the greedy algorithm:
fl <- flash(Y,
greedy.Kmax=5,
prior.family=c(prior.bimodal(), prior.normal()))Adding factor 1 to flash object...
Adding factor 2 to flash object...
Adding factor 3 to flash object...
Adding factor 4 to flash object...
Adding factor 5 to flash object...
Factor doesn't significantly increase objective and won't be added.
Wrapping up...
Done.
Nullchecking 4 factors...
Done.plot_loadings(fl$flash.fit$EF[[1]], labs)
| Version | Author | Date |
|---|---|---|
| da3995d | Joseph Marcus | 2020-05-12 |
flash learns a shared factor between all the populations but where YRI has a lower loading then CEU and HAN and then it also learns population specific factors.
flash [backfit]
Run flash [backfit] initializing from the greedy solution:
flbf <- fl %>%
flash.backfit() %>%
flash.nullcheck(remove=TRUE)Backfitting 4 factors (tolerance: 5.33e-02)...
Difference between iterations is within 1.0e+02...
Difference between iterations is within 1.0e+01...
Difference between iterations is within 1.0e+00...
Difference between iterations is within 1.0e-01...
Wrapping up...
Done.
Nullchecking 4 factors...
Wrapping up...
Done.plot_loadings(flbf$flash.fit$EF[[1]], labs)
the backfitting algorithm represents the data with a sparser solution.
drift
Run drift initializing from the greedy solution:
init <- init_from_flash(fl)
dr <- drift(init, miniter=2, maxiter=500, tol=0.01, verbose=TRUE) 1 : -2865526.968
2 : -2864791.747
3 : -2864664.530
4 : -2864625.627
5 : -2864609.146
6 : -2864600.343
7 : -2864594.570
8 : -2864590.168
9 : -2864586.500
10 : -2864583.297
11 : -2864580.417
12 : -2864577.764
13 : -2864575.242
14 : -2864572.738
15 : -2864570.080
16 : -2864566.973
17 : -2864562.840
18 : -2864556.457
19 : -2864545.166
20 : -2864527.208
21 : -2864518.747
22 : -2864518.001
23 : -2864517.676
24 : -2864517.424
25 : -2864517.226
26 : -2864517.071
27 : -2864516.949
28 : -2864516.853
29 : -2864516.777
30 : -2864516.717
31 : -2864516.670
32 : -2864516.632
33 : -2864516.603
34 : -2864516.579
35 : -2864516.561
36 : -2864516.546
37 : -2864516.534
38 : -2864516.525 plot_loadings(dr$EL, labs)
| Version | Author | Date |
|---|---|---|
| da3995d | Joseph Marcus | 2020-05-12 |
drift seems to maintain the greedy solution.
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 ggplot2_3.3.0 drift.alpha_0.0.9
[13] 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_0.3-17 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 fs_1.3.1
[41] fansi_0.4.1 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