Last updated: 2019-12-13
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Knit directory: SMF/
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GTEx adipose and skin tissue data.
The structure stm finds is similar to the one by NMF since stm uses NMF fit as initialization.
stm + smash-gen gives smoother fit and clearer structures
Simply using Poisson smoothing seems to give very similar estimates to the ones from NMF. But using smash-gen can reveal some potential alternative splicing patterns.
True nugget effect is usually around \(\sigma=\) 0.04 to 0.07.
10 splicing variants, 6 exons
library(stm)
library(NNLM)
RPS13 = read.table('~/NMF/YangLi/Counts_11:17095938-17099220.txt.gz',header = TRUE)
dim(RPS13)
[1] 3282 1475
tissues = colnames(RPS13)
tissue = c()
for(i in 1:length(tissues)){
tissue = c(tissue, (strsplit(tissues[i],split = '_')[[1]])[1])
}
table(tissue)
tissue
chrom end GeuvadisLCL GTEXAdipose
1 1 88 226
GTEXSkinExposed start WholeBlood
231 1 927
# only use data from GTEx
idx = c(which(tissue=='GTEXAdipose'), which(tissue=='GTEXSkinExposed'))
Fit \(K=3\) topics.
Loss = mean KL divergence
method = lee’s multiplicative update
K=3
load('~/SMF/data/RPS13_NMF_mkl_lee_K3.RData')
lf = poisson2multinom(t(fit_NMF$H),fit_NMF$W)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
Initialize using above NMF fit, smooth F using BMSM.
load('~/SMF/data/RPS13_stm_bmsm_K3.RData')
lf = poisson2multinom(t(fit_stm$qf),fit_stm$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
Initialize using above NMF fit, smooth F using smash-gen
load('~/SMF/data/RPS13_stm_nugget_K3.RData')
lf = poisson2multinom(t(fit_stm_nugget$qf),fit_stm_nugget$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
abline(v = sep_lines, lwd = 2)
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
fit_stm_nugget$nugget
$nugget_l
[1] 0 0 0
$nugget_f
[1] 0.04586420 0.04107629 0.04936441
11 splicing variants, 6 exons
Loss = mean KL divergence
method = lee’s multiplicative update
K=3
load('~/SMF/data/GPX3_NMF_mkl_lee_K3.RData')
lf = poisson2multinom(t(fit_NMF$H),fit_NMF$W)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
Initialize using above NMF fit, smooth F using BMSM.
load('~/SMF/data/GPX3_stm_bmsm_K3.RData')
lf = poisson2multinom(t(fit_stm$qf),fit_stm$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
Initialize using above NMF fit, smooth F using smash-gen
load('~/SMF/data/GPX3_stm_nugget_K3.RData')
lf = poisson2multinom(t(fit_stm_nugget$qf),fit_stm_nugget$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
fit_stm_nugget$nugget
$nugget_l
[1] 0 0 0
$nugget_f
[1] 0.04060634 0.04691368 0.05454463
6 splicing variants, 15 exons
K=3
load('~/SMF/data/PSAP_NMF_mkl_lee_K3.RData')
lf = poisson2multinom(t(fit_NMF$H),fit_NMF$W)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
load('~/SMF/data/PSAP_stm_nugget_K3.RData')
lf = poisson2multinom(t(fit_stm_nugget$qf),fit_stm_nugget$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
Version | Author | Date |
---|---|---|
901e63e | DongyueXie | 2019-12-13 |
fit_stm_nugget$nugget
$nugget_l
[1] 0 0 0
$nugget_f
[1] 0.06671526 0.07175778 0.04846421
K=4
load('~/SMF/data/PSAP_NMF_mkl_lee_K4.RData')
lf = poisson2multinom(t(fit_NMF$H),fit_NMF$W)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
lines(lf$FF[,4],col=5)
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
load('~/SMF/data/PSAP_stm_nugget_K4.RData')
lf = poisson2multinom(t(fit_stm_nugget$qf),fit_stm_nugget$ql)
plot(lf$FF[,1],col=2,ylim=range(lf$FF),type='l',xlab = 'base',ylab='Intensity',main='Factors')
lines(lf$FF[,2],col=3)
lines(lf$FF[,3],col=4)
lines(lf$FF[,4],col=5)
barplot(t(lf$L),col=2:(K+1),axisnames = F, space = 0, border = NA, las = 1, ylim = c(0, 1), cex.axis = 1.5, cex.main = 1.4)
sep_lines = c(226)
sep_lines_mid = c(113,341)
tissue_name=c('Adipose','Skin')
axis(1, at = sep_lines_mid, labels = tissue_name, cex = 2, padj = -1, tick = FALSE)
mtext("tissue", 1, line = 2, cex = 1.2)
mtext("membership proportion", 2, line = 3, cex = 1.2)
abline(v = sep_lines, lwd = 2)
fit_stm_nugget$nugget
$nugget_l
[1] 0 0 0 0
$nugget_f
[1] 0.06999425 0.05084868 0.06090485 0.06112265
Other findings:
Initialize stm from random cannot find structures
Initialize from NMF with different choices of loss and algorithm give different structures.
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] NNLM_0.4.2 stm_1.0.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 compiler_3.5.1 later_0.7.5
[4] git2r_0.26.1 highr_0.7 workflowr_1.5.0
[7] bitops_1.0-6 iterators_1.0.10 tools_3.5.1
[10] digest_0.6.18 evaluate_0.12 lattice_0.20-38
[13] Matrix_1.2-15 foreach_1.4.4 yaml_2.2.0
[16] parallel_3.5.1 smashr_1.2-7 stringr_1.3.1
[19] knitr_1.20 caTools_1.17.1.1 fs_1.3.1
[22] gtools_3.8.1 rprojroot_1.3-2 grid_3.5.1
[25] data.table_1.12.0 glue_1.3.0 R6_2.3.0
[28] rmarkdown_1.10 mixsqp_0.2-2 ashr_2.2-39
[31] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[34] promises_1.0.1 codetools_0.2-15 htmltools_0.3.6
[37] MASS_7.3-51.1 httpuv_1.4.5 wavethresh_4.6.8
[40] stringi_1.2.4 doParallel_1.0.14 pscl_1.5.2
[43] truncnorm_1.0-8 SQUAREM_2017.10-1 ebpm_0.0.0.9004
[46] logitnorm_0.8.37