Low-Dimensional Covariance Regression Applied to HCP Data
Christof Seiler
2017-06-22
Goal
Apply low-dimensional covariance regression model to preprocessed timeseries data from the Human Connectome Project.
library(CovRegFC)## Loading required package: Rcpp## Warning: replacing previous import 'cowplot::ggsave' by 'ggplot2::ggsave'
## when loading 'CovRegFC'library(cowplot)## Loading required package: ggplot2##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggplot2':
##
## ggsavelibrary(ggplot2)
library(reshape2)
library(magrittr)
library(stringr)
library(devtools)
library(rstan)## Loading required package: StanHeaders## rstan (Version 2.15.1, packaged: 2017-04-19 05:03:57 UTC, GitRev: 2e1f913d3ca3)## For execution on a local, multicore CPU with excess RAM we recommend calling
## rstan_options(auto_write = TRUE)
## options(mc.cores = parallel::detectCores())##
## Attaching package: 'rstan'## The following object is masked from 'package:magrittr':
##
## extractlibrary(abind)
library(png)
library(cowplot)
library(BatchJobs)## Loading required package: BBmisc## Sourced 1 configuration files:## 1: /Users/cseiler/Code/RLibraries/BatchJobs/etc/BatchJobs_global_config.R## BatchJobs configuration:
## cluster functions: Interactive
## mail.from:
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## default.resources:
## debug: FALSE
## raise.warnings: FALSE
## staged.queries: TRUE
## max.concurrent.jobs: Inf
## fs.timeout: NA
## measure.mem: TRUElibrary(coda)##
## Attaching package: 'coda'## The following object is masked from 'package:rstan':
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## traceplotparams## $num_regions
## [1] "15"
##
## $num_subjects
## [1] "80"
##
## $tp_per_subject
## [1] "Auto"
##
## $num_samples
## [1] "1"Load HCP Data
Analysis based on data from the WU-Minn HCP 1200 Subjects Data Release with four complete rfMRI runs (with 100% of collected time points). Load subject information and behavioral measurements.
subject_info = read.csv("HCP_PTN820/sample_info.csv")
subject_info = subject_info[,c(
"Subject",
"Age",
"Gender",
"Acquisition",
"PSQI_AmtSleep", # amount of sleep in hours
"PSQI_Score" # Pittsburgh Sleep Quality Index (PSQI) Completed
)] Classification of sleep duration from Hirshkowitz et al. (2015)1:
- short sleepers: average equal or less than 6 hours each night
- conventional sleepers: average between 7 and 9 hours each night
sleep_duration = rep("undefined",nrow(subject_info))
sleep_duration[subject_info$PSQI_AmtSleep <= 6] = "short"
sleep_duration[(subject_info$PSQI_AmtSleep >= 7) & (subject_info$PSQI_AmtSleep <= 9)] = "conventional"
subject_info$sleep_duration = factor(sleep_duration)
table(subject_info$sleep_duration)##
## conventional short undefined
## 710 366 130str(subject_info)## 'data.frame': 1206 obs. of 7 variables:
## $ Subject : int 100004 100206 100307 100408 100610 101006 101107 101208 101309 101410 ...
## $ Age : Factor w/ 4 levels "22-25","26-30",..: 1 2 2 3 2 3 1 3 2 2 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 2 1 2 2 1 2 1 2 2 ...
## $ Acquisition : Factor w/ 13 levels "Q01","Q02","Q03",..: 6 11 1 3 8 6 6 12 6 6 ...
## $ PSQI_AmtSleep : num 5 6 6.5 8 7 7 4 7 5.5 5.5 ...
## $ PSQI_Score : int 8 6 4 4 4 2 9 6 5 8 ...
## $ sleep_duration: Factor w/ 3 levels "conventional",..: 2 2 3 1 1 1 2 1 2 2 ...Load timeseries.
num_regions = as.integer(params$num_regions)
num_regions## [1] 15channel_names = paste0("R",1:num_regions)
path = paste0("HCP_PTN820/node_timeseries/3T_HCP820_MSMAll_d",num_regions,"_ts2")
file_names = list.files(path = path,pattern = ".txt")
file_subject_ids = strsplit(file_names,split = ".txt") %>% unlist
ts = lapply(file_subject_ids,function(Subject) {
print(paste("reading subject:",Subject))
full_path = paste0(path,"/",Subject,".txt")
timeseries = read.csv(full_path,header = FALSE,sep = " ")
timeseries$Subject = Subject
timeseries
}) %>% do.call(rbind,.) %>% data.frame## [1] "reading subject: 100206"
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## [1] "reading subject: 996782"names(ts)[1:num_regions] = channel_namesMerge timeseries and subject info into one data frame.
ts_subject_info = merge(ts,subject_info,by = "Subject")Plot timeseries for one subject.
subject_names = names(table(ts_subject_info$Subject))
timeseries = subset(ts_subject_info,Subject == subject_names[1])
timeseries$timepoint = rep(1:(nrow(timeseries)/4),4)
timeseries$run = lapply(paste0("run_",1:4),function(i) rep(i,nrow(timeseries)/4)) %>% unlist
timeseries_long = melt(timeseries,id.vars = c("timepoint","run",names(subject_info)))
ggplot(timeseries_long,aes(x = timepoint,y = value,color = variable)) +
geom_line() + facet_wrap(~run)
timeseries_long_subset = subset(timeseries_long,
timepoint < 51 & (variable == "R1" | variable == "R2" | variable == "R3"))
ggplot(timeseries_long_subset,aes(x = timepoint,y = value,color = variable)) +
geom_line() + facet_wrap(~run)
What is the effective samples size computed using autocorrelation function.
run = cut_number(1:nrow(timeseries), 4, labels=FALSE)
ess = lapply(subject_names,function(subject_name) {
sapply(1:4,function(i) {
timeseries = subset(ts_subject_info,Subject == subject_name)
Y = timeseries[run==i,channel_names]
effectiveSize(Y) %>% min
}) %>% sum
}) %>% unlist
df = data.frame(subject_id=1:length(ess),ess)
ggplot(df,aes(ess)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
tp_per_subject = round(min(df$ess))
tp_per_subject## [1] 167Subsample time points within each subject to account for dependencies.
#tp_per_subject = as.integer(params$tp_per_subject)
#tp_per_subject
ts_subject_info_subset = subset(ts_subject_info,
sleep_duration == "short" | sleep_duration == "conventional") %>% droplevels
subject_names = names(table(ts_subject_info_subset$Subject))
subsample_ids = lapply(subject_names,function(subject_name) {
ids = which(ts_subject_info_subset$Subject == subject_name)
seq(from = min(ids),to = max(ids),length.out = tp_per_subject) %>% floor
}) %>% unlist
ts_subject_info_subset = ts_subject_info_subset[subsample_ids,] %>% droplevels
str(ts_subject_info_subset)## 'data.frame': 121910 obs. of 22 variables:
## $ Subject : chr "100206" "100206" "100206" "100206" ...
## $ R1 : num 53.1 26.6 47.1 48.7 52.9 ...
## $ R2 : num -45.7 110 -16.2 -28.1 41.6 ...
## $ R3 : num 15.8 16.8 -153 58.5 24.2 ...
## $ R4 : num 74 -20.8 -39.7 50.5 90.3 ...
## $ R5 : num 9.81 53.45 -19.24 -46.09 -92.13 ...
## $ R6 : num 15.8 57.7 -36.5 -50.4 -87.1 ...
## $ R7 : num 6.83 -127.34 26.82 -81.19 -44.93 ...
## $ R8 : num 44.6 -18.9 -36.7 -60.7 62.6 ...
## $ R9 : num -44.7 -33.3 -45.4 156 12 ...
## $ R10 : num -41 35.7 42.3 -57.8 -1.2 ...
## $ R11 : num 3.58 -38.95 23.69 47.86 17.75 ...
## $ R12 : num 12.91 -20.89 59.28 -64.38 7.05 ...
## $ R13 : num -95.77 -70.64 -8.49 -3.9 62.22 ...
## $ R14 : num 13.12 8.83 -30.12 60.29 -43.07 ...
## $ R15 : num -76.9 30.1 -34.1 33.5 -36.3 ...
## $ Age : Factor w/ 4 levels "22-25","26-30",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ Acquisition : Factor w/ 12 levels "Q01","Q02","Q03",..: 11 11 11 11 11 11 11 11 11 11 ...
## $ PSQI_AmtSleep : num 6 6 6 6 6 6 6 6 6 6 ...
## $ PSQI_Score : int 6 6 6 6 6 6 6 6 6 6 ...
## $ sleep_duration: Factor w/ 2 levels "conventional",..: 2 2 2 2 2 2 2 2 2 2 ...table(ts_subject_info_subset$Subject)##
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## 673455 677766 679568 679770 680250 680957 683256 686969 687163 690152
## 167 167 167 167 167 167 167 167 167 167
## 693764 695768 700634 702133 704238 705341 706040 707749 709551 713239
## 167 167 167 167 167 167 167 167 167 167
## 715647 715950 720337 724446 725751 727654 729254 729557 731140 732243
## 167 167 167 167 167 167 167 167 167 167
## 734045 735148 737960 742549 748258 749058 751348 753150 753251 756055
## 167 167 167 167 167 167 167 167 167 167
## 759869 761957 765056 767464 769064 770352 771354 773257 782561 783462
## 167 167 167 167 167 167 167 167 167 167
## 784565 788876 789373 792564 792766 792867 793465 800941 803240 812746
## 167 167 167 167 167 167 167 167 167 167
## 814649 816653 818859 820745 825048 826353 826454 833148 833249 837560
## 167 167 167 167 167 167 167 167 167 167
## 837964 843151 844961 845458 849264 849971 852455 856766 856968 859671
## 167 167 167 167 167 167 167 167 167 167
## 865363 867468 870861 871762 871964 872158 872562 872764 873968 877168
## 167 167 167 167 167 167 167 167 167 167
## 877269 878776 880157 882161 885975 887373 889579 891667 894067 894673
## 167 167 167 167 167 167 167 167 167 167
## 894774 896778 896879 898176 901139 901442 904044 907656 910241 910443
## 167 167 167 167 167 167 167 167 167 167
## 912447 917255 917558 919966 922854 923755 926862 930449 932554 937160
## 167 167 167 167 167 167 167 167 167 167
## 942658 947668 951457 952863 955465 957974 958976 959574 965367 965771
## 167 167 167 167 167 167 167 167 167 167
## 966975 978578 979984 983773 984472 987983 990366 992673 992774 994273
## 167 167 167 167 167 167 167 167 167 167ct = table(ts_subject_info_subset[,c("Acquisition","sleep_duration")])/tp_per_subject
addmargins(ct)## sleep_duration
## Acquisition conventional short Sum
## Q01 28 12 40
## Q02 48 14 62
## Q03 40 30 70
## Q04 53 26 79
## Q05 37 37 74
## Q06 55 31 86
## Q07 45 15 60
## Q08 42 20 62
## Q09 41 14 55
## Q10 46 22 68
## Q11 46 18 64
## Q12 8 2 10
## Sum 489 241 730Subsample subject for power curve.
subsample_subjects = function(ts_subject_info_subset,
num_subjects,
seed = 934321) {
set.seed(seed)
total_samples = table(ts_subject_info_subset$sleep_duration)/table(ts_subject_info_subset$Subject)[1]
num_subject_short = ceiling(num_subjects * total_samples["short"] / sum(total_samples))
num_subject_conventional = num_subjects-num_subject_short
short_names = subset(ts_subject_info_subset,sleep_duration == "short")$Subject %>%
table %>% names %>% sample(.,num_subject_short)
conventional_names = subset(ts_subject_info_subset,sleep_duration == "conventional")$Subject %>%
table %>% names %>% sample(.,num_subject_conventional)
subject_names = c(short_names,conventional_names)
ts_subject_info_subset[ts_subject_info_subset$Subject %in% subject_names,]
}Low-Dimensional Covariance Modeling
We can account for correlation between the error terms of the multivariate response variable using covariance regression model. Repeat many times for power curve.
condition = "sleep_duration"
ts_subject_info_subset = within(ts_subject_info_subset,
sleep_duration <- relevel(sleep_duration, ref = "conventional"))
num_subjects = as.integer(params$num_subjects)
num_samples = as.integer(params$num_samples)
log_name = paste0("HCP_low_",num_regions,"_",num_subjects,"_",tp_per_subject)
if(dir.exists(log_name)) unlink(log_name,recursive = TRUE) # cleanup
reg = makeRegistry(id = log_name, file.dir = log_name)## Creating dir: HCP_low_15_80_167## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries## Saving registry: /Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/registry.RDatabatchExport(reg, li = list(channel_names = channel_names,
condition = condition,
overwrite = TRUE))
fun = function(data) {
library(CovRegFC)
CovRegFC::covreg_low(data = data,
response = channel_names,
condition = condition)
}
if(is.na(num_subjects)) {
batchMap(reg, fun, ts_subject_info_subset)
} else {
data = lapply(1:num_samples,function(seed) {
subsample_subjects(ts_subject_info_subset,
num_subjects = num_subjects,
seed = seed)
})
batchMap(reg, fun, data)
}## Adding 1 jobs to DB.## Warning in checkUserFunction(fun): The environment of provided function
## exceeds 10Mb.
## Warning in checkUserFunction(fun): Don't need to call dbFetch() for
## statements, only for queries
## Warning in checkUserFunction(fun): Don't need to call dbFetch() for
## statements, only for queriessubmitJobs(reg)## Saving conf: /Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/conf.RData## Submitting 1 chunks / 1 jobs.## Cluster functions: Interactive.## Auto-mailer settings: start=none, done=none, error=none.## Loading registry: /Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/registry.RData## Loading RData files: channel_names,condition,overwrite## Loading conf: /Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/conf.RData## 2017-06-22 17:25:00: Starting job on node Christofs-MacBook-Pro-2.local.## Auto-mailer settings: start=none, done=none, error=none.## Setting work dir: /Users/cseiler/CovRegFC_HCP## Loading jobs from file '/Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/jobs/01/1.rds'## ########## Executing jid=1 ############ Timestamp: 2017-06-22 17:25:00## Setting seed: 963208816## The following numerical problems occurred the indicated number of times on chain 1## count
## Exception thrown at line 20: normal_lpdf: Scale parameter[1] is 0, but must be > 0! 1## When a numerical problem occurs, the Hamiltonian proposal gets rejected.## See http://mc-stan.org/misc/warnings.html#exception-hamiltonian-proposal-rejected## If the number in the 'count' column is small, there is no need to ask about this message on stan-users.## Writing result file: /Users/cseiler/CovRegFC_HCP/HCP_low_15_80_167/jobs/01/1-result.RData## 2017-06-22 18:28:38: All done.## Setting work back to: /Users/cseiler/CovRegFC_HCP## Memory usage according to gc:## Sending 1 submit messages...
## Might take some time, do not interrupt this!waitForJobs(reg,sleep = 360)## Syncing registry ...## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries
## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
## statements, only for queries## [1] TRUEfit_list = reduceResultsList(reg)## Reducing 1 results...save(fit_list,file = paste0("fit_list_low_",num_regions,"_",num_subjects,"_",tp_per_subject,".Rdata"))
fit = fit_list[[1]]Traceplots to assess convergence of HMC sampler.
CovRegFC::plot_diagnostics(fit,par_name = "sigma",num_par = 8)
CovRegFC::plot_diagnostics(fit,par_name = "B",num_par = 8)
Visualize Results
Plot regions.
path = paste0("HCP_PTN820/groupICA/groupICA_3T_HCP820_MSMAll_d",num_regions,".ica/melodic_IC_sum.sum")
pngs = list.files(path = path,pattern = ".png")
full_paths = paste(path,pngs,sep = "/")
ps = lapply(1:length(full_paths),function(i)
CovRegFC::plot_brain(full_paths[i],title = paste0("R",i))
)
p_brains = do.call(plot_grid,c(ps,nrow = 2))
p_brains
ggsave(p_brains,filename = paste0("brains_",num_regions,".pdf"),width = 9,height = 3)Analyze posterior covariance explained by simulation conditions.
condition_names = ts_subject_info_subset[,condition] %>% contrasts %>% rownames %>% rev
title = paste(condition_names,collapse = " vs. ")
p_brain_coeff = CovRegFC::plot_coeff(fit,
response = channel_names,
alpha = 0.05/length(channel_names),
title = title,
brain_slices = full_paths)
p_brain_coeff
ggsave(p_brain_coeff,filename = paste0("brains_coeff_",num_regions,"_",num_subjects,"_",tp_per_subject,".pdf"),
height = 5,width = 10)Session Info
session_info()## Session info -------------------------------------------------------------## setting value
## version R version 3.3.2 (2016-10-31)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/Los_Angeles
## date 2017-06-22## Packages -----------------------------------------------------------------## package * version date
## abind * 1.4-5 2016-07-21
## backports 1.1.0 2017-05-22
## base * 3.3.2 2016-10-31
## base64enc 0.1-3 2015-07-28
## BatchJobs * 1.7 2016-10-01
## BBmisc * 1.11 2017-03-10
## bit 1.1-12 2014-04-09
## bit64 0.9-7 2017-05-08
## blob 1.1.0 2017-06-17
## brew 1.0-6 2011-04-13
## checkmate 1.8.2 2016-11-02
## coda * 0.19-1 2016-12-08
## codetools 0.2-15 2016-10-05
## colorspace 1.3-2 2016-12-14
## CovRegFC * 0.1.0 2017-06-22
## cowplot * 0.7.0 2016-10-28
## data.table 1.10.4 2017-02-01
## datasets * 3.3.2 2016-10-31
## DBI 0.7 2017-06-18
## devtools * 1.13.2 2017-06-02
## digest 0.6.12 2017-01-27
## evaluate 0.10 2016-10-11
## ggplot2 * 2.2.1 2016-12-30
## graphics * 3.3.2 2016-10-31
## grDevices * 3.3.2 2016-10-31
## grid 3.3.2 2016-10-31
## gridExtra 2.2.1 2016-02-29
## gtable 0.2.0 2016-02-26
## htmltools 0.3.6 2017-04-28
## inline 0.3.14 2015-04-13
## knitr 1.16 2017-05-18
## labeling 0.3 2014-08-23
## lattice 0.20-35 2017-03-25
## lazyeval 0.2.0.9000 2017-05-09
## magrittr * 1.5 2014-11-22
## memoise 1.1.0 2017-04-21
## methods * 3.3.2 2016-10-31
## munsell 0.4.3 2016-02-13
## pkgconfig 2.0.1 2017-03-21
## plyr 1.8.4 2016-06-08
## png * 0.1-7 2013-12-03
## Rcpp * 0.12.11 2017-05-22
## reshape2 * 1.4.2 2016-10-22
## rlang 0.1.1 2017-05-18
## rmarkdown 1.6 2017-06-15
## rprojroot 1.2 2017-01-16
## RSQLite 2.0 2017-06-19
## rstan * 2.15.1 2017-04-19
## scales 0.4.1 2016-11-09
## sendmailR 1.2-1 2014-09-21
## StanHeaders * 2.15.0-1 2017-04-19
## stats * 3.3.2 2016-10-31
## stats4 3.3.2 2016-10-31
## stringi 1.1.5 2017-04-07
## stringr * 1.2.0 2017-02-18
## tibble 1.3.3 2017-05-28
## tools 3.3.2 2016-10-31
## utils * 3.3.2 2016-10-31
## withr 1.0.2 2016-06-20
## yaml 2.1.14 2016-11-12
## source
## CRAN (R 3.2.5)
## CRAN (R 3.3.2)
## local
## CRAN (R 3.2.0)
## Github (ChristofSeiler/BatchJobs@0e48846)
## CRAN (R 3.3.2)
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## cran (@0.6.12)
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## CRAN (R 3.2.4)
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## CRAN (R 3.2.0)
## CRAN (R 3.3.2)
## Github (hadley/lazyeval@c155c3d)
## CRAN (R 3.2.0)
## CRAN (R 3.3.2)
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## CRAN (R 3.2.3)
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## CRAN (R 3.3.2)
## local
## local
## CRAN (R 3.2.5)
## cran (@2.1.14)Hirshkowitz et al. (2015), National Sleep Foundation’s sleep recommendations: Methodology and results summary, Sleep Health.↩