All code for this document is located at here.
Using the neurohcp package
Although the neurohcp package was built specifically for the the Human Connectome Project (HCP) data, it provides the worker functions for accessing an Amazon S3 bucket and downloading data. We have adapted these functions to work with the Functional Connectomes Project S3 Bucket (fcp-indi) from the INDI initiative. Although the code is the same, but the bucket is changed, we also must specify we do not want to sign the request as fcp-indi is an open bucket and the authentication we used for signing fails if we add keys to the data when unneccesary.
Getting Access to the Data
The data is freelly available;.
Installing the neurohcp package
We will install the neurohcp package using the Neuroconductor installer:
source("http://neuroconductor.org/neurocLite.R")
neuro_install("neurohcp", release = "stable")Once these are set, the functions of neurohcp are ready to use. To test that the API keys are set correctly, one can run bucketlist:
neurohcp::bucketlist(sign = FALSE)Warning in neurohcp::bucketlist(verbose = FALSE, sign = FALSE): Response
was html from amazon, returning output rather than parsingResponse [https://aws.amazon.com/s3/]
Date: 2017-10-16 18:41
Status: 200
Content-Type: text/html;charset=UTF-8
Size: 281 kB
<!DOCTYPE html>
<html class="no-js aws-lng-en_US" lang="en-US" xmlns="http://www.w3.org/...
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="dns-prefetch" href="https://a0.awsstatic.com" />
<link rel="dns-prefetch" href="//d0.awsstatic.com" />
<link rel="dns-prefetch" href="//d1.awsstatic.com" />
<title>Amazon Simple Storage Service (S3) — Cloud Storage — AWS</title>
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
...We see that fcp-indi is a bucket that we have access to, and therefore have access to the data.
Getting Data: Downloading a Directory of Data
In the neurohcp package, there is a data set indicating the scans read for each subject, named hcp_900_scanning_info. We can subset those subjects that have diffusion tensor imaging:
ids_with_dwi = hcp_900_scanning_info %>%
filter(scan_type %in% "dMRI") %>%
select(id) %>%
unique
head(ids_with_dwi)# A tibble: 6 x 1
id
<chr>
1 100307
2 100408
3 101006
4 101107
5 101309
6 101410Let us download the complete directory of diffusion data using download_hcp_dir:
r = download_hcp_dir("HCP/100307/T1w/Diffusion", verbose = FALSE)
print(basename(r$output_files))[1] "bvals" "bvecs"
[3] "data.nii.gz" "grad_dev.nii.gz"
[5] "nodif_brain_mask.nii.gz"This diffusion data is the data that can be used to create summaries such as fractional anisotropy and mean diffusivity.
If we create a new column with all the directories, we can iterate over these to download all the diffusion data for these subjects from the HCP database.
ids_with_dwi = ids_with_dwi %>%
mutate(id_dir = paste0("HCP/", id, "/T1w/Diffusion"))Getting Data: Downloading a Single File
We can also download a single file using download_hcp_file. Here we will simply download the bvals file:
ret = download_hcp_file("HCP/100307/T1w/Diffusion/bvals", verbose = FALSE)Session Info
devtools::session_info()Session info ------------------------------------------------------------- setting value
version R version 3.4.2 (2017-09-28)
system x86_64, darwin15.6.0
ui X11
language (EN)
collate en_US.UTF-8
tz America/New_York
date 2017-10-16 Packages ----------------------------------------------------------------- package * version date source
assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
backports 1.1.1 2017-09-25 CRAN (R 3.4.2)
base * 3.4.2 2017-10-04 local
base64enc 0.1-3 2015-07-28 CRAN (R 3.4.0)
bindr 0.1 2016-11-13 CRAN (R 3.4.0)
bindrcpp 0.2 2017-06-17 CRAN (R 3.4.0)
colorout * 1.1-0 2015-04-20 Github (jalvesaq/colorout@1539f1f)
compiler 3.4.2 2017-10-04 local
curl 3.0 2017-10-06 CRAN (R 3.4.2)
datasets * 3.4.2 2017-10-04 local
devtools 1.13.3 2017-08-02 CRAN (R 3.4.1)
digest 0.6.12 2017-01-27 CRAN (R 3.4.0)
dplyr * 0.7.4 2017-09-28 CRAN (R 3.4.2)
evaluate 0.10.1 2017-06-24 cran (@0.10.1)
glue 1.1.1 2017-06-21 cran (@1.1.1)
graphics * 3.4.2 2017-10-04 local
grDevices * 3.4.2 2017-10-04 local
htmltools 0.3.6 2017-04-28 CRAN (R 3.4.0)
httr 1.3.1 2017-08-20 cran (@1.3.1)
knitr 1.17 2017-08-10 cran (@1.17)
magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
memoise 1.1.0 2017-04-21 CRAN (R 3.4.0)
methods 3.4.2 2017-10-04 local
neurohcp * 0.6 2017-05-14 CRAN (R 3.4.0)
pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.0)
R6 2.2.2 2017-06-17 CRAN (R 3.4.0)
Rcpp 0.12.13 2017-09-28 cran (@0.12.13)
rlang 0.1.2.9000 2017-09-11 Github (tidyverse/rlang@cf9de64)
rmarkdown 1.6 2017-06-15 cran (@1.6)
rprojroot 1.2 2017-01-16 CRAN (R 3.4.0)
stats * 3.4.2 2017-10-04 local
stringi 1.1.5 2017-04-07 CRAN (R 3.4.0)
stringr 1.2.0 2017-02-18 CRAN (R 3.4.0)
tibble 1.3.4 2017-08-22 cran (@1.3.4)
tools 3.4.2 2017-10-04 local
utils * 3.4.2 2017-10-04 local
withr 2.0.0 2017-10-05 Github (jimhester/withr@d1f0957)
xml2 1.1.9000 2017-09-05 Github (hadley/xml2@5799cd9)
yaml 2.1.14 2016-11-12 CRAN (R 3.4.0)