A Case-Study of Parkinson’s Disease using Simulated Big Data

Overview: This case-study examines the associations between clinical, demographic, imaging and genetics variables for Parkinson’s disease. This is an example of Big Data for investigating important neurodegenerative disorders.

Driving Challenges:
- Are there relations between imaging, genetic and clinical covariates?
- Can we predict subject diagnosis using model-based approaches?
- Do exploratory data analytics provide clues to Parkinson’s disease?
- If clinical PD Dx is solely based on UPDRS scores, is there value added by including other covariates?

Meta-data:
- ID: Case subject identifier
- Imaging Biomarkers: ComputeArea = surface area of 3D brain region of interest, Volume= the 3D volume/size of the region of interest, region name is encoded in morphometry measure name (e.g., L_putamen_ComputeArea represents the left putamen surface area):

L_caudate_ComputeArea, L_caudate_Volume, R_caudate_ComputeArea, R_caudate_Volume, L_putamen_ComputeArea, L_putamen_Volume, R_putamen_ComputeArea, R_putamen_Volume, L_hippocampus_ComputeArea, L_hippocampus_Volume, R_hippocampus_ComputeArea, R_hippocampus_Volume, cerebellum_ComputeArea, cerebellum_Volume, L_lingual_gyrus_ComputeArea, L_lingual_gyrus_Volume, R_lingual_gyrus_ComputeArea, R_lingual_gyrus_Volume, L_fusiform_gyrus_ComputeArea, L_fusiform_gyrus_Volume, R_fusiform_gyrus_ComputeArea, R_fusiform_gyrus_Volume

Demographics variables: Sex, Weight, Age
Diagnosis: Dx, PD=Parkinson’s, HC=Healthy Control, SWEDD = (tremor associated clinical parkinsonism features) scans without evidence of dopaminergic deficit
Genetics: chr12_rs34637584_GT, chr17_rs11868035_GT
Clinical: UPDRS_part_I, UPDRS_part_II, UPDRS_part_III (Movement Disorder Society-Sponsored Revision of the Unified. Parkinson's Disease Rating Scale (MDS-UPDRS)), http://www.movementdisorders.org/MDS/Education/Rating-Scales.htm (Normal=0)
Time: VisitTime: four time-points (baseline (0), 6, 12, and 18 month follow-ups).

Data: Complete data is in this table (HS853_PD_SimData.csv), from the Canvas-files partition.

Provenance: This case study only uses simulated data. The complete R-script generating the data is included below. The entire case study is CC-BY licensed and can be used, updated, refactored and expanded by the entire community.

# Define number of subjects

NumSubj <- 282

NumTime <- 4

# Define data elements

# Cases

Cases <- c(2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 37, 41, 42, 43, 44, 45, 53, 55, 58, 60, 62, 67, 69, 71, 72, 74, 79, 80, 85, 87, 90, 95, 97, 99, 100, 101, 106, 107, 109, 112, 120, 123, 125, 128, 129, 132, 134, 136, 139, 142, 147, 149, 153, 158, 160, 162, 163, 167, 172, 174, 178, 179, 180, 182, 192, 195, 201, 208, 211, 215, 217, 223, 227, 228, 233, 235, 236, 240, 245, 248, 250, 251, 254, 257, 259, 261, 264, 268, 269, 272, 273, 275, 279, 288, 289, 291, 296, 298, 303, 305, 309, 314, 318, 324, 325, 326, 328, 331, 332, 333, 334, 336, 338, 339, 341, 344, 346, 347, 350, 353, 354, 359, 361, 363, 364, 366, 367, 368, 369, 370, 371, 372, 374, 375, 376, 377, 378, 381, 382, 384, 385, 386, 387, 389, 390, 393, 395, 398, 400, 410, 421, 423, 428, 433, 435, 443, 447, 449, 450, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 465, 466, 467, 470, 471, 472, 476, 477, 478, 479, 480, 481, 483, 484, 485, 486, 487, 488, 489, 492, 493, 494, 496, 498, 501, 504, 507, 510, 513, 515, 528, 530, 533, 537, 538, 542, 545, 546, 549, 555, 557, 559, 560, 566, 572, 573, 576, 582, 586, 590, 592, 597, 603, 604, 611, 619, 621, 623, 624, 625, 631, 633, 634, 635, 637, 640, 641, 643, 644, 645, 646, 647, 648, 649, 650, 652, 654, 656, 658, 660, 664, 665, 670, 673, 677, 678, 679, 680, 682, 683, 686, 687, 688, 689, 690, 692)

# Imaging Biomarkers

L_caudate_ComputeArea <- rpois(NumSubj, 600)

L_caudate_Volume <- rpois(NumSubj, 800)

R_caudate_ComputeArea <- rpois(NumSubj, 893)

R_caudate_Volume <- rpois(NumSubj, 1000)
L_putamen_ComputeArea <- rpois(NumSubj, 900)
L_putamen_Volume <- rpois(NumSubj, 1400)
R_putamen_ComputeArea <- rpois(NumSubj, 1300)
R_putamen_Volume <- rpois(NumSubj, 3000)
L_hippocampus_ComputeArea <- rpois(NumSubj, 1300)
L_hippocampus_Volume <- rpois(NumSubj, 3200)
R_hippocampus_ComputeArea <- rpois(NumSubj, 1500)
R_hippocampus_Volume <- rpois(NumSubj, 3800)
cerebellum_ComputeArea <- rpois(NumSubj, 16700)
cerebellum_Volume <- rpois(NumSubj, 14000)
L_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
L_lingual_gyrus_Volume <- rpois(NumSubj, 11000)
R_lingual_gyrus_ComputeArea <- rpois(NumSubj, 3300)
R_lingual_gyrus_Volume <- rpois(NumSubj, 12000)
L_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3600)
L_fusiform_gyrus_Volume <- rpois(NumSubj, 11000)
R_fusiform_gyrus_ComputeArea <- rpois(NumSubj, 3300)
R_fusiform_gyrus_Volume <- rpois(NumSubj, 10000)

# Demographics variables

Sex <- ifelse(runif(NumSubj)<.5,0,1)

Weight <- as.integer(rnorm(NumSubj, 80,10))

Age <- as.integer(rnorm(NumSubj, 62,10))

# Diagnosis:

Dx <- c(rep("PD", 100), rep("HC", 100), rep("SWEDD", 82))

# Genetics

chr12_rs34637584_GT <- c(ifelse(runif(100)<.3,0,1), ifelse(runif(100)<.6,0,1), ifelse(runif(82)<.4,0,1)) # NumSubj Bernoulli trials

chr17_rs11868035_GT <- c(ifelse(runif(100)<.7,0,1), ifelse(runif(100)<.4,0,1), ifelse(runif(82)<.5,0,1)) # NumSubj Bernoulli trials

# Clinical # rpois(NumSubj, 15) + rpois(NumSubj, 6)

UPDRS_part_I <- c( ifelse(runif(100)<.7,0,1)+ifelse(runif(100)<.7,0,1),

ifelse(runif(100)<.6,0,1)+ ifelse(runif(100)<.6,0,1),

ifelse(runif(82)<.4,0,1)+ ifelse(runif(82)<.4,0,1) )

UPDRS_part_II <- c(sample.int(20, 100, replace=T), sample.int(14, 100, replace=T),

sample.int(18, 82, replace=T) )

UPDRS_part_III <- c(sample.int(30, 100, replace=T), sample.int(20, 100, replace=T),

sample.int(25, 82, replace=T) )

# Time: VisitTime – done automatically below in aggregator

# Data (putting all components together)

sim_PD_Data <- cbind(

rep(Cases, each= NumTime), # Cases

rep(L_caudate_ComputeArea, each= NumTime), # Imaging

rep(L_caudate_Volume, each= NumTime),

                                         rep(R_caudate_ComputeArea, each= NumTime),
                                         rep(R_caudate_Volume, each= NumTime),
                                         rep(L_putamen_ComputeArea, each= NumTime),
                                         rep(L_putamen_Volume, each= NumTime),
                                         rep(R_putamen_ComputeArea, each= NumTime),
                                         rep(R_putamen_Volume, each= NumTime),
                                         rep(L_hippocampus_ComputeArea, each= NumTime),
                                         rep(L_hippocampus_Volume, each= NumTime),
                                         rep(R_hippocampus_ComputeArea, each= NumTime),
                                         rep(R_hippocampus_Volume, each= NumTime),
                                         rep(cerebellum_ComputeArea, each= NumTime),
                                         rep(cerebellum_Volume, each= NumTime),
                                         rep(L_lingual_gyrus_ComputeArea, each= NumTime),
                                         rep(L_lingual_gyrus_Volume, each= NumTime),
                                         rep(R_lingual_gyrus_ComputeArea, each= NumTime),
                                         rep(R_lingual_gyrus_Volume, each= NumTime),
                                         rep(L_fusiform_gyrus_ComputeArea, each= NumTime),
                                         rep(L_fusiform_gyrus_Volume, each= NumTime),
                                         rep(R_fusiform_gyrus_ComputeArea, each= NumTime),
                                         rep(R_fusiform_gyrus_Volume, each= NumTime),

rep(Sex, each= NumTime), # Demographics

rep(Weight, each= NumTime),

rep(Age, each= NumTime),

rep(Dx, each= NumTime), # Dx

rep(chr12_rs34637584_GT, each= NumTime), # Genetics

rep(chr17_rs11868035_GT, each= NumTime),

rep(UPDRS_part_I, each= NumTime), # Clinical

rep(UPDRS_part_II, each= NumTime),

rep(UPDRS_part_III, each= NumTime),

rep(c(0,6,12,18), NumSubj) # Time

)

# Assign the column names

colnames(sim_PD_Data) <- c(

"Cases",

"L_caudate_ComputeArea",

"L_caudate_Volume",

"R_caudate_ComputeArea",

"R_caudate_Volume",

"L_putamen_ComputeArea",

"L_putamen_Volume",

"R_putamen_ComputeArea",

"R_putamen_Volume",

"L_hippocampus_ComputeArea",

"L_hippocampus_Volume",

"R_hippocampus_ComputeArea",

"R_hippocampus_Volume",

"cerebellum_ComputeArea",

"cerebellum_Volume",

"L_lingual_gyrus_ComputeArea",

"L_lingual_gyrus_Volume",

"R_lingual_gyrus_ComputeArea",

"R_lingual_gyrus_Volume",

"L_fusiform_gyrus_ComputeArea",

"L_fusiform_gyrus_Volume",

"R_fusiform_gyrus_ComputeArea",

"R_fusiform_gyrus_Volume",

"Sex", "Weight", "Age",

"Dx", "chr12_rs34637584_GT", "chr17_rs11868035_GT",

"UPDRS_part_I", "UPDRS_part_II", "UPDRS_part_III",

"Time")

# some QC

summary(sim_PD_Data)

dim(sim_PD_Data)

head(sim_PD_Data)

# Write out (save) the result to a file that can be shared

write.table(sim_PD_Data, "output_data.csv", sep=",", row.names=FALSE, col.names=TRUE)

The transposed matrix looks like this:

Cases	2	2	2	2	3	3	3	3	6	6
L_caudate_ComputeArea	597	597	597	597	604	604	604	604	580	..
L_caudate_Volume	767	767	767	767	873	873	873	873	797	..
R_caudate_ComputeArea	855	855	855	855	935	935	935	935	919	..
R_caudate_Volume	968	968	968	968	1043	1043	1043	1043	1023	..
L_putamen_ComputeArea	842	842	842	842	892	892	892	892	908	..
L_putamen_Volume	1357	1357	1357	1357	1366	1366	1366	1366	1415	..
R_putamen_ComputeArea	1285	1285	1285	1285	1305	1305	1305	1305	1264	..
R_putamen_Volume	3052	3052	3052	3052	2920	2920	2920	2920	2995	..
L_hippocampus_ComputeArea	1306	1306	1306	1306	1292	1292	1292	1292	1313	..
L_hippocampus_Volume	3238	3238	3238	3238	3079	3079	3079	3079	3227	..
R_hippocampus_ComputeArea	1513	1513	1513	1513	1516	1516	1516	1516	1541	..
R_hippocampus_Volume	3759	3759	3759	3759	3827	3827	3827	3827	3791	..
cerebellum_ComputeArea	16845	16845	16845	16845	16698	16698	16698	16698	16480	..
cerebellum_Volume	13949	13949	13949	13949	14076	14076	14076	14076	13992	..
L_lingual_gyrus_ComputeArea	3268	3268	3268	3268	3243	3243	3243	3243	3331	..
L_lingual_gyrus_Volume	11130	11130	11130	11130	11033	11033	11033	11033	11093	..
R_lingual_gyrus_ComputeArea	3294	3294	3294	3294	3190	3190	3190	3190	3407	..
R_lingual_gyrus_Volume	12221	12221	12221	12221	12187	12187	12187	12187	12062	..
L_fusiform_gyrus_ComputeArea	3625	3625	3625	3625	3631	3631	3631	3631	3520	..
L_fusiform_gyrus_Volume	11087	11087	11087	11087	11116	11116	11116	11116	10890	..
R_fusiform_gyrus_ComputeArea	3232	3232	3232	3232	3302	3302	3302	3302	3328	..
R_fusiform_gyrus_Volume	10122	10122	10122	10122	10162	10162	10162	10162	9884	..
Sex	1	1	1	1	0	0	0	0	0	..
Weight	84	84	84	84	97	97	97	97	96	..
Age	67	67	67	67	39	39	39	39	54	..
Dx	PD	PD	PD	PD	PD	PD	PD	PD	PD	..
chr12_rs34637584_GT	1	1	1	1	1	1	1	1	0	..
chr17_rs11868035_GT	0	0	0	0	1	1	1	1	0	..
UPDRS_part_I	1	1	1	1	0	0	0	0	1	..
UPDRS_part_II	12	12	12	12	19	19	19	19	15	..
UPDRS_part_III	1	1	1	1	22	22	22	22	19	..
Time	0	6	12	18	0	6	12	18	0	..

See more on this case-study here (including the complete dataset).