SAMPLE DATA FILES 
These sample files are from the actual dataset which is private
1) MT_321_120501_Direct_d500k.vcf
sample .vcf file

2) information file
ROSMAP_SamplesInfoForMito.txt

CODE AND CORRESPONDING RESULT FILES
#################
PART 1 - global statistics
1)test1.R
extracts relevant features from the .vcf files and index file
Output files:
ADindRat.txt
NADindRat.txt


2) stats_Test.R
computes basic statistics on output of 1)


3) hyp_test_new.R
doing all the hypothesis testing described in the paper


4) ml_test.R
machine learning
a) plotting raw data
b) logistic / probit regression with the 70-30 train test split

5) ml_part2.R
generating the ROC curve with LOOCV


6) pmi_test.R
testing whether the metadata variable 'post-mortem interval' is related to AD/NAD with t-test.

############
PART 2
RARE AND COMMON MUTATIONS

7) rarecommon_test.R
obtaining information (by thresholding; <=3 -> rare) of rare and common mutations
Output:
mutation_info.txt
commonmutation_info3.txt
raremutation_info_thresh3.txt

8) rarecommon2_test.R
go through each .vcf file and count number of rare mutations in it. also check if AD or not; 
ouput:
ADind_withrare_thresh3.txt
NADind_withrare_thresh3.txt

9) stats_rare.R
computer simple stats on rare mutation information


10) hyp_test_rare.R
hypothesis testing AD/NAD and total number of rare mutations in the cell

11) common.R
building file of containing binary indicator vector for presence/absence for each common mutation
output:
ADind_common3.txt
NADind_common3.txt

12) stats_common.R
compute simple stats on common mutations for each mutated position

13) hyp_common.R
hypothesis testing for each common mutation


##########################
PART 3
14) number_mit.R
obtaining number of mitochondria (estimate from average read depth) for each cell which contains a mutation (rare and/or common)
output:
AD_mitCount.txt
NAD_mitCount.txt

15) stats_mit.R
simple statistics on the results of 14)

15) correln_mit
spearmann2 correlation coefficient for each mutation and read depth