Z-scoring¶
This section describes how to run the z-scoring module to normalize single patient data against a cohort of controls.
Setting up your control dataset¶
You will need to set up a csv file with two mandatory columns:
One column containing the subject IDs for all controls to be included in the analysis (column name: ID
),
as well as one column with corresponding session name to be analyzed (column name: SES
).
Note: if your dataset does not contain session information, then leave the SES
column blank.
An example control table can be found in our GitHub repository.
Regional analysis¶
After defining your directories as in the previous section (-proc
), you can run the regional z-scoring analysis as follows:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # Define path to control dataset csv hc_info=/PATH/TO/CSV/control.csv # Information on patient to be analyzed px_id="PX001" px_ses="01" z-brains -sub "$id" -ses "$ses" \ -rawdir "${rawdir}" \ -micapipedir "${micapipedir}" \ -hippdir "${hippdir}" \ -outdir "${outdir}" \ -run regional \ -approach "zscore" \ -demo_cn "${hc_info}" \ -verbose 2
Asymmetry analysis¶
Similar to the regional
analysis, you can generated z-scored asymmetry maps with the following approach:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # Define path to control dataset csv hc_info=/PATH/TO/CSV/control.csv # Information on patient to be analyzed px_id="PX001" px_ses="01" z-brains -sub "$id" -ses "$ses" \ -rawdir "${rawdir}" \ -micapipedir "${micapipedir}" \ -hippdir "${hippdir}" \ -outdir "${outdir}" \ -run asymmetry \ -approach "zscore" \ -demo_cn "${hc_info}" \ -verbose 2