Vidhi Lalchand
Postdoctoral Fellow, Broad and MIT
NMDP [002 SP_B]
Vidhi Lalchand, Ph.D.
IMU Biosciences
16th Feb, 2026
[203 donors]
Data Semantics & Processing
NMDP 002 SP_B Combined Ratios: Tv5 Panel
Columns dropped due to NaNs for significant cross-section of donors:
clean_tv_num = clean_tv_num.loc[:, clean_tv_num.nunique(dropna=False) > 1] # Drop columns with only one unique value
clean_tv_num = clean_tv_num.drop(columns=["aTreg_Tv5", "mTreg_Tv5", "rTreg_Tv5"])
Log1p + Standardization
Batch effect + Noise columns
Non-Trivial Predictive Signal for Chronic Relapse in Cell Ratios & Clinical Co-variates
The figures below show averaged ROC curves computed on unseen data for classifying the binary variable of chronic relapse from cell-ratios and clinical covariates. The shaded band shows standard error of the mean at each threshold.
No clinical covariates
With clinical covariates
Non-Trivial Predictive Signal for Chronic Relapse in Cell Ratios & Clinical Co-variates
The figures below show averaged ROC curves computed on unseen data for classifying the binary variable of chronic relapse from cell-ratios and clinical covariates. The shaded band shows standard error of the mean at each threshold.
No clinical covariates
With clinical covariates
Quantifying the effect of covariates across two independent algorithms
The plot on the left shows the average recall rate across 10-fold CV on unseen donors jointly across both methods (MLP and Kernel Reg.). Although the standard errors overlap, the results suggest modest incremental predictive value from the clinical covariates.
NMDP Results v3
By Vidhi Lalchand
