Time window selection.

The three subplots represent the precision values for different time windows based on 21 start frames (x axis) and 12 window lengths (7 frames to 29 frames) for phases 1, 2, and 3 (from top to bottom) respectively, and the black bash line in each subplot indicates a precision value of 0.55.</p

iPS progenitor cells vs. MEFs and feature correlation.

(a) shows the examples of iPS progenitor cell images (blue circles) and normal MEFs images (yellow boxes) taken from phase 1, 2 and 3 of field 2 (Left, middle and right). Nucleus and cytoplasm of the enlarged progenitor cells and normal MEFs are colored in light blue and green respectively. (b) shows the Pearson coefficients between remaining types of features in three phases after the first step of feature selection. Note in this figure ellipsoid-prolate is denoted as E-prolate, intensity-StdDev as I-stdDev, intensity-min as I-Min, intensity-max as I-Max, nucleus-cytoplasm volume ratio as Ratio, ellipsoid-oblate as E-oblate.</p

Feature ranking and selection.

This figure shows how the precision values change with the deleted feature in a recursive fashion. Least important features are removed earlier.</p

Model comparison for different missing frame number and imputation methods.

(a) shows the average precision over six time periods (TP1 to TP6) for each missing frame number and imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray). (b) shows the standard deviation, as a function of missing frame number, of imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray).</p

Model validation.

In all sub-figures, X axis indicates the start frame of the best time windows and the corresponding window length (13 frames) is indicated in the inlet. (a) 5-fold cross-validation precisions over 10 runs. (b) the standard deviation of the average precision of the neighborhood time windows in Fig 6D. (c) the standard deviation of the average precision of the distant windows in Fig 6E. (d) the average precision of seven neighborhood time windows calculated over 10 holdout validation runs. (e) the average precision over 10 independent tests for six best time windows on their corresponding distant windows.</p

Flow chart of the machine learning based approach for iPS progenitor cell identification.

In time-lapse imaging, we record the reprogramming process periodically among 54 fields after 48h of viral infection. For retrospective tracking, the figure only shows the reprogramming lineage images of the first frame of all eight phases. Only datasets from phase 1, 2 and 3 are used for model training and testing.</p