## 2 Materials and Design of Experiment
### Evaluation Procedure
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \# & **Dataset** & **Phenotype** & **\# of 1s** & **\# of 0s** \\ \hline
1 & EMTAB2805 & Cell Stages (G1 \& G2M) & 96 & 96 \\ \hline  & & 16-cell stage blastomere \& Mid & \\
2 & GSE100911 & blastocyst cell (92–94 h & 43 & 38 \\  & & post-fertilization) & & \\ \hline
3 & GSE102299-smartseq2 & treatment: IL25 \& treatment: IL33 & 188 & 282 \\  & & 16-cell stage blastomere \& Mid & \\
4 & GSE45719 & blastocyst cell (92–94 h & 50 & 60 \\  & & post-fertilization) & & \\ \hline
5 & GSE48968-GPL13112 & BMDC (Unstimulated Replicate & \\  & & Experiment) \& BMDC (1 h LPS & 96 & 95 \\  & & Stimulation) & & \\ \hline
6 & GSE48968-GPL17021-125 bp & BMDC (On Chip 4 h LPS & \\  & & Stimulation) \& BMDC (2 h IFN-B & 90 & 94 \\  & & Stimulation) & & \\ \hline
7 & GSE48968-GPL17021-25 bp & LPS 4 h, GolgiPlug 1 h \& & \\  & & stimulation: LPS 4 h, GolgiPlug 2 h & 46 & 53 \\ \hline
8 & GSE52529-GPL16791 & hour post serum-switch: 0 \& hour & \\  & & post serum-switch: 24 & 96 & 96 \\ \hline
9 & GSE52583-GPL13112 & age: Embryonic day 18.5 \& age: & \\  & & Embryonic day 14.5 & 80 & 45 \\ \hline
10 & GSE57872 & cell type: Glioblastoma \& cell type: Glioblastoma \& cell type: & \\ \hline
11 & GSE60749-GPL13112 & culture conditions: serum + LIF \& culture conditions: 2i + LIF & 174 & 94 \\ \hline
12 & GSE60749-GPL17021 & serum + LIF \& Selection in ITSFr, followed by expansion in N2 + bFGF/laminin & 93 & 54 \\ \hline
13 & GSE63818-GPL16791 & male \& female & 197 & 131 \\ \hline
14 & GSE66053-GPL18573 & Cells were added to the Fluidigm C1... \& Fixed cells were added to the Fluidigm C1... & 82 & 14 \\ \hline
15 & GSE71585-GPL13112 & input material: single cell \& & \\  & & tdtomato labeling: positive & 81 & 108 \\  & & (partially) & & \\ \hline
16 & GSE71585-GPL17021 & dissection: All \& input material: & \\  & & single cell & & \\ \hline
17 & GSE71982 & Vestibular epithelium \& Cochlear & \\  & & epithelium & & \\ \hline
18 & GSE77847 & AML-1 \& sample type: cKit + & & \\  & & Fit3 ITD/ITD,Dmrt3 af1/ -MxCre & & \\  & & AML-1 \& sample type: cKit + & 48 & 48 \\  & & Fit3 ITD/ITD,Dmrt3 af1/ -MxCre & & \\  & & AML-2 & & \\ \hline
19 & GSE79102 & patient 1 scRNA-seq \& & & \\  & & patient 2 scRNA-seq & & \\ \hline
20 & GSE81903 & retina ID: 1 Ra \& retina ID: 11a & 96 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of datasets used in our evaluation along with their chosen phenotypes that were eventually set at either 1 or 0, # refers to the number of 1s and 0s in the matrix. At the start of each testing iteration, we shuffle the rows (samples that were denoted by 1 and 0) of our matrix \(X_{j,i}\) to prevent any bias that may occur from having identical sample IDs. Next, we conduct a 10-fold cross-validation test where we take 10% of the samples (rows) as our testing set and the remaining 90% as our training set. We measure the system's time (which is our execution start time), and then we fit the training set into the glm package using its \"binomial\" function. Immediately after this step, we again measure the system's time (which is our execution end time). By subtracting the end time from the start time, we obtain the glm runtime at that specific iteration. The constructed model obtained from fitting glm is now used for prediction by employing the testing set. From this process, we can derive the confusion matrix that is the result of predicting the recently created model against the testing set. The confusion matrix is a \(2\times 2\) matrix. In some cases where we have only a one-dimension matrix, we programmatically force the creation of a \(2\times 2\) matrix to enable finding the actual and predicted values. We now calculate the Precision by dividing the total number of true positive values by the total number of true positive values and the total number of false positive values.