## 2 Materials and Design of Experiment
### Evaluation Procedure  
To access _auextlength_'s data in each dataset, we closely followed the steps provided by the Conquer repository authors (using experiment()[\"gene\"] and assay()[auextlength]). We downloaded the.rds file for each dataset and, by using the R programming language, we retrieved _ExperimentList_ instances that contained _RangedSummarizedExperiment_ for a gene-level object, which allowed us to access all available abundances; _TPM_ (transcripts per million), _gene count_, _length-scaled TPMs_, and _auextlength_. In our test, we chose genes' _auextlength_ because it perfectly fits our model. The _auextlength_ data at this stage constitute a matrix, \(X_{i,j}\), where \(i\) represents samples and \(j\) represents genes. To be able to use our binomial model, we individually explored each dataset to find two distinct phenotypic characteristics associated with the dataset's sample groups that could be used to label our outputs. We denote the first characteristic as 1 and the other as 0. Thus, sample IDs are replaced by either 1 or 0. For example, in the EMTAB2805 dataset, different phenotypes are associated with each group of samples. We chose only two phenotypes based on stages of the cell cycle: G1 and G2M. As a result, IDs of the samples associated with the first stage, G1, were replaced by 1 (True), and IDs of the samples associated with the second stage, G2M, were replaced by 0 (False). We eliminated any extra samples associated with other cell cycle stages, if any. In some datasets, such as in GSE80032, there is only one phenotype; thus, classification is impossible. Table 2 shows the current status of the \(X_{i,j}\) matrix. We now rotate the dimensions of \(X_{i,j}\) so that rows represent samples, and columns represent genes (Table 3). We then substitute each sample's ID with either 1 or 0, depending on their original classification in their original dataset status (Table 4). \begin{table}
\begin{tabular}{l l l l l l} \hline
**Data Set** & **ID** & **Organism** & **Brief Description** & **\# of Cells** & **Protocol** \\ \hline GSE71585- & Tasic2016 & _Mus musculus_ & cell type identification in primary & 1035 & SMARTer \\ GPL13112 & & _Mus musculus_ & cell type identification in primary & 749 & SMARTer \\ GSE71585- & Tasic2016 & _Mus musculus_ & utricular and cochlear sensory epithelia & 313 & SMARTer C1 \\ GPL17021 & & _Mus musculus_ & Dnmt3a loss of function in FIt3-ITD & 96 & SMARTer C1 \\ GSE71982 & & _Homo sapiens_ & different patients with & 181 & Smart-Seq2 \\ GSE71903 & & _Mus musculus_ & Keng4-crewstop-YFP X Thy1-stop-YFP & 384 & Smart-Seq2 \\  & & & Line \#1 mice & & \\  & & & in vitro cultured H7 embryonic stem & & \\ SRP073808 & & Koh2016 & _Homo sapiens_ & cells (WiCell) and H7-derived & 651 & SMARTer C1 \\  & & & & & \\ GSE94383 & & Lane2017 & _Mus musculus_ & LPS stimulated and unstimulated & 839 & Smart-Seq2 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._In Table 5, we list all selected datasets after we identified the appropriate phenotypes (column 3) that were used in our test. To increase the integrity of our evaluation, we conducted a Wilcoxon test on our matrix to find the \(p\)-values associated with each gene. At the time of testing, we ran into memory exhaustion errors caused by the large amount of genes that the matrix retained. Thus, we had to trim our matrix to include only 1000 genes that had the lowest \(p\)-values. The final form of our input matrix is \(X_{j,i}\), where \(j\) ranges from 1 to \(n\) (the total number of phenotypes represented by either 1s or 0s) and \(i\) ranges from 1 to 1000 (genes with the lowest \(p\)-value). At this point, our matrix has been completely normalized; thus, we can proceed into testing the glm R package over 100 iterations. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **ENSMUSG...1.4** & **ENSMUSGO...3.15** & **ENSMUSG...28.14** & **ENSMUSG...31.15** & **ENSMUSG...37.16** & \\ \hline
**1** & 3046.720 & 466.049 & 1531.720 & 910.4161 & 2715.72