Additional file 1 of scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data
Creators: name: Wang, Lifei
name: Nie, Rui
name: Zhang, Jiang
name: Cai, Jun
Publisher: name: figshare
Year: 2024
Subjects: Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified

description: Additional file 1.Figure S1. The performance for single-cell type recognition in the scCapsNet-mask is comparable to that of scCapsNet and other machine learning methods. The training accuracies and testing accuracies of scCapsNet-mask (mask), scCapsNet, neural network, random forest and support vector machine (SVM) on the human kidney dataset are plotted. The box-and-whisker plots drawn by boxplot from R show the training and testing accuracies in nine replicates of each method. Figure S2. The average coupling coefficients show that the mask leads to the one-to-one correspondence between primary capsules and type capsules in the mRBC dataset. The average coupling coefficients (heatmaps) generated by scCapsNet-mask for the mRBC dataset with the cell types listed above. The row represents type capsules and the column represents primary capsules in each heatmap. These heatmaps show that applying the mask leads to a one-to-one correspondence between primary capsules and type capsules. Figure S3. The average coupling coefficients show that the mask leads to the one-to-one correspondence between primary capsules and type capsules in the human kidney dataset. The average coupling coefficients (heatmaps) generated by scCapsNet-mask for the human kidney dataset with cell types listed above. The row represents type capsules and the column represents primary capsules in each heatmap. These heatmaps show that applying the mask leads to a one-to-one correspondence between primary capsules and type capsules. Figure S4. The average coupling coefficients show that lacking the mask leads to the complex correspondences between primary capsules and type capsules in the mRBC dataset. The average coupling coefficients (heatmaps) generated by scCapsNet-mask for the mRBC dataset with the cell types listed above. The row represents type capsules and the column represents primary capsules in each heatmap. These heatmaps show that lacking mask leads to the complex correspondence between primary capsules and type capsules. Figure S5. The average coupling coefficients show that lacking the mask leads to the complex correspondences between primary capsules and type capsules in the human kidney dataset. The average coupling coefficients (heatmaps) generated by scCapsNet-mask for the human kidney dataset with cell types listed above. The row represents type capsules and the column represents primary capsules in each heatmap. These heatmaps show that lacking the mask leads to the complex correspondence between primary capsules and type capsules. Figure S6. The scCapsNet-mask could identify cell type associated genes along Principal Component 1 in mRBC dataset. A Each colored line represents the prediction accuracy for cell types along principal component in B. The y axis represents prediction accuracy, the x axis represents principal component in B and the dotted lines are corresponding to the chosen genes of blue dots in B. In the dotted lines, recognition accuracy degrades close to 0 for corresponding cell type listed above whereas other cell types are not significantly affected. B The plot depicts the two-dimensional PCA on the weight matrix for the primary capsule. Each dot represents a gene and the blue dots are chosen as cell type related genes (dotted line associated genes in A). The colored stars with labels represent the marker genes for each cell type (Prkca, Apoe, Sox6, Igfn1, Lect1, Slitrk5, Pcdh17, Nnat, Wls, Syt2, Lrrtm1, Erbb4, Chrm2, Col11a1, Serpini1). Figure S7. The fate bias inferred by FateID is less robust against the missing intermediate progenitor stages. A Left: The FateID trained on erythroid cell, monocytes, and dendritic cells. Middle: the fate bias of precursor and HSC estimated by FateID. Right: the fate bias of HSCs estimated by FateID when precursors are absent. B The composition of fate bias is dramatically changed for HSC population after the intermediate progenitor population is missing. Figure S8. Predicted spatial localization of cell types by RCTD and scCapsNet-mask in the Slide-seqV2 hippocampus. Left: CA1, CA3, Choroid, Dentate in RCTD. Right: CA1, CA3, Choroid, Dentate in scCapsNet-mask. Figure S9. Predicted spatial localization of cell types by RCTD and scCapsNet-mask in the Slide-seqV2 hippocampus. Left: Entorhinal, Interneuron, Neuron.Slc17a6, Oligodendrocyte in RCTD. Right: Entorhinal, Interneuron, Neuron.Slc17a6, Oligodendrocyte in scCapsNet-mask. Figure S10. Predicted spatial localization of cell types by RCTD and scCapsNet-mask in the Slide-seqV2 hippocampus. Left: Microglia_Macrophages, Mural, Neurogenesis, Endothelial_Stalk, Endothelial_Tip in RCTD. Right: Microglia_Macrophages, Mural, Neurogenesis, Endothelial_Stalk, Endothelial_Tip in scCapsNet-mask. Figure S11. Predicted spatial localization of cell types by RCTD and scCapsNet-mask in the Slide-seqV2 hippocampus. Left: Astrocyte, Cajal_Retzius, Entorhinal, Polydendrocyte in RCTD. Right: Astrocyte, Cajal_Retzius, Entorhinal, Polydendrocyte in scCapsNet-mask.