Fig 1.
Illustration of the WMC workflow.
( A) Raw[0pc][-1pc]Figure 1,3,4 - The quality of the image is poor and pixelated. Hence please supply a corrected version with an unpixelated typeface. The font size of the image is below 6 pt which affects the readability of the image. Hence, please supply a corrected version with font size above 6 pt. scRNA-seq data matrix with row as transcripts and columns as individual cells. ( B) Quality control diagrams, demonstrating the process of removing unqualified cells and transcripts involving mitochondrial genes. ( C) Logarithm transformation of the raw matrix. ( D) Applying M-band DWT on the logarithm transformed matrix. ( E) PCA-based dimension reduction on matrices with and without DWT. ( F) Visualization of clustering based on different methods, with (F1) for UMAP in Seurat, (F2) for SC3 and (F3) for HGC. ( G) Assessing the performance of WMC via intersection analysis and ARI computation.
Fig 2.
Multi-view of clusters of the innate lymphoid cell differentiation dataset.
( A) UMAP visualization of cell types based on data matrix without DWT. ( B)-( D) are clusters under wavelet analysis, with ( B) for 2-band DWT, ( C) for 3-band DWT, and ( D) for 4-band DWT.
Fig 3.
Consensus matrices among different cell phases of ILC differentiation dataset by Wavelet-SC3 method.
(A) Consensus matrix using SC3 without DWT. ( B)-( D) are consensus matrices under Wavelet-SC3, with ( B) for 2-band DWT, ( C) for 3-band DWT, and ( D) for 4-band DWT.
Fig 4.
Multi-view hierarchical clusters on ILC dataset using Wavelet-HGC method.
( A) Dendrogram for ILC dataset without DWT. ( B)-( D) are dendrograms under wavelet analysis, with ( B) for 2-band DWT, ( C) for 3-band DWT, and ( D) for 4-band DWT.
Fig 5.
Assessing the performance of WMC on the breast cancer dataset.
The x-axes of panels (A) through (C) represents the total number of genes in each component and the y-axes shows the number of genes in each intersection set. The cyan dots represent the number of genes belonging to the corresponding components, for gray dots vice versa. ( A) The intersection of genes between original data and its different wavelet transformed components using 2-band DWT, while ( B) for 3-band and ( C) for 4-band DWT, respectively.
Table 1.
Average ARI and NMI for clusters of M-band DWT components
Fig 6.
Average ARI results for breast cancer datasets on different subtypes of cancer cells.