PMC8855158_01
Flowchart of our proposed methodology to perform differential network analysis from scRNA-seq data. The input is a preprocessed scRNA-seq dataset and the output consists of the estimated control and treatment gene-gene correlations and the differential network with significant gene-gene differences between treatment groups. *Note: The bootstrap method is our recommended technique for model parameter estimation, but parameter estimation could alternatively be performed with a single posterior optimization or via HMC.
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F1
Figures of the individual GCNs with these 10 DCGs for the non-hospitalized and hospitalized groups are provided in the Supplementary Figures S1-S4.
Heatmaps of the "true" correlation structures of control group (row 1) and treatment group (row 2) for Network Structure A and Network Structure B. The colored cells in these heatmaps represent the magnitude and direction of gene-gene correlations. Row 3 contains the differences between the control and treatment correlation structures. The colored cells in row 3 indicate the magnitude and direction of the differences in gene-gene associations across the two groups (control - treatment).
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F2
Figures of the individual GCNs with these 10 DCGs for the non-hospitalized and hospitalized groups are provided in the Supplementary Figures S1-S4.
Example proportions of significant differential network edges (number of significant differential network edges divided by ) detected by SFM-SHS and SFM-DHS with bootstrap estimation for different choices of F from Sim 1. The   symbols denote the considered "peak" points across the different number of factor choices.
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F3
Figures of the individual GCNs with these 10 DCGs for the non-hospitalized and hospitalized groups are provided in the Supplementary Figures S1-S4.
UpSet plot of the number of differential edges determined by four differential network methods in the SARS-CoV-2 case study dataset. The numbers in parentheses represent the total number of differential edges detected by the corresponding method. Results displayed for SFM-SHS and SFM-DHS are from the bootstrap estimation procedure.
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F4
The UpSet plot for the intersection between the differential edges detected by each method is displayed in Figure 4. From Figure 4, we see that the methods performed quite differently as only 388 edges were common across all four methods. Figures of the individual GCNs with these 10 DCGs for the non-hospitalized and hospitalized groups are provided in the Supplementary Figures S1-S4.
UpSet plot of the top DCGs determined by four differential network methods in the SARS-CoV-2 case study dataset. Results displayed for SFM-SHS and SFM-DHS are from the bootstrap estimation procedure.
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F5
In Figure 5, the UpSet plot for the intersection between the top DCGs detected by each differential network method is displayed.
Differential network estimated by SFM-SHS with the bootstrap procedure for the SARS-CoV-2 case study dataset. Edges displayed are for 10 DCGs. The 7 unique top DCGs identified by both SFM-SHS and SFM-DHS are listed in black and the 3 top DCGs identified by all considered methods are listed in green. Each edge color represents the group with the larger correlation magnitude.
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F6
Lastly, we visualized the differential networks estimated from SFM-SHS and SFM-DHS in Figures 6, 7, respectively.
Differential network estimated by SFM-DHS with the bootstrap procedure for the SARS-CoV-2 case study dataset. Edges displayed are for 10 DCGs. The 7 unique top DCGs identified by both SFM-SHS and SFM-DHS are listed in black and the 3 top DCGs identified by all considered methods are listed in green. Each edge color represents the group with the larger correlation magnitude.
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F7
Lastly, we visualized the differential networks estimated from SFM-SHS and SFM-DHS in Figures 6, 7, respectively.