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Table 2 Network variations used for technical assessment. NB: for all networks undefined correlations are set to 0

From: Exploiting single-cell expression to characterize co-expression replicability

Network Method Property tested
UMI • Spearman correlation of UMI data to make a network for each batch
• Batch networks are rank standardized then aggregated
Do UMI expression estimates produce functional co-expression?
CPM • Spearman correlation of CPM normalized data to make a network for each batch
• Batch networks are rank standardized then aggregated
What types of artifacts can sample standardization introduce?
Batch-affected • Spearman correlation across all samples using UMI data
• Rank standardization
What impact does co-variation across batches have?
Binary expression • All non-zero values are set to 1
• Spearman correlation to make a network for each batch
• Batch networks are rank standardized then aggregated
How informative is gene representation?
Combat • UMI data is log2 transformed then Combat is run for each celltype (ChC and Pv)
• Spearman correlation to make a network for each cell type
• Aggregate is made from the addition of rank-standardized ChC and Pv networks
Do methods for removing batch effects alter co-expression?
Removal of unwanted variation (RUV) • UMI data is log2 transformed then RUV is run for each cell type (ChC and Pv) using ERCC spike-ins as control genes
• Spearman correlation to make a network for each cell type
• Aggregate is made from the addition of rank-standardized ChC and Pv networks
What are the combined influence of batch correction and ERCC-based normalization?
UMI excluding zeroes • All zeroes are set to NA
• Networks are made for each batch using pairwise Spearman correlation
• Batch networks are rank standardized then aggregated
How does removing zeroes alter network topology and performance?