Genome Biol. Furthermore, to evaluate the robustness of our conclusions, different number of cells (cell pool sizes) are used to build the pseudo-bulk mixtures. expression profiles of constituent cell types. Nat. Nat. Additionally, we generated scenarios where cells from a given individual were used only in one split (training or test) by assigning half of the samples to each split prior to selecting the cells based on the cell type. 17)). 20, 295 (2019). 7a). Commun. Genome Biol. To note, it was not possible to evaluate MuSiC and SCDC because the 10x scRNA-seq data used as reference came from only one individual. As with the example of the three-cell-line mixture, we observed cell types with transcriptomic profiles that were highly similar (Supplementary Figure S4A); we therefore adopted a tree-guided approach for deconvolution in order to distinguish the closely related cell types (Supplementary Figure S4B,C). Specifically in the Baron and PBMC datasets, the use of the top 2 markers per cell type (top_n2) led to a) optimal results when used with DSA; b) similar results as using the bottom_50p_AveExpr or bottom_50p_logFC with ordinary linear regression strategies; c) worse results than random when used with penalized regression strategies (lasso, ridge, elastic net, DCQ) and CIBERSORT. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Finally, once the number of cells to be picked up from specific cell types was determined, the cells were randomly selected without replacement (= a given cell can only be present once in a mixture). 11, R25 (2010). With the exception of EPIC26, DeconRNASeq20, and DSA17, the choice of normalization strategy does not have a substantial impact on the deconvolution results (evidenced by narrow boxplots). CAS  DWLS, MuSiC and SCDC consistently showed the highest performance (comparable to the top-performers from the bulk methods, see also Fig. Among the retained ones, those with absolute fold changes greater or equal to 2 with respect to the second cell type with highest expression and BH adj p-value < 0.05 were kept as markers in all three pancreatic datasets. Robust enumeration of cell subsets from tissue expression profiles. Required fields are marked *. As stated in its original publication, EPIC assumes transcripts per million (TPM) normalized expression values as input. Each transformed output file was further scaled/normalized with the approaches listed on Table 3. 12902 USF Magnolia Drive Tamp a, FL 33612 T ampa, FL 33612, USA 10. The researchers benchmarked the performance of spatialDWLS by comparing with a number of existing deconvolution methods using both real and simulated datasets, and they found that spatialDWLS outperformed the other methods in terms of accuracy and speed. Therefore, other strategies that aim to change the observations so that they follow a normal distribution (= normalization) may be preferred. Since the kidney and PBMC datasets contained more closely related cell types, the fold-change threshold was lowered to 1.8 and 1.5, respectively. This is the other aspect of how spatial and scRNA-seq data complement each other. Pathol. Five different datasets coming from different single-cell isolation techniques (FACS and droplet-based microfluidics) and encompassing both full-length (Smart-Seq2) and tag-based library preparation protocols (3′-end with UMIs) were used throughout this article (see Table 1). 11, 34 (2019). They observed high pairwise correlations between the estimated cell type proportions with and without standardizing the data, suggesting a neglectable effect. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. 3). Silverman et al.33 showed that using log counts per million with sparse data strongly distorts the difference between zero and non-zero values and Townes et al.34 showed the same when log-normalizing UMIs. Each boxplot contains all normalization strategies that were tested in combination with a given marker strategy across the different bulk deconvolution methods. In the meantime, to ensure continued support, we are displaying the site without styles Furthermore, five deconvolution methods that use scRNA-seq as reference have been evaluated: deconvSeq49, MuSiC24, DWLS23, Bisque50 and SCDC25. The identification of cell type-specific genes (markers) is an essential step for the deconvolution of the cellular fractions, primarily, from the gene expression data of a bulk sample. Penalized regression approaches, including lasso, ridge, elastic net regression, and DCQ performed slightly worse than the ones described above (median RMSE ~ 0.1). 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