Integrative Seurat: we employed Seurat's standard scRNA-seq integration pipeline. We used standard workflow for the integration of scRNA-seq datasets. We used  canonical correlation analysis (‘CCA’) as well as reciprocal PCA (‘RPCA’) to identify anchors. We followed Seurat's standard scRNA-seq integration pipeline with slightly changed parameters to avoid errors caused by a small number of samples in either dataset.



Supplemental_code/CCA: Here, we used canonical correlation analysis (‘CCA’) to identify anchors
Integrative_Seurat_CCA_Study1_count: This pipeline is for Study 1 Count data.
Integrative_Seurat_CCA_Study2_TPM: This pipeline is for Study 2 TPM data. In this pipeline we take log transformed TPM data and skip normalization step as TPM data is already length normalized. 



Supplemental_code/RPCA: Here, we used reciprocal PCA (‘RPCA’) to identify anchors
Integrative_Seurat_RPCA_Study1_count: This pipeline is for Study 1 Count data.
Integrative_Seurat_RPCA_Study2_TPM: This pipeline is for Study 2 TPM data. In this pipeline we take log transformed TPM data and skip normalization step as TPM data is already length normalized. 