- Title
-
Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets
- Authors
- Shainer, I., Stemmer, M.
- Source
- Full text @ BMC Genomics
Alignment results of all datasets (Table |
Impact of reference choice and alignment pipeline on zebrafish scRNA-seq datasets. |
Violin-plots showing comparison of gene detection per cell between kallisto and Cell Ranger across selected examples ( |
Downstream analysis of the dr_pineal_s2 dataset. a 2D visualization using UMAP of Cell Ranger analyzed clusters. Each point represents a single cell, colored according to cell type. The cells were clustered into 13 distinct types, which were defined according to their unique transcriptomes (Additional file 2). b Expression profile of marker genes according to cluster [7] of (a). Cone- and rod-like PhRs are defined according to their transducin protein subunits (gnat1 and gngt1 in rods vs. gnat2 and gngt2a in cones [26] and opn1lw1 and parietopsin in cone-like). c 2D visualization using UMAP of kallisto analyzed clusters. Each point represents a single cell, colored according to cell type. The cells were clustered into 14 distinct types, which were defined according to their unique transcriptomes (Additional file 2). d Expression profile of marker genes according to cluster [7] of (c). Two different populations of cone-like PhRs are detected. Both express the cone unique transducin protein-subunits, but differ in the expressed opsin (opn1lw1 in red-cones, and parietopsin in green-cones), demonstrating the bi-chromatic photoreception characteristic of the pineal gland. col14a1b was only detected in the kallisto dataset and is the strongest DE marker within the red-cone cluster (c, d) |