This Github repository holds data, Notebooks and results of running SDePER on both Simulated and Real datasets, and Notebooks for figure panels in manuscript, as well as the codes for running other cell type deconvolution methods.
For source code of SDePER, please refer Github repository SDePER.
Homepage: https://az7jh2.github.io/SDePER/.
Full Documentation for SDePER is available here.
Example data and Analysis using SDePER are summarized in this page.
- Simulation: including STARmap-based simulation study, study with downsampling Oligo cells in reference, and simulation based on STARmap data but only including a subset of cell types.
- Simulation_seq_based: including sequencing-based simulation study, and high density sequencing-based simulation study.
- RealData: including analysis of 4 real datasets.
- Ablation: Ablation test of SDePER on 4 simulated datasets.
- Figures: Notebooks for figures in simulation and real data analysis in manuscript.
- Run_other_methods: codes for running other cell type deconvolution methods.
If you use SDePER, please cite:
Yunqing Liu, Ningshan Li, Ji Qi et al. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 25, 271 (2024). https://doi.org/10.1186/s13059-024-03416-2
To improve accessibility for colorblind individuals, we revised the color palette in the manuscript, updating the color codes:
- SDePER: from
#E6194B to
#DC267F
- SpatialDWLS: from
#3CB44B to
#44AA99
- SPOTlight: from
#4363D8 to
#DDCC77
- DestVI: from
#911EB4 to
#77AADD
In Ablation test:
- NO PlatEffRmv: from
#808000 to
#BBCC33
- NO pseudo spots: from
#BCF60C to
#99DDFF
- NO LASSO: from
#FABEBE to
#FFAABB
- NO Laplacian: from
#008080 to
#EE8866