update md

Author: xingzhongyu

README.md

@@ -262,7 +262,7 @@ pip install -e .
 | Neural Network          | ACTINN        | ACTINN: automated identification of cell types in single cell RNA sequencing.                                    | 2020 | ✅      |
 | Hierarchical Clustering | SingleR       | Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.           | 2019 | P1      |
 | SVM                     | SVM           | A comparison of automatic cell identification methods for single-cell RNA sequencing data.                       | 2018 | ✅      |
-| GNN                     | scHeteroNet   | scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection | 2018 | ✅      |
+| GNN                     | scHeteroNet   | scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection | 2025 | ✅      |
 
 | Model         | GSE67835 Brain<br>(DANCE 2.0/DANCE 1.0/Original) | CD8+ TIL atlas<br>(DANCE 2.0/DANCE 1.0/Original) | GSE123813 Immune<br>(DANCE 2.0/DANCE 1.0/Original) | CD4+ TIL atlas<br>(DANCE 2.0/DANCE 1.0/Original) | GSE182320 (Tissue- Spleen)<br>(DANCE 2.0/DANCE 1.0/Original) | Evaluation Metric |
 | ------------- | ------------------------------------------------ | ------------------------------------------------ | -------------------------------------------------- | ------------------------------------------------ | ------------------------------------------------------------ | ----------------- |
@@ -382,8 +382,7 @@ pip install -e .
 | Bayesian                         | BayesSpace | Spatial transcriptomics at subspot resolution with BayesSpace                                                                                                                 | 2021 | P1      |
 | Pseudo-space-time (PST) Distance | stLearn    | stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues | 2020 | ✅      |
 | Heuristic                        | Louvain    | Fast unfolding of community hierarchies in large networks                                                                                                                     | 2008 | ✅      |
-| GNN                              | EfNST      | A composite scaling network of EfficientNet for improving spatial domain identification performance                                                                           | 2024 |         |
-| ✅                               |            |                                                                                                                                                                               |      |         |
+| GNN                              | EfNST      | A composite scaling network of EfficientNet for improving spatial domain identification performance                                                                           | 2024 | ✅      |
 
 | Model   | 151676 (DANCE 2.0/DANCE 1.0/Original) | Sub MBA (DANCE 2.0/DANCE 1.0/Original) | Evaluation Metric |
 | ------- | ------------------------------------- | -------------------------------------- | ----------------- |

examples/tuning/custom-methods/328_138/results/params/0_best.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,params.0.FilterGenesPercentile.max_val,params.0.FilterGenesPercentile.min_val,params.0.FilterGenesPercentile.mode,params.1.ColumnSumNormalize.eps,params.1.ColumnSumNormalize.mode,params.2.FilterGenesRegression.method,params.2.FilterGenesRegression.num_genes,params.3.CellPCA.n_components,params.3.CellPCA.svd_solver,test_acc,train_acc
-w0rno5wd,14.312397241592407,0,1754737525.2395022,12,0.6818181872367859,98,1,cv,0.5,minmax,enclasc,4284,446,arpack,0.3115941882133484,0.9618320465087892
-3xw5xrnw,11.834450244903564,0,1754737490.5787213,10,0.3636363744735718,95,7,cv,0.5,normalize,enclasc,1266,449,auto,0.06521739065647125,0.7251908183097839
+h8j0oo74,,,,10,,96,1,cv,0.3,standardize,seurat3,9419,636,arpack,,
+69ew4oa2,13.29924178123474,0.0,1755663986.4532037,11,0.7121211886405945,98,8,rv,0.7,minmax,scmap,5388,227,arpack,0.36231884360313416,0.8969465494155884

examples/tuning/custom-methods/328_138/results/params/1_best.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,params.0.FilterGenesPercentile.max_val,params.0.FilterGenesPercentile.min_val,params.0.FilterGenesPercentile.mode,params.1.ColumnSumNormalize.eps,params.1.ColumnSumNormalize.mode,params.2.FilterGenesRegression.method,params.2.FilterGenesRegression.num_genes,params.3.CellSVD.algorithm,params.3.CellSVD.n_components,test_acc,train_acc
-ex4i83qv,13.576059818267822,0.0,1754737592.5319088,11,0.7121211886405945,99,8,rv,0.5,minmax,seurat3,6106,arpack,218,0.3550724685192108,0.885496199131012
-nou13et9,,,,9,,97,1,cv,0.1,l2,scmap,8594,arpack,590,,
+9bwt95uk,13.6163969039917,0.0,1755664066.86501,11,0.3636363744735718,95,6,var,0.5,normalize,scmap,1609,randomized,539,0.06521739065647125,0.6717557311058044
+jhryorbj,,,,9,,98,4,sum,0.1,minmax,scmap,7435,arpack,793,,

examples/tuning/custom-methods/328_138/results/pipeline/best_test_acc.csv

@@ -1,3 +1,3 @@
 id,_runtime,_step,_timestamp,_wandb_runtime,acc,pipeline.0.filter.gene,pipeline.1.normalize,pipeline.2.filter.gene,pipeline.3.feature.cell,test_acc,train_acc
-8vujyei2,13.406305074691772,0,1754737445.079577,11,0.5,FilterGenesPercentile,ColumnSumNormalize,FilterGenesRegression,CellSVD,0.0942028984427452,0.6412213444709778
-u9e1l0us,13.42415475845337,0,1754737417.9047508,11,0.5151515007019043,FilterGenesPercentile,ColumnSumNormalize,FilterGenesRegression,CellPCA,0.10144927352666856,0.6564885377883911
+nxe1pfd6,11.684813261032104,0,1755663959.4838474,9,0.5,FilterGenesPercentile,ColumnSumNormalize,FilterGenesRegression,CellSVD,0.0942028984427452,0.6412213444709778
+dduxrl3d,13.366324424743652,0,1755663933.7720325,11,0.5151515007019043,FilterGenesPercentile,ColumnSumNormalize,FilterGenesRegression,CellPCA,0.10144927352666856,0.6564885377883911