Increase time resources (#27)

* Increase mnnpy time

* Increase batchelor_mnn_correct time

* Increase scanorama time

* Increase UCE time

* Set remaining methods to hightime

* Set scgpt_finetune, liger to hightime

---------

Co-authored-by: Robrecht Cannoodt <[email protected]>

src/methods/batchelor_fastmnn/config.vsh.yaml

@@ -11,9 +11,9 @@ description: |
   4. Correct the cells in the target batch towards the reference, using locally weighted correction vectors.
   5. Merge the corrected target batch with the reference, and repeat with the next target batch.
 references:
-  # Haghverdi, L., Lun, A., Morgan, M. et al. 
-  # Batch effects in single-cell RNA-sequencing data are 
-  # corrected by matching mutual nearest neighbors. 
+  # Haghverdi, L., Lun, A., Morgan, M. et al.
+  # Batch effects in single-cell RNA-sequencing data are
+  # corrected by matching mutual nearest neighbors.
   # Nat Biotechnol 36, 421–427 (2018). https://doi.org/10.1038/nbt.4091
   doi: 10.1038/nbt.4091
 links:
@@ -35,4 +35,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/batchelor_mnn_correct/config.vsh.yaml

@@ -5,9 +5,9 @@ summary: Mutual nearest neighbors correction
 description: |
   Correct for batch effects in single-cell expression data using the mutual nearest neighbors method.
 references:
-  # Haghverdi, L., Lun, A., Morgan, M. et al. 
-  # Batch effects in single-cell RNA-sequencing data are 
-  # corrected by matching mutual nearest neighbors. 
+  # Haghverdi, L., Lun, A., Morgan, M. et al.
+  # Batch effects in single-cell RNA-sequencing data are
+  # corrected by matching mutual nearest neighbors.
   # Nat Biotechnol 36, 421–427 (2018). https://doi.org/10.1038/nbt.4091
   doi: 10.1038/nbt.4091
 links:
@@ -30,4 +30,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/bbknn/config.vsh.yaml

@@ -48,4 +48,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, lowcpu]
+      label: [hightime, midmem, lowcpu]

src/methods/combat/config.vsh.yaml

@@ -33,4 +33,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, highmem, lowcpu]
+      label: [hightime, highmem, lowcpu]

src/methods/geneformer/config.vsh.yaml

@@ -56,4 +56,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, midcpu, gpu]
+      label: [hightime, midmem, midcpu, gpu]

src/methods/harmony/config.vsh.yaml

@@ -3,7 +3,7 @@ name: harmony
 label: Harmony
 summary: Fast, sensitive and accurate integration of single-cell data with Harmony
 description: |
-  Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. 
+  Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets.
   It is especially useful for large single-cell datasets such as single-cell RNA-seq.
 references:
   # Korsunsky, I., Millard, N., Fan, J. et al.
@@ -30,4 +30,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/harmonypy/config.vsh.yaml

@@ -3,7 +3,7 @@ name: harmonypy
 label: Harmonypy
 summary: harmonypy is a port of the harmony R package by Ilya Korsunsky.
 description: |
-  Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. 
+  Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets.
   It is especially useful for large single-cell datasets such as single-cell RNA-seq.
 references:
   # Korsunsky, I., Millard, N., Fan, J. et al.
@@ -31,4 +31,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/liger/config.vsh.yaml

@@ -3,9 +3,9 @@ name: liger
 label: LIGER
 summary: Linked Inference of Genomic Experimental Relationships
 description: |
-  LIGER or linked inference of genomic experimental relationships uses iNMF 
-  deriving and implementing a novel coordinate descent algorithm to efficiently 
-  do the factorization. Joint clustering is performed and factor loadings are 
+  LIGER or linked inference of genomic experimental relationships uses iNMF
+  deriving and implementing a novel coordinate descent algorithm to efficiently
+  do the factorization. Joint clustering is performed and factor loadings are
   normalised.
 references:
   doi: 10.1016/j.cell.2019.05.006
@@ -31,4 +31,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/mnnpy/config.vsh.yaml

@@ -57,4 +57,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, lowcpu, highmem]
+      label: [hightime, lowcpu, highmem]

src/methods/pyliger/config.vsh.yaml

@@ -4,9 +4,9 @@ label: pyliger
 summary: Python implementation of LIGER (Linked Inference of Genomic Experimental
   Relationships
 description: |
-  LIGER (installed as rliger) is a package for integrating and analyzing multiple 
-  single-cell datasets, developed by the Macosko lab and maintained/extended by the 
-  Welch lab. It relies on integrative non-negative matrix factorization to identify 
+  LIGER (installed as rliger) is a package for integrating and analyzing multiple
+  single-cell datasets, developed by the Macosko lab and maintained/extended by the
+  Welch lab. It relies on integrative non-negative matrix factorization to identify
   shared and dataset-specific factors.
 references:
   doi: 10.1016/j.cell.2019.05.006
@@ -37,4 +37,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowcpu, highmem, midtime]
+      label: [lowcpu, highmem, hightime]

src/methods/scalex/config.vsh.yaml

@@ -34,4 +34,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [lowmem, lowcpu, midtime]
+      label: [lowmem, lowcpu, hightime]

src/methods/scanorama/config.vsh.yaml

@@ -34,4 +34,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, highmem, lowcpu]
+      label: [hightime, highmem, lowcpu]

src/methods/scanvi/config.vsh.yaml

@@ -54,4 +54,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, lowmem, lowcpu, gpu]
+      label: [hightime, lowmem, lowcpu, gpu]

src/methods/scgpt_zeroshot/config.vsh.yaml

@@ -65,4 +65,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, midcpu, gpu]
+      label: [hightime, midmem, midcpu, gpu]

src/methods/scimilarity/config.vsh.yaml

@@ -32,4 +32,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, lowcpu]
+      label: [hightime, midmem, lowcpu]

src/methods/scprint/config.vsh.yaml

@@ -79,4 +79,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, midcpu, gpu]
+      label: [hightime, midmem, midcpu, gpu]

src/methods/scvi/config.vsh.yaml

@@ -50,4 +50,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, midmem, lowcpu, gpu]
+      label: [hightime, midmem, lowcpu, gpu]

src/methods/uce/config.vsh.yaml

@@ -43,4 +43,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [midtime, highmem, midcpu, gpu]
+      label: [hightime, highmem, midcpu, gpu]