def main():
actions = (
('action1', 'list, open, read, close files in dirs under purge policy'),
('action2', 'use find touch command to avoid purge on a dir'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('cMLM', 'Perform GWAS using compressed mixed linear model'),
('SUPER', 'Perform GWAS using SUPER'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('RunMACS2', 'run macs2'),
('FetchSeqs', 'extract sequences of peak reagions'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('GLM', 'Performe GWAS using general linear model'),
('MLM', 'Performe GWAS using mixed linear model '),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('DependentCitations', 'calculate dependent citations'),
('DownloadCitations', 'download all related citations from google scholar searching results'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('hmp2vcf', 'transform hapmap format to vcf format'),
('hmp2bimbam', 'transform hapmap format to BIMBAM format (GEMMA)'),
('hmp2numRow',
'transform hapmap format to numeric format in rows(gapit and farmcpu), more memory'
),
('hmp2numCol',
'transform hapmap format to numeric format in columns(gapit and farmcpu), less memory'
),
('hmp2MVP', 'transform hapmap format to MVP genotypic format'),
('genKinship', 'using gemma to generate centered kinship matrix'),
('genPCA', 'using tassel to generate the first N PCs'),
('reorgnzTasselPCA',
'reorganize PCA results from TASSEL so it can be used in other software'
),
('reorgnzGemmaKinship',
'reorganize kinship results from GEMMA so it can be used in other software'
),
('genGemmaPheno', 'reorganize normal phenotype format to GEMMA'),
('ResidualPheno',
'generate residual phenotype from two associated phenotypes'),
('combineHmp', 'combine split chromosome Hmps to a single large one'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('farmcpu', 'Perform GWAS using FarmCPU (muti-loci mixed model)'),
('pdf2png', 'convert pdf image to png format'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('keras', 'using keras model to make prediction'),
('dpp', 'using dpp model to make prediction'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('three2two', 'convert 3d npy to 2d'),
('hyp2arr', 'convert hyperspectral images to a numpy array'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('hmp2vcf', 'transform hapmap format to vcf format'),
('hmp2BIMBAM', 'transform hapmap format to BIMBAM format (GEMMA)'),
('hmp2numRow', 'transform hapmap format to numeric format in rows(gapit and farmcpu), more memory'),
('hmp2numCol', 'transform hapmap format to numeric format in columns(gapit and farmcpu), less memory'),
('hmp2MVP', 'transform hapmap format to MVP genotypic format'),
('hmp2ped', 'transform hapmap format to plink ped format'),
('FixPlinkPed', 'fix the chr names issue and Convert -9 to 0 in the plink map file'),
('ped2bed', 'convert plink ped format to binary bed format'),
('genKinship', 'using gemma to generate centered kinship matrix'),
('genPCA', 'using tassel to generate the first N PCs'),
('subsampling', 'resort hmp file by extracting part of samples'),
('downsampling', 'using part of SNPs when dataset is too large'),
('LegalHmp', 'convert illegal genotypes in hmp file to legal genotypes'),
('SortHmp', 'Sort hmp position in wired tassle way'),
('reorgnzTasselPCA', 'reorganize PCA results from TASSEL so it can be used in other software'),
('reorgnzGemmaKinship', 'reorganize kinship results from GEMMA so it can be used in other software'),
('genGemmaPheno', 'reorganize normal phenotype format to GEMMA'),
('ResidualPheno', 'generate residual phenotype from two associated phenotypes'),
('combineHmp', 'combine split chromosome Hmps to a single large one'),
('IndePvalue', 'calculate the number of independent SNPs and estiamte the bonferroni p value'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (('upload', 'load images to zooniverse'),
('export', 'Get annotation and other exports'),
('manifest',
'Generate a manifest for zooniverse subject set upload'))
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('freebayes', 'call SNPs using freebayes'),
('samtools', 'call SNPs using samtools'),
('gatk', 'call SNPs using gatk'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('regression', 'using pretrained model to solve regression problems'),
('prediction', 'make predictions using the trained model'),
('classification', 'using pretrained model to solve classification problems'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('genTissueSpecies', 'generate leaf, stem and root csv files'),
('filterSpeciesTreatment',
'remove DHS with too many 0s either in species or treatment'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('submit', 'submit a batch of jobs or all of them'),
('quickjob', 'create a quick slurm job'),
('cancel', 'canceal running, pending or all jobs'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('keras_cnn', 'train vgg model'),
('keras_snn', 'train simple neural network'),
('dpp', 'train dpp model'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('ExtractRGBs', 'extract images from project folder'),
('Info', 'summary of image data under the project folder'),
('List', 'list specified image folders'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('cpu', 'request cpu node'),
('gpu', 'request gpu node'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('PrepareData',
'Prepare the data including Genotype, Map, Kinship, and PC files for running MVP.'
),
('RunMVP', 'Run both MLM and FarmCPU models in MVP.'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('GLM', 'Performe GWAS using general linear model'),
('MLM', 'Performe GWAS using mixed linear model '),
# Visualization
('Manhattan', 'Draw the Manhanttan plot using GEMMA results'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('Plot', 'plot training model history'),
('Predict', 'using trained neural network to make prediction'),
('Imgs2Arrs',
'convert hyperspectral images under a dir to a numpy array object'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('crop', 'crop sorghum images based their zoom levels'),
('pdf2png', 'convert pdf to png format'),
('downsize', 'down size the image'),
('PlantHull', 'detect he convex hull of a image'),
('PlantHullBatch', 'run PlantPixels script on all images'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (('asset_types', 'print all available asset types'),
('item_types', 'print all available item types'),
('stat', 'check available imagery counts'),
('quick_search',
'perform quick search to get all the target ids'),
('activate', 'activate assets for downloading'),
('download', 'download activated links'))
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('toy', 'random pick up some images for testing purporse'),
('divide', 'divide a large number of images to sevearl subsets'),
('upload', 'load images to zooniverse'),
('BatchUpload', 'upload multiple dirs on HCC'),
('export', 'Get annotation and other exports'),
('manifest', 'Generate a manifest for zooniverse subject set upload')
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('splitVCF', 'split a vcf to several smaller files with equal size'),
('combineVCF', 'combine split vcfs'),
('impute', 'impute vcf using beagle or linkimpute'),
('vcf2hmp', 'convert vcf to hmp format'),
('FixIndelHmp',
'fix the indels problems in hmp file converted from tassel'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('Missing', 'filter missing rate using customized script'),
('MAF', 'filter minor allele frequency using customized script'),
('Heterozygous', 'filter SNPs with high heterozygous rates'),
('Bad_Indels', 'remove wrong INDELs'),
('GrepImputatedVcf', 'grep the SNPs with lower missing rate before imputation from whole imputed vcf'),
('SummarizeLD', 'ld decay in log scale'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('CombineRep', 'combine fastq.gz files for the same sample'),
('Trim', 'perform quality control on raw fq.gz file'),
('Align', 'align reads to genome'),
('Sam2Bam', 'convert sam file to bam format'),
('SortBam', 'Sort bam files'),
('IndexBam', 'generate the index file on bam'),
('SNPsCall', 'call SNPs'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('genlabel', 'genearte label for training image files'),
('extract_info',
'extract testing and prediction results from dpp log file'),
('statistics',
'calculate CountDiff, AbsCountDiff, MSE, Agreement, r2, p_value, and draw scatter, bar plots'
),
('gentraining', 'create balanced training dataset for each class'),
('gentesting', 'create balanced testing dataset for each class'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('three2two', 'convert 3d npy to 2d'),
('three2two_slurms', 'gen slurm jobs for three2two'),
('crop_png', 'crop png images'),
('crop_png_slurms', 'gen slurm jobs for crop_png'),
('resize_png', 'resize png images'),
('resize_png_slurms', 'gen slurm jobs for resize_png'),
('hyp2arr', 'convert hyperspectral images to a numpy array'),
('hyp2arr_slurms', 'gen slurm jobs for hyp2arr'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())
def main():
actions = (
('Polish', 'Polish the predicted images (hyper)'),
('PolishBatch', 'generate all slurm jobs of polish (hyper)'),
('CallHeight', 'call height from polished image (hyper)'),
('CallHeightBatch',
'generate all slurm jobs of plant height calling (hyper)'),
('CallHeightRGB', 'call height from RGB image'),
('CallHeightRGBBatch',
'generate all slurm jobs of plant height calling (RGB)'),
)
p = ActionDispatcher(actions)
p.dispatch(globals())