Preprocess 10x data.

betsy_run.py --num_cores 20 --network_png count12.pdf --receipt count14.txt \
--input TenXFastqFolder --input_file fastq01 \
--input SampleGroupFile --input_file samp17.xls \
--output TenXPreprocessing --output_file count11 \
--mattr sample_names_are_10x_format=yes \

Preprocess iCell8 data.

GTF=genomes/GENCODE.GRCh38.v29.annotation.gtf
betsy_run.py --num_cores 30 \
--network_png proc02.pdf --receipt proc03.txt \
--input ICell8FastqFolder --input_file fastq21 \
--input ICell8WellListFile --input_file well81.txt \
--input ICell8SampleFile --input_file samp81.txt \
--input GTFGeneModel --input_file $GTF \
--input ReferenceGenome --input_file genomes/GENCODE.GRCh38.p12 \
--output ICell8Preprocessing --output_file proc01 \
--dattr ICell8Preprocessing.aligner=star \
--dattr ICell8Preprocessing.gene_expression_estimator=featurecounts \
--mattr num_reads_in_subset=50000 \
--mattr max_files_in_subset=20 \
--mattr use_shared_memory=yes

Preprocess for Fluidigm signal data.

Make a fluidigm sample file.

betsy_run.py \
--input FluidigmFastqFolder --input_file fastq01 \
--output UnvalidatedFluidigmSampleFile --output_file samp01.xls

GTF=genomes/Broad.hg19.RefSeq.NM_only.no_isoforms.170703.gtf
betsy_run.py --num_cores 40 --network_png fastq52.pdf \
--input FluidigmFastqFolder --input_file fastq01 \
--input FluidigmSampleFile --input_file samp01.xls \
--input GTFGeneModel --input_file $GTF \
--input ReferenceGenome --input_file genomes/Broad.hg19 \
--output FeatureCountsSummary --output_file fastq51.xls \
--dattr FeatureCountsSummary.aligner=star \
--mattr use_shared_memory=yes

Preprocess for Fluidigm signal reads (already demultiplexed).

GTF=genomes/gencode.vM14.annotation.gtf
betsy_run.py --num_cores 20 --network_png proc12.pdf --receipt proc13.txt \
--input FastqFolder --input_file fastq21 \
--input SampleGroupFile --input_file samp02.xls \
--input GTFGeneModel --input_file $GTF \
--input ReferenceGenome --input_file genomes/GENCODE.GRCm38 \
--output RNASeqSignalFile --output_file exp01.txt \
--dattr RNASeqSignalFile.preprocess=counts \
--dattr RNASeqSignalFile.aligner=star \
--dattr RNASeqSignalFile.gene_expression_estimator=featurecounts

QC on the data with:

betsy_run.py --num_cores 20 --network_png meta02.pdf \
--input SignalFile --input_file count01.txt \
--dattr SignalFile.preprocess=counts \
--output scRNAMetadata --output_file meta01

Differential expression analysis.

betsy_run.py --network_png test02.pdf --num_cores 20 \
--input SignalFile --input_file exp01.txt \
--dattr SignalFile.preprocess=counts \
--input SimpleClassFile --input_file class01.xlsx \
--dattr SimpleClassFile.preprocess=counts \
--output DiffExpAnalysis --output_file test01 \
--dattr DiffExpAnalysis.de_algorithm=deseq2 \
--dattr DiffExpAnalysis.de_comparison=one_vs_others \
--dattr DiffExpAnalysis.preprocess=counts \
--dattr SignalFile.filter_and_threshold_genes=yes \
--mattr keep_genes_expressed_in_perc_samples=5 \
--mattr p_cutoff=0.05 \
--mattr plot_max_genes_per_group=10

UMAP on ssGSEA scores.

betsy_run.py --network_png clust12.pdf --num_cores 40 \
--input SignalFile --input_file gsea22.txt \
--dattr SignalFile.has_entrez_gene_info=yes \
--dattr SignalFile.has_human_gene_info=yes \
--dattr SignalFile.logged=not_applicable \
--output UMAPAnalysis --output_file clust11 \
--mattr umap_alternate_pca_genes=10,25 \
--mattr umap_alternate_pca_dimensions=5,10 \
--mattr umap_alternate_snn_k=10,25,50 \
--mattr umap_alternate_snn_resolution=0.6,0.8,1.0 \
--mattr umap_alternate_umap_dimensions=5,10 \
--mattr umap_pca_genes=25 \
--mattr umap_pca_dimensions=5 \
--mattr umap_snn_k=10 \
--mattr umap_snn_resolution=0.8 \
--mattr umap_umap_dimensions=10

ImmClassifier analysis

betsy_run.py --num_cores 20 --network_png imm02.pdf
--input RNASeqSignalFile --input_file exp01.txt
--dattr RNASeqSignalFile.preprocess=counts
--output ImmClassifierResults --output_file imm01.txt

Can work with counts, tpm, rma.

SingleR analysis

betsy_run.py --num_cores 20 --network_png test52.pdf
--input SignalFile --input_file test31.txt
--dattr SignalFile.preprocess=counts
--output SingleRResults --output_file test51

InferCNV analysis

GTF=genomes/Broad.hg19.RefSeq.NM_only.170811.gtf betsy_run.py --num_cores 20 --network_png cnva2.pdf --receipt cnva4.txt
--input SignalFile --input_file exp31.txt
--dattr SignalFile.preprocess=counts
--input GTFGeneModel --input_file ${GTF}
--output InferCNVAnalysis --output_file cnva1
--mattr reference_dataset=exp01.txt
--mattr reference_dataset2=exp11.txt
--mattr reference_name=fibroblasts
--mattr reference_name2=hmec

Other attributes:

• --dattr InferCNVAnalysis.group_cells=yes
• --dattr InferCNVAnalysis.num_clusters=3
• --dattr InferCNVAnalysis.group_ref_cells=yes
• --mattr infercnv_analysis=subclusters
• --mattr infercnv_class_file=class_file.txt
• --mattr infercnv_cutoff=1
• --mattr infercnv_merge_references=yes