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- mikropml::otu_data_preprocMini OTU abundance dataset - preprocessed
- mikropml::otu_mini_binMini OTU abundance dataset
- mikropml::otu_mini_bin_results_glmnetResults from running the pipeline with L2 logistic regression on 'otu_mini_bin' with feature importance and grouping
- mikropml::otu_mini_bin_results_rfResults from running the pipeline with random forest on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_rpart2Results from running the pipeline with rpart2 on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_svmRadialResults from running the pipeline with svmRadial on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_xgbTreeResults from running the pipeline with xbgTree on 'otu_mini_bin'
- mikropml::otu_mini_cont_results_glmnetResults from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome
- mikropml::otu_mini_cont_results_nocvResults from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome column, using a custom train control scheme that does not perform cross-validation
- mikropml::otu_mini_cvCross validation on 'train_data_mini' with grouped features.
- mikropml::otu_mini_multiMini OTU abundance dataset with 3 categorical variables
- mikropml::otu_mini_multi_groupGroups for otu_mini_multi
- mikropml::otu_mini_multi_results_glmnetResults from running the pipeline with glmnet on 'otu_mini_multi' for multiclass outcomes
- mikropml::otu_smallSmall OTU abundance dataset
- mikropml::otu_data_preprocMini OTU abundance dataset - preprocessed
- mikropml::otu_mini_binMini OTU abundance dataset
- mikropml::otu_mini_bin_results_glmnetResults from running the pipeline with L2 logistic regression on 'otu_mini_bin' with feature importance and grouping
- mikropml::otu_mini_bin_results_rfResults from running the pipeline with random forest on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_rpart2Results from running the pipeline with rpart2 on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_svmRadialResults from running the pipeline with svmRadial on 'otu_mini_bin'
- mikropml::otu_mini_bin_results_xgbTreeResults from running the pipeline with xbgTree on 'otu_mini_bin'
- mikropml::otu_mini_cont_results_glmnetResults from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome
- mikropml::otu_mini_cont_results_nocvResults from running the pipeline with glmnet on 'otu_mini_bin' with 'Otu00001' as the outcome column, using a custom train control scheme that does not perform cross-validation
- mikropml::otu_mini_cvCross validation on 'train_data_mini' with grouped features.
- mikropml::otu_mini_multiMini OTU abundance dataset with 3 categorical variables
- mikropml::otu_mini_multi_groupGroups for otu_mini_multi
- mikropml::otu_mini_multi_results_glmnetResults from running the pipeline with glmnet on 'otu_mini_multi' for multiclass outcomes
- mikropml::otu_smallSmall OTU abundance dataset
- MOSuite::gene_countsRSEM expected gene counts
- MOSuite::nidap_batch_corrected_countsBatch-corrected counts for the NIDAP test dataset.
- MOSuite::nidap_batch_corrected_counts_2Batch-corrected counts for the NIDAP test dataset. The result of running 'batch_correct_counts()' on 'nidap_norm_counts'.
- MOSuite::nidap_clean_raw_countsClean raw counts for the NIDAP test dataset. The result of running 'clean_raw_counts()' on 'nidap_raw_counts'.
- MOSuite::nidap_deg_analysisDifferential gene expression analysis for the NIDAP test dataset.
- MOSuite::nidap_deg_analysis_2Differential gene expression analysis for the NIDAP test dataset. The result of running 'diff_counts()' on 'nidap_filtered_counts'.
- MOSuite::nidap_deg_gene_listList of differentially expressed genes from the NIDAP test dataset using default parameters with 'filter_diff()'.
- MOSuite::nidap_filtered_countsFiltered counts for the NIDAP test dataset. The result of running 'filter_counts()' on 'nidap_clean_raw_counts'.
- MOSuite::nidap_norm_countsNormalized counts for the NIDAP test dataset. The result of running 'normalize_counts()' on 'nidap_filtered_counts'.
- MOSuite::nidap_raw_countsRaw counts for the NIDAP test dataset Pairs with 'nidap_sample_metadata'.
- MOSuite::nidap_sample_metadataSample metadata for the NIDAP test dataset
- MOSuite::nidap_venn_diagram_datOutput data from venn diagram. The result of running 'plot_venn_diagram()' on 'nidap_volcano_summary_dat'
- MOSuite::nidap_volcano_summary_datSummarized differential expression analysis for input to venn diagram
- MOSuite::gene_countsRSEM expected gene counts
- MOSuite::nidap_batch_corrected_countsBatch-corrected counts for the NIDAP test dataset.
- MOSuite::nidap_batch_corrected_counts_2Batch-corrected counts for the NIDAP test dataset. The result of running 'batch_correct_counts()' on 'nidap_norm_counts'.
- MOSuite::nidap_clean_raw_countsClean raw counts for the NIDAP test dataset. The result of running 'clean_raw_counts()' on 'nidap_raw_counts'.
- MOSuite::nidap_deg_analysisDifferential gene expression analysis for the NIDAP test dataset.
- MOSuite::nidap_deg_analysis_2Differential gene expression analysis for the NIDAP test dataset. The result of running 'diff_counts()' on 'nidap_filtered_counts'.
- MOSuite::nidap_deg_gene_listList of differentially expressed genes from the NIDAP test dataset using default parameters with 'filter_diff()'.
- MOSuite::nidap_filtered_countsFiltered counts for the NIDAP test dataset. The result of running 'filter_counts()' on 'nidap_clean_raw_counts'.
- MOSuite::nidap_norm_countsNormalized counts for the NIDAP test dataset. The result of running 'normalize_counts()' on 'nidap_filtered_counts'.
- MOSuite::nidap_raw_countsRaw counts for the NIDAP test dataset Pairs with 'nidap_sample_metadata'.
- MOSuite::nidap_sample_metadataSample metadata for the NIDAP test dataset
- MOSuite::nidap_venn_diagram_datOutput data from venn diagram. The result of running 'plot_venn_diagram()' on 'nidap_volcano_summary_dat'
- MOSuite::nidap_volcano_summary_datSummarized differential expression analysis for input to venn diagram
