runall — From FASTQ to TME
runall
--mode {salmon|star} (required)--outdir <DIR> (required): root output directory--fastq <DIR> (required): forwarded to fastq_qc --path1_fastq--threads <INT> (per-block): CPU/concurrency control set via block-level flags (e.g., fastq_qc –num_threads, batch_salmon –num_threads, batch_star_count –num_threads, merge_salmon –num_processes, cibersort –threads, calculate_sig_score –parallel_size).--batch_size <INT> (per-block): batching size set via block-level flags (e.g., fastq_qc –batch_size, batch_salmon –batch_size, batch_star_count –batch_size).--resume: skip steps if outputs already exist--dry_run: print planned commands without executing
All-in-one TME profiling - tme_profile
tme_profile
-i/--input <CSV|TSV[.gz]> (required): TPM matrix (genes × samples)-o/--output <DIR> (required): root output directory--threads <int> (default: 1): threads for scoring/deconvolution
From FASTQ through FASTQ Quality Control and Salmon/STAR to TPM
fastq_qc
--path1_fastq <DIR> (required): raw FASTQ directory--path2_fastp <DIR> (required): output directory for fastp results (01-qc/)--num_threads <int> (default: 8)--suffix1 <str> (default: _1.fastq.gz): forward read suffix--batch_size <int> (default: 5)--se: single-end mode--length_required <int> (default: 50)Notes: Writes per-sample *_fastp.html/json; if multiqc is present, also writes 01-qc/multiqc_report/multiqc_fastp_report.html.(Implementation: automatic MultiQC invocation and output path)
Salmon mode
batch_salmon
--index <DIR> (required): salmon index--path_fq <DIR> (required): directory of FASTQs (after fastq_qc)--path_out <DIR> (required): output root (e.g., 02-salmon/)--suffix1 <str> (default: _1.fastq.gz)--batch_size <int> (default: 1): concurrent samples (processes)--num_threads <int> (default: 8): threads per salmon--gtf <FILE>: optional GTF for -g gene-level quantBehavior: safe R1 to R2 inference; per-sample task.complete; progress; preflight prints salmon version & index meta keys.
merge_salmon
--path_salmon <DIR> (required): root containing per-sample salmon outputs (searched recursively)--project <STR> (required): prefix for outputs--num_processes <int>: I/O threads (default: CPU count)Output: <project>_salmon_tpm.tsv.gz, <project>_salmon_count.tsv.gz under --path_salmon with progress and head preview.
prepare_salmon
-i/--input <TSV|TSV.GZ> (required): Salmon-combined gene TPM table-o/--output <CSV/TSV> (required): cleaned TPM matrix (genes × samples)-r/--return_feature {ENST|ENSG|symbol} (default: symbol): which identifier to keep--remove_version: strip version suffix from gene IDs (e.g., ENSG000001.12 to ENSG000001)
STAR mode
batch_star_count
--index <DIR> (required): STAR genomeDir--path_fq <DIR> (required): directory of FASTQs (after fastq_qc)--path_out <DIR> (required): outputs (e.g., 02-star/)--suffix1 <str> (default: _1.fastq.gz)--batch_size <int> (default: 1)--num_threads <int> (default: 8)Notes: generates sorted BAM and _ReadsPerGene.out.tab per sample and a summary of paths.
merge_star_count
--path <DIR> (required): directory containing multiple *_ReadsPerGene.out.tab--project <STR> (required): output prefixOutput: <project>.STAR.count.tsv.gz (gzipped TSV with gene IDs as rows and samples as columns)
count2tpm
-i/--input <CSV/TSV[.gz]> (required): raw count matrix (genes × samples)-o/--output <CSV/TSV> (required): output TPM matrix--effLength_csv <CSV>: optional effective-length file with columns id, eff_length, symbol--idtype {ensembl|entrez|symbol|mgi} (default: ensembl)--org {hsa|mmus} (default: hsa)--id <str> (default: id): ID column name in --effLength_csv--length <str> (default: eff_length): length column--gene_symbol <str> (default: symbol): gene symbol column--check_data: check & drop missing/invalid entries before conversion--remove_version: strip version suffix from gene IDs
(Optional) Mouse to Human symbol mapping
mouse2human_eset
-i/--input <CSV|TSV|TXT[.gz]> (required): input expression matrix or table -o/--output <CSV|TSV|TXT[.gz]> (required): converted matrix indexed by human symbols (genes × samples)--is_matrix: treat input as a matrix (rows = mouse gene symbols, columns = samples); if omitted, runs in table mode --column_of_symbol <str> (required in table mode): column name that contains mouse gene symbols--sep <,|\t>: override input separator; if omitted, inferred by extension.--out_sep <,|\t>: override output separator; if omitted, inferred by output path extension--verbose: print shapes and basic run info
(Optional) Annotate / de‑duplicate
anno_eset
-i/--input <CSV/TSV/TXT> (required)-o/--output <CSV/TSV/TXT> (required)--annotation {anno_hug133plus2|anno_rnaseq|anno_illumina|anno_grch38} (required unless using external file)--annotation-file <pkl/csv/tsv/xlsx>: external annotation (overrides built-in)--annotation-key <str>: key to pick a table if external .pkl stores a dict of DataFrames--symbol <str> (default: symbol): column used as gene symbol--probe <str> (default: id): column used as probe/feature ID--method {mean|sd|sum} (default: mean): duplicate-ID aggregation--remove_version: strip version suffix from gene IDs
Signature scoring
calculate_sig_score
-i/--input <CSV/TSV/TXT> (required), -o/--output <CSV/TSV/TXT> (required)--signature <one or more groups> (required; space- or comma-separated; all uses every group)go_bp, go_cc, go_mf, signature_collection, signature_tme, signature_sc, signature_tumor, signature_metabolism, kegg, hallmark, reactome--method {pca|zscore|ssgsea|integration} (default: pca)--mini_gene_count <int> (default: 3)--adjust_eset: apply extra filtering after log2 transform--parallel_size <int> (default: 1; threads for scoring (PCA/zscore/ssGSEA))
Deconvolution / scoring
cibersort
-i/--input <CSV/TSV> (required), -o/--output <CSV/TSV> (required)--perm <int> (default: 100)--QN <True|False> (default: True): quantile normalization--absolute <True|False> (default: False): absolute mode--abs_method {sig.score|no.sumto1} (default: sig.score)--threads <int> (default: 1)Output: columns are suffixed with _CIBERSORT, index name is ID, separator inferred from output extension. quantiseq
-i/--input <CSV/TSV> (required; genes × samples), -o/--output <TSV> (required)--arrays: perform quantile normalization for arrays--signame <str> (default: TIL10)--tumor: remove genes highly expressed in tumors--scale_mrna: enable mRNA scaling (otherwise raw signature proportions)--method {lsei|hampel|huber|bisquare} (default: lsei)--rmgenes <str> (default: unassigned; allowed: default, none, or comma-separated list) epic
-i/--input <CSV/TSV> (required; genes × samples)-o/--output <CSV/TSV> (required)--reference {TRef|BRef|both} (default: TRef) estimate
-i/--input <CSV/TSV/TXT> (required; genes × samples)-p/--platform {affymetrix|agilent|illumina} (default: affymetrix)-o/--output <CSV/TSV/TXT> (required)Output is transposed; columns are suffixed with _estimate; index label is ID; separator inferred from extension. mcpcounter
-i/--input <TSV> (required; genes × samples)-f/--features {affy133P2_probesets|HUGO_symbols|ENTREZ_ID|ENSEMBL_ID} (required)-o/--output <CSV/TSV> (required)Output: suffixed with _MCPcounter; index label ID; separator inferred from extension. IPS
-i/--input <matrix> (required), -o/--output <file> (required)No extra flags (the expression matrix yields IPS sub-scores and a total score). deside (deep learning–based deconvolution)
-m/--model_dir <dir> (required): path to the pre-downloaded DeSide model directory-i/--input <CSV/TSV> (required): rows = genes, columns = samples-o/--output <CSV> (required)--exp_type {TPM|log_space|linear} (default: TPM)
TPM: already log2 processedlog_space: log2(TPM+1)linear: linear space (TPM/counts) --gmt <file1.gmt file2.gmt ...>: optional one or more GMT files for pathway masking--method_adding_pathway {add_to_end|convert} (default: add_to_end)--scaling_by_constant, --scaling_by_sample, --one_minus_alpha: optional scaling/transforms--print_info: verbose logs--add_cell_type: append predicted cell-type labels--transpose: use if your file is samples × genes -r/--result_dir <dir>: optional directory to save result plots/logs
Clustering / decomposition
tme_cluster
-i/--input <CSV/TSV/TXT> (required): input table for clustering.
Expected shape: first column = sample ID (use --id if not first), remaining columns = features.
-o/--output <CSV/TSV/TXT> (required): output file for clustering results.--features <spec>: select feature columns by 1-based inclusive range, e.g. 1:22 (intended for CIBERSORT outputs; exclude the sample ID column when counting).--pattern <regex>: alternatively select features by a regex on column names (e.g. ^CD8|^NK).Tip: use one of --features or --pattern. --id <str> (default: first column): column name containing sample IDs.--scale / --no-scale: toggle z-score scaling of features (help text: default = True ).--min_nc <int> (default: 2): minimum number of clusters to try.--max_nc <int> (default: 6): maximum number of clusters to try.--max_iter <int> (default: 10): maximum iterations for k-means.--tol <float> (default: 1e-4): convergence tolerance for centroid updates.--print_result: print intermediate KL scores and cluster counts.--input_sep <str> (default: auto): input delimiter (e.g. , or \t); auto-detected if unset.--output_sep <str> (default: auto): output delimiter; inferred from filename if unset. nmf
-i/--input <CSV/TSV> (required): matrix to factorize; first column should be sample names (index).-o/--output <DIR> (required): directory to save results.--kmin <int> (default: 2): minimum k (inclusive).--kmax <int> (default: 8): maximum k (inclusive).--features <spec>: 1-based inclusive selection of feature columns (e.g. 2-10 or 1:5), typically cell-type columns.--log1p: apply log1p to the input (useful for counts).--normalize: L1 row normalization (each sample sums to 1).--shift <float> (default: None): if data contain negatives, add a constant to make all values non-negative.--random-state <int> (default: 42): random seed for NMF.--max-iter <int> (default: 1000): NMF max iterations.--skip_k_2: skip evaluating k = 2 when searching for the best k.
Ligand–receptor
LR_cal
-i/--input <CSV/TSV> (required): expression matrix (genes × samples).-o/--output <CSV/TSV> (required): file to save LR scores.--data_type {count|tpm} (default: tpm): type of the input matrix.--id_type <str> (default: ensembl): gene ID type expected by the LR backend.Choices: ensembl, entrez, symbol, mgi.--cancer_type <str> (default: pancan): cancer-type network to use.--verbose: verbose logging.