-Version 1.5.2 * Be compatible with Seurat v5 * Add report_dynamic module: Interactive report with shiny features (still in testing) * call_peak: change to skip peak extension;
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Version 1.5.1
- Integrate module takes SampleSheet.csv file as input, where sample names, paths of peaks, fragments, and cell barcodes can be specified
- Enable 4/5 bp shift for each read by setting SHIFT_READS_IN_BAM as TRUE in configure_user.txt file
- Correctted an error for single-end sequencing data in the mapping module
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Version 1.5.0 released
- Donot annotate peaks in the seurat object (peaks are still annotated for modules runDA, runGO and visualize)
- Add new module process_from_align to do processing from alignment step (including aligment), given trimmed demultiplexed fastq files
- Enable to change default expected doublet rate in the configure_user.txt file
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Version 1.4.4 released
- Only consider standard chromosomes in the qc_per_barcode module
- Correct a minor bug in the qc_per_barcode module
- Add version# in the html report
- Clean and correct a minor bug in the trimming module
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Version 1.4.3 released
- add new module reprocess_cellranger_output to reprocess scATAC-seq data originally processed by cellranger
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Version 1.4.2 released
- correct bugs in processing single-end sequencing data
- correct minor underestimate of overall FrIP in the qc report
- correct a bug in process_with_bam module
- correct a bug in integrate module
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Version 1.4.1 released
- qc_per_barcode: add tss enrichment score per cell into the QC metrics
- update tutorial
- correct a bug relate to input filepath for report module
- record input file paths for the integration module
- using BAMPE for macs2 for paired-end sequencing
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Version 1.4.0 released
- new module added: labelTransfer (for cell annotation) from scRNA-seq
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Version 1.3.1 released
- rmDoublets: a new module added, to remove doublets
- clustering: accepts seurat obj (in .rds format) as input as well
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Version 1.3.0 released
- qc_per_barcode: add tss enrichment score per cell into the QC metrics
- call_cell: enable filtering barcodes by tss enrichment score
- fragments file indexed by tabix (named fragments.tsv.gz)
- footprint module: suppoort comparison of any two sets of cell clusters)
- motif_analysis and runDA: accept seurat object in .rds format as input
- integrate: rename cell name for each sample to avoid shared barcodes among samples; enable a distance parameter to merge peaks
- integrate_mtx: added, as an alias of previous integrate_seu module
- report: rearranged some plots and enabled output cicero interaction plot for a specific gene (specify it through *Cicero_Plot_Region parameter in the configure_user.txt file)
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Version 1.2.1 released
- new module addCB2bam: add cell barcode (CB) tag to a give bam file, new bam file will be saved in the same folder as the input bam (with name *_withCBtag.bam)
- save .rds file for matrix and correct bug of calculating insert size
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Version: 1.2.0 released:
- update footprint dependency rgt-hint module to python3
- save qc statistics in html report into tables, and peak calling summary inf added in the report
- add qc per cell to seurat obj metadata as: total.unique.frags, frac.peak, frac.mito, frac.tss, frac.promoter, and frac.enhancer
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VERSION 1.1.4 released
- demplx_fastq: the input supports directory path of 10x fastq files
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VERSION: 1.1.3 released
- runGO: update background genes to be all genes associated with any peak
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May, 2020 --VERSION 1.1.2 released
- integrate: add VFACS (Variable Features Across ClusterS) option for the integration module, which reselect variable features across cell clusters after an initial clustering, followed by another round of dimension reduction and clustering, specify Integrate_by = VFACS in configure file
- clustering: filter peaks before clustering (accessible in less than 0.5% of cells) and remove rare peaks (accessible in less than 1% of cells) from the variable features list
- reConsMtx: enable specifying a path for saving reconstructed matrix (optional)
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VERSION 1.1.1 released
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March,April, 2020
- get_mtx: it requires two input files: a fragments.txt file and a peak file, separated by comma
- annotate peak as overlapped with a gene Tss if the corresponding distance <= 1000bp; mark peak with a gene if their distance <= 100kb
- update DA, fix bug of using covariate
- using mefa4::Melt instead of melt -- better for large sparse matrix
- add PEAK_CALLER prefix to qc_per_barcode.txt filename
- fix a bug of file location of tmpJob when calling cells
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VERSION 1.1.0 released
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Feb, 2020
- integrate module enables 3 options: seurat, harmony and pool
- new module visualize, allowing interactively explore and analyze the data
- footprint module supports one-vs-rest comparison and provides result in heatmap
- module runDA changed to use group name as the input (e.g. "0:1,2" or "one,rest")
- installed rgt-hint (for footprinting analysis) using miniconda3
- added module process_with_bam, allowing processing from aggregated bam file
- enabled data integration from peaks files, assuming all data sets are processed using scATAC-pro. Output matrix with the same merged peaks/features and the previously called cells, along with an integrated seurat object
- added new parameters in the configuration file: Top_Variable_Features, REDUCTION, nREDUCTION
- enabled all clustering methods mentioned in the manuscript, along with kmeans clustering on principal components
- file path changed to like downstreame_analysis/PEAK_CALLER/CELL_CALLER/..., indicating peak caller
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Jan, 2020
- added a new module mergePeaks to merge different peak files called from different data sets
- added a new module reConstMtx to reconstruct peak-by-cell matrix given a peak file, a fragment file and a barcodes.txt file
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Dec, 2019
- corrected an error due to using older version of chromVAR
- corrected a bug for demultiplexing multiple index files
- added a module convert10xbam to convert 10x position sorted bam file to scATAC-pro file format
- updated module get_bam4Cells, with the inputs as a bam file and a txt file of barcodes, separated by comma