The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Perform quality control, filter, extract, cure, and quantify lineage barcodes and UMIs from raw FASTQ files.
library(CellBarcode)
# 1. Load User Inputs
example_data <- system.file("extdata", "mef_test_data", package = "CellBarcode")
fq_files <- dir(example_data, "fastq.gz", full=TRUE)
sample_name <- paste0("sample_", seq_along(fq_files))
# 2. Quality Control
qc_noFilter <- bc_seq_qc(fq_files)
bc_plot_seqQc(qc_noFilter)
# 3. Filtering
fq_filter <- bc_seq_filter(fq_files, min_average_quality = 30, min_read_length = 60, sample_name = sample_name)
# 4. Extraction
pattern <- "([ACGT]{12})CTCGAGGTCATCGAAGTATC([ACGT]+)CCGTAGCAAGCTCGAGAGTAGACCTACT"
bc_obj <- bc_extract(fq_filter, pattern = pattern, pattern_type = c("UMI" = 1, "barcode" = 2), sample_name = sample_name)
# 5. Curing and Quantification
bc_sub <- bc_cure_umi(bc_obj, depth = 2)
bc_sub <- bc_cure_depth(bc_sub, depth = 2)
# 6. Export
df_counts <- bc_2df(bc_sub)
mat_counts <- bc_2matrix(bc_sub)
Inputs/Outputs: Takes raw FASTQ files and a regular expression pattern as inputs, and outputs a BarcodeObj containing cleaned barcode and UMI counts, exportable to a data frame (bc_2df) or matrix (bc_2matrix).
Extract lineage barcodes, cell barcodes, and UMIs from 10X Genomics single-cell RNA-seq SAM files.
library(CellBarcode)
# 1. Load User Inputs
sam_file <- system.file("extdata", "scRNASeq_10X.sam", package = "CellBarcode")
# 2. Extract Lineage Barcode
d <- bc_extract_sc_sam(
sam = sam_file,
pattern = "AGATCAG(.*)TGTGGTA",
cell_barcode_tag = "CR",
umi_tag = "UR"
)
Inputs/Outputs: Takes a SAM file derived from 10X Genomics CellRanger output and a regular expression pattern as inputs, and outputs a data frame containing cell_barcode, umi, barcode_seq, and count.
bc_extract.bc_extract_sc_sam.bc_seq_filter and perform quality control visualization using bc_plot_seqQc.bc_cure_umi and bc_cure_depth.Rsubread or Salmon instead).Seurat or scran instead)."([ACGT]{12})CTCGAGGTCATCGAAGTATC([ACGT]+)CCGTAGCAAGCTCGAGAGTAGACCTACT").() capturing the target sequences.c("UMI" = 1, "barcode" = 2)) mapping captured groups to sequence types.bc_seq_filter.bc_seq_filter.bc_cure_umi and bc_cure_depth.bc_extract_sc_sam.bc_extract_sc_sam.bc_seq_qc and bc_plot_seqQc before extraction to identify constant and random regions of the sequencing reads.bc_seq_filter to reduce noise and false-positive barcodes.bc_cure_umi followed by bc_cure_depth to perform sequence error correction and remove low-abundance barcode artifacts.bc_plot_seqQc base ratio plots.samtools view -f 4) before running bc_extract_sc_sam.ShortRead for general FASTQ manipulation and quality control without specialized barcode/UMI extraction.scuttle for single-cell preprocessing and QC without lineage barcode extraction.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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