The most popular method of such normalization is conversion of raw reads to Transcripts per Million TPM. The conversion accounts for two biases: 1 different samples are sequenced at different depth, not directly related to gene expression differences; 2 long genes are expected to generate more cDNA fragments than the short ones. After this, the resulting numbers are scaled linearly to add up to one million. Thus, the sum of all TPM values for a particular sample is always equal to approximately 1,, Most modern scRNA-seq technologies generate read sequences containing three key pieces of information:.
It uses STAR aligner, which performs splicing-aware alignment of reads to the genome. After this, it uses the transcript annotation GTF to bucket the reads into exonic, intronic, and intergenic, and by whether the reads align confidently to the genome. Following the read type assignment, mapping quality adjustment is done: for reads that align to a single exonic locus but also align to 1 or more non-exonic loci, the exonic locus is prioritized and the read is considered to be confidently mapped to the exonic locus and given a maximum mapping quality score.
Cell Ranger further aligns exonic and intronic confidently mapped reads to annotated transcripts by examining their compatibility with the transcriptome. Reads are classified based on whether they are sense or antisense and based on whether they are exonic, intronic or whether their splicing pattern is compatible with transcript annotations associated with that gene.
By default, reads that are transcriptomic blue in the figure above are carried forward to UMI counting. When the input to the assay consists of nuclei, a high percentage of the reads comes from the unspliced transcripts and align to introns. If this option is used, any reads that map in the sense orientation to a single gene - which include the reads labeled transcriptomic blue , exonic light blue , and intronic red in the diagram above - are carried forward to UMI counting.
Importantly, a read is considered uniquely mapping if it is compatible with only a single gene. Only uniquely mapping reads are carried forward to UMI counting; multimapping genes are discarded by Cell Ranger.
Primary genome assembly versions i. Annotation GTF files are filtered, using the scripts that could be found here. All pseudogenes and small noncoding RNAs are removed. There are several versions of Cell Ranger reference that come pre-packaged with the software; A is the newest version of reference to date. All individual assembly and annotation combinations used by Cell Ranger previously are listed below.
Cellular barcode sequences are synthetic sequences attached to the beads that identify individual cells. The library of unique sequences is called a whitelist and depends on the Chromium library preparation kit version. The whitelist files are available from the Cell Ranger repository.
CBs from the first list are 14 bp long, and two others are 16 bp. The table below provides cellular barcodes and UMI lengths, as well as appropriate whitelist files, for popular 10x single cell sequencing kits:. Cell Ranger uses the following algorithm to correct putative barcode sequences against the whitelist :. The corrected barcodes are used for all downstream analysis and output files.
In the output BAM file, the original uncorrected barcode is encoded in the CR tag, and the corrected barcode sequence is encoded in the CB tag. This has been shown in numerous studies on the accurate estimate of isoform abundance 56 , After normalizing by length, we then removed isoforms that had fewer than one count and that were in fewer than one cell.
We also removed genes and their corresponding isoforms that had a dispersion of less than 0. To generate the cell-by-gene matrix we summed the isoforms that correspond to the same gene.
Cells were normalized to TPM by dividing the counts in each cell by the sum of the counts for that cell, then multiplying by 1,, The count matrices were then transformed with log1p and the columns were scaled to unit variance and zero mean. The resulting gene and isoform matrix contained 6, cells and 19, genes, corresponding to 69, isoforms.
Highly variable isoforms and genes were identified by first computing the dispersion for each feature, and then binning all of the features into 20 bins. The dispersion for each feature was normalized by subtracting the mean dispersion and dividing by the variance of the dispersions within each bin. Then the top 5, features were retained based on the normalized dispersion. This was computed 58 using scanpy. Both matrices were loaded into python using kb python.
Cells with less than gene counts and with greater than Cells were normalized to counts per million CPM by dividing the counts in each cell by the sum of the counts for that cell, then multiplying by 1,, The count matrices were then transformed with log1p and the columns scaled to unit variance and zero mean.
The resulting gene matrix contained 94, cells and 24, genes. We identified batch effect among cells assayed on different dates so we restricted our analysis to only the cells assayed on the same date and selected the date with the most number of cells Supplementary Fig. Additionally, we performed pairwise comparison of gene counts for each of the 4 10xv3 batches and found the Pearson correlation to be very high for all pairs, with a mean of 0. This was computed using 58 scanpy. NCA takes as input not just a collection of cells with their associated abundances, but also cluster labels for those cells, and seeks to find a projection that minimizes leave-one-out k -nearest neighbour error Similarly uniform manifold approximation was performed on the 10 NCA components and the 50 truncated singular value decomposition SVD -derived components.
UMAP 27 was computed with the umap package with default parameters. To ensure that NCA was not overfitting cells to their corresponding subclasses, we randomly permuted all of the subclasses labels and reran the NCA-to- t -SNE dimensionality-reduction method. We observed uniform mixing of the permuted subclass labels, indicating that NCA was not overfitting the cells to their corresponding subclasses. No explicit calculations were performed to determine sample size. We analysed both male and female mice to understand differences in gene and isoform expression.
The smallest cluster size contained seven cells, with all cells having non-zero expression of the tested genes. We computed error bars for all tests to ensure that sample sizes were sufficient. After finding a meaningful projection that appears to respect global structure of the cells we searched for possible sources of batch effect within the datasets. We found evidence of batch effect in the 10xv3 data by assay date Supplementary Fig. To ensure that our findings were not confounded by this batch effect we selected the set of cells from only one assay date and picked the set with the largest number of cells and the one with cells present in all clusters.
We then looked at the MERFISH data and found minimal evidence of batch effect across samples based on the distribution of batch labels across clusters where the observed fraction of cells per batch in each cluster was almost exactly the expected fraction of cells per batch assuming uniform mixing Supplementary Fig. In further examining the single 10xv3 batch we settled on, we noted a low correlation in one case, the L5 IT subclass. We hypothesized that this low correlation stems from a subclass-specific sex effect within the L5 IT, where those cells differ drastically in their overall expression compared to other subclasses.
After performing differential expression between male and female cells within all subclasses we found that the L5 IT had the highest amount of uniquely differential genes Supplementary Fig. The other subclasses, however, did not exhibit sex-based segregation. Without being able to rule out that the low correlation for L5 IT cells across the technologies was due to confounding between batch and sex in the dataset, we decided to excluded the subclass from our analyses.
We parsed the transcripts-to-genes map, grouping together transcripts that had the same end site that were in the same gene. We then counted the number of these end site sets within a gene and plotted them against the number of isoforms within that gene. For each pair we started with two raw matrices and restricted to the set of genes or isoforms common to the two.
Then we normalized the counts for each matrix per cell to one million, log1p-transformed the entire matrix, and scaled the features to zero mean and unit variance.
We then found the mean cell within the respective clusters in the two matrices and computed the Pearson correlation between them. These methods were implemented for Extended Data Figs. Comparisons of different scRNA-seq technologies have tended to focus on throughput, cost and gene-level accuracy 60 in a winner-takes-all competition.
Our results shed some light on the matter; it has been previously shown that quantification of isoform abundance is necessary for accurate gene-level estimates 61 , and we found that it matters in practice Supplementary Fig. For each level of clustering, class, subclass and cluster, we performed a t -test for each gene or isoform between the cluster and its complement on the log1p counts.
To identify isoform enrichment that is masked at a gene-level analysis, we looked for isoforms that were upregulated by checking that the gene containing that isoform was not significantly expressed in that cluster relative to the complement of that cluster. All t -tests used a significance level of 0. The Pvalb gene is a marker for the Pvalb subclass. Only one of the two isoforms for Pvalb marked the Pvalb cluster.
Additionally, we identified all of the genes that mark the specific subclasses in the MERFISH data through differential analysis and checked if their underlying isoforms were also differentially expressed. We then noted which isoforms were differentially expressed for the spatial isoform atlas. First, we selected a representative slice of the MOp. Then we found the outer hull of the MOp by using scipy. We selected the points that defined the upper boundary of the Mop, then performed linear regression to fit a line to those points using sklearn.
For each subclass in the glutamatergic class of cells we identified the centroid of the subclass and determined the perpendicular distance of the centroid to the MOp boundary line. We normalized the set of distances by dividing by the centroid with the largest distance to the boundary. For each isoform, we performed weighted least squares regression for all of the subclasses with the weights equal to the variance of isoform expression for each subclass.
We used the statsmodel. WLS function. All weighted least-squares tests used a significance level of 0. Monotonicity was checked for isoforms with an absolute value slope greater than 1. Differential analysis was then performed in exactly the same way as above.
For each cluster and each TSS or isoform, a t -test was performed between the cells in that cluster and the cells in the complement of that cluster. All statistical tests used a significance level of 0. Naive gene-count matrices were constructed from the SMART-seq data by summing the counts corresponding to a single gene. Gene-count matrices quantified by the expectation maximization EM algorithm and normalized appropriately were made with SMART-seq by first dividing isoform abundances by the length of their transcripts, and then summing the abundances of isoforms by gene.
Differential analysis was performed independently on these two gene-count matrices and the resultant differential genes were compared.
Differential expression was then performed on all of the genes for both the EM and naive gene quantifications. Software versions used were: Anndata 0.
Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Picelli, S. Full-length RNA-seq from single cells using Smart-seq2. Chen, K. Spatially resolved, highly multiplexed RNA profiling in single cells.
Science , aaa Zheng, G. Massively parallel digital transcriptional profiling of single cells. Weyn-Vanhentenryck, S. Precise temporal regulation of alternative splicing during neural development. Walker, R. Genetic control of gene expression and splicing in the developing human brain.
Porter, R. Neuron-specific alternative splicing of transcriptional machineries: implications for neurodevelopmental disorders. Lukacsovich, D. Single-cell RNA-seq reveals developmental origins and ontogenetic stability of neurexin alternative splicing profiles. Cell Rep. Song, Y. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation.
Cell 67 , Que, L. Deep survey of GABAergic interneurons: emerging insights from gene-isoform transcriptomics. Huang, C. Cell type-specific expression of eps8 in the mouse hippocampus. BMC Neurosci. Zhang, Y. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. Sugino, K. Mapping the transcriptional diversity of genetically and anatomically defined cell populations in the mouse brain.
Bittar, P. Selective distribution of lactate dehydrogenase isoenzymes in neurons and astrocytes of human brain. Blood Flow Metab. Tasic, B. The Google Colab version uses the 10x 1k neurons dataset and the kb wrapper of kallisto and bustools to make that notebook more interactive the slowest step is installing packages.
This static version shows the individual kallisto and bustools commands, which may be helpful for modularization of the workflow. This notebook demonstrates the use of command line tools kallisto and bustools. The binary of bustools can be found here.
After you download the binary, you should decompress the file if it is tar. Then you can directly invoke the binary on the command line as we will do in this notebook. We will be using the R packages below. BUSpaRse is now on Bioconductor 3. BUSpaRse will be used to generate the transcript to gene file for bustools and to read output of bustools into R.
We will use SingleR , which is also on Bioconductor 3. This vignette uses the version of DropletUtils from Bioconductor version 3. If you are using a version of R older than 3.
The package monocle should also be installed from Bioconductor:. The dataset we are using is 10x 10k neurons from an E18 mouse almost 25 GB. Here we use kallisto to pseudoalign the reads to the transcriptome and then to create the bus file to be converted to a sparse matrix. The first step is to build an index of the mouse transcriptome. The transcriptome downloaded here is Ensembl version For the sparse matrix, most people are interested in how many UMIs per gene per cell, we here we will quantify this from the bus output, and to do so, we need to find which gene corresponds to each transcript.
Those are the transcripts in the transcriptome index. Transcript IDs must be in the same order as in the kallisto index. With the index and the fastq files, the kallisto bus command generates a binary bus file called output. A whitelist that contains all the barcodes known to be present in the kit is provided by 10x and comes with CellRanger. A CellRanger installation is required, though we will not run CellRanger here. First, bustools runs barcode error correction on the bus file.
Then, the corrected bus file is sorted by barcode, UMI, and equivalence classes. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.
BayesSpace Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. BiocIO Implements import and export standard generics for importing and exporting biological data formats.
The import interface optionally provides transparent access to remote e. Developers can register a file extension, e.
Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. BrainSABER is an R package that facilitates comparison of user data with the various developmental stages and brain structures found in the BrainSpan atlas by generating dynamic similarity heatmaps for the two data sets.
It also provides a self-validating container for user data. CellaRepertorium Methods to cluster and analyze high-throughput single cell immune cell repertoires, especially from the 10X Genomics VDJ solution. Methods to visualize and analyze paired heavy-light chain data.
Tests for specific expansion, as well as omnibus oligoclonality under hypergeometric models. Analyzing and visualizing insert size metrics could be time intensive. This package intends to simplify this exploration process, and it offers two sets of functions for data characterization and data visualization.
It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.
Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale.
The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. The package also includes the option to apply CA-style processing to continuous data e. CytoTree A trajectory inference toolkit for flow and mass cytometry data. CytoTree is a valuable tool to build a tree-shaped trajectory using flow and mass cytometry data.
The application of CytoTree ranges from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation. It offers complete analyzing workflow for flow and mass cytometry data. DegNorm This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy.
The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory for example, in early development.
However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.
ExperimentSubset Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data.
Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset.
The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. These artificial chimeric reads can lead to a large number of false positive structural variation SV calls. Furthermore, this package provides Ridge Regression and Elastic Net. For diamond BLASTX, the output parameter should be: -outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe qframe.
The files in the bucket can be viewed and read as if they are locally stored. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily.
As of April , over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. GSEAmining Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes.
Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology GO classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging.
Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge core subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets.
For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets as wordclouds - The most enriched genes in the leading edge subsets as bar plots.
In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. GSgalgoR A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.
The research related to this package was supported in part by National Science Foundation Award GWENA The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature i. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. Herper Many tools for data analysis are not available in R, but are present in public repositories like conda.
The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages.
The function also has multiple arguements to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations.
Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance.
At last, our methodology does not require replication in either or both of the two comparison groups. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences.
The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection ICP , to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.
Informeasure This package compiles most information measures currently available: mutual information, conditional mutual information, interaction information, partial information decomposition and part mutual information.
Using gene expression profile data, all these estimators can be employed to quantify nonlinear dependence between variables in biological regulatory network inference. The first estimator is used to infer bivariate network while the last four estimators are dedicated to analyze trivariate networks. ISAnalytics In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny.
The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites IS , essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo.
This package is aimed at 1 supporting automation of IS workflow, 2 performing base and advance analysis for IS tracking clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments.
The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks histograms. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files.
With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. MesKit MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing MRS.
This package allows to decipher ITH, infer metastatic routes as well as uncover the underlying process of mutagenesis. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the understanding of cancer cell evolution and its relevance to cancer therapeutics. Input tables are assumed to be acquired using similar but not necessarily identical analytical methods.
Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included.
MethReg Epigenome-wide association studies EWAS detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes.
MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.
It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package.
In addition to applying common analytical workflows the application enables automated analysis report generation. This allows analyzing different biological networks at the same time. Currently, msImpute completes missing values by low-rank approximation of the underlying data matrix.
MSPrep Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. Typically, the analysis involves the quantification of PTM sites i.
They can be used to both plot the summarized results and model the summarized datasets. MultiBaC MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events.
MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered.
We created musicatk MUtational SIgnature Comprehensive Analysis ToolKit to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type.
Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.
These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats.
Nebulosa This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features e.
Seurat and SingleCellExperiment objects can be used within Nebulosa. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption.
It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. Omixer Omixer - an R package for multivariate and reproducible randomization with lab-friendly sample layouts.
Omixer ensures optimal sample distribution across batches with well-documented methods, and can output intuitive sample sheets for the wet lab if needed. Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. PeacoQC This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control.
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