Glossary

50. Glossary#

Algorithm#
Algorithms#: A pre-defined set of instructions to solve a problem.
AnnData#
AnnDatas#: A Python package for annotated data matrices. The primary data structure used in the scverse ecosystem.
Barcode#
Barcodes#
Bar code#
Bar codes#: Short DNA barcode fragments (“tags”) that are used to identify reads originating from the same cell. Reads are later grouped by their barcode during raw data processing steps.
Batch effect#: Technical confounding factors in an experiment that cause dataset distribution shifts. Usually lead to inaccurate conclusions if the causes of the batch effects are correlated with outcomes of interest in an experiment and should be accounted for (usually removed).
Benchmark#: An (independent) comparison of performance of several tools with respect to pre-defined metrics.
Bulk RNA sequencing#: Contrary to single-cell sequencing, bulk sequencing measures the average expression values of several cells. Therefore, resolution is lost, but bulk sequencing is usually cheaper, less laborious and faster to analyze.
Cell#: The fundamental unit of life. Consists of cytoplasm enclosed within a membrane that contains many biomolecules such as proteins and nucleic acids. Cells acquire specific functions, transition to cell types, divide, communicate and keep the organism going. Learning about the structure, activity and communication of cells helps deciphering biology.
Cell barcode#: See barcode
Cluster#
Clusters#: A group of a population or data points that share similarities. In single-cell, clusters usually share a common function or marker gene expression that is used for annotation (see cell type annotation).
Cell type annotation#: The process of labeling groups of clusters of cells by cell type. Commonly done based on cell type specific markers, automatically with classifiers or by mapping against a reference.
Cell type#: Cells that share common morphological or phenotypic features.
Cell state#: Cells can be annotated according to cell type or other cell states as defined by the cell-cycle, perturbational state or other features.
Chromatin#: The complex of DNA and proteins efficiently packaging the DNA inside the nucleus and involved in regulating gene expression.
Demultiplexing#: The process of determining which sequencing reads belong to which cell using barcodes.
DNA#: DNA is the acronym of Deoxyribonucleic acid. It is the organic chemical storing hereditary information and instructions for protein synthesis. DNA gets transcribed into RNA.
Doublets#: Reads obtained from droplet based assays might be mistakenly associated to a single cell while the RNA expression origins from two or more cells (a doublet).
Dropout#: A gene with low expression that is observed in one cell, but not in other cells of the same cell type. The reason for dropouts are commonly low amounts of mRNA expression in cells and the general stochasticity of mRNA expression. Dropouts are one of the reasons why scRNA-seq data is sparse.
Drop-seq#: A protocol for scRNA-seq that separates cells into nano-liter sized aqueous droplets enabling large-scale profiling.
FASTQ reads#: Sequencing reads that are saved in the FASTQ format. FASTQ files are then used to map against the reference genome of interest to obtain gene counts for cells.
Gene expression matrix#: A cell (barcode) by gene (scverse ecosystem) or gene by cell (barcode) matrix storing counts in the cell values.
Imputation#: The replacement of missing values with usually artificial values.
Indrop#: A Droplet based protocol for scRNA-seq.
Library#: Also known as sequencing library. A pool of DNA fragments with attached sequencing adapters.
MuData#: A Python package for multimodal annotated data matrices. The primary data structure in the scverse ecosystem for multimodal data.
muon#: A Python package for multi-modal single-cell analysis in Python by scverse.
Negative binomial distribution#: A discrete probability distribution that models the number of successes in a sequence of independent and identically distributed Bernoulli trials before a specified number of failures.
Pipeline#: Also often times denoted as workflow. A pre-specified selection of steps that are commonly executed in order.
RNA#: Ribonucleic acid. Single-stranded nucleic acid present in all living cells that encodes and regulates gene expression.
RT-qPCR#: Quantitative reverse transcription PCR (RT-qPCR) monitors the amplification of a targeted DNA molecule during the PCR.
PCR#: Polymercase chain reaction (PCR) is a method to amplify sequences to create billions of copies. PCR requires primers, which are short synthetic DNA fragments, to select the genome segments to be amplified and subsequently multiple rounds of DNA synthesis to amplify the targeted segments.
Poisson distribution#: Discrete probability distribution denoting the probability of a specified number of events occurring in a fixed interval of time or space with the events occurring independently at a known constant mean rate.
Promoter#: Sequence of DNA to which proteins bind to initiate and control transcription.
Pseudotime#: Latent and therefore unobserved dimension reflecting cells’ progression through transitions. Pseudotime is usually related to real time events, but not necessarily the same.
scanpy#: A Python package for single-cell analysis in Python by scverse.
scverse#: A consortium for fundamental single-cell tools in the life sciences that are maintaining computational analysis tools like scanpy, muon and scvi-tools. See: https://scverse.org/
Spike-in RNA#: RNA transcripts of known sequence and quantity to calibrate measurements in RNA hybridization steps for RNA-seq.
Trajectory inference#: Also known as pseudotemporal ordering. The computational recovery of dynamic processes by ordering cells by similarity or other means.
Unique Molecular Identifier (UMI)#: Specific type of molecular barcodes aiding with error correction and increased accuracy during sequencing. UMIs unique tag molecules in sample libraries enabling estimation of PCR duplication rates.