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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Definite Integral01:29

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Consider a real-valued function defined on a closed interval. One of the fundamental objectives in calculus is to determine the area under the graph of such a function. When an exact computation is not readily available, this area can be estimated by dividing the interval into a finite number of equal subintervals. Each subinterval corresponds to a rectangle whose width is the length of the subinterval and whose height is determined by the value of the function at a selected point within that...
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Indefinite Integrals01:25

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The water inflow rate into a storage tank is not constant but increases over time. Initially, the pump delivers water at a rate of 5 L/min. However, the inflow rate increases by 2 L/min for each additional minute due to rising pressure or system adjustments. This scenario can be described mathematically by a linear function:It is necessary to integrate the inflow rate function to measure the total volume of water added to the tank over time. The total water volume V(t) is obtained by performing...
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単細胞データの包括的な統合

Tim Stuart1, Andrew Butler2, Paul Hoffman1

  • 1New York Genome Center, New York, NY, USA.

Cell
|June 11, 2019
PubMed
まとめ
この要約は機械生成です。

この研究は,遺伝子発現とクロマチンのアクセシビリティを含む多様な単細胞データを統合するための新しい戦略を導入し,細胞のアイデンティティと機能をより深く理解します. この方法はデータの統合を改善し,生物学的発見のためのクロスモダル分析を可能にします.

キーワード:
統合マルチモダルscATAC-seq についてscRNA-seq についてシングルセル単細胞ATACシーケンシング単細胞RNAシーケンシング

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科学分野:

  • * コンピュータ生物学
  • * ゲノミクス
  • * 分子生物学

背景:

  • * シングル・セル・トランスクリプトミクス (scRNA-seq) は,細胞状態の特徴づけを可能にするが,包括的な理解のために他のモダリティと統合する必要があります.
  • * 多様な単細胞データセット (例えば,scRNA-seq,scATAC-seq,空間トランスクリプトミクス) を統合することは,重要な分析的課題です.
  • * 既存のデータ統合方法は,マルチモダルのデータや技術間のデータセットの処理に欠けていることが多い.

研究 の 目的:

  • * 多様な単細胞データセットを"アンカー"する新しい戦略を開発し,技術とモダリティの間の統合を容易にする.
  • 単細胞RNAシーケンシング (scRNA-seq) データを統合するための既存の方法を改善する.
  • * 細胞のアイデンティティ,機能,空間的組織を調査するためのクロスモダルのデータ統合を可能にします.

主な方法:

  • * 異なる単細胞データセットの調和と統合のための"アンカリング"戦略の開発.
  • 異なる技術からのscRNA-seqデータを統合するためにアンカーメソッドの適用.
  • * 染色体差を分析するために,scRNA-seqとトランスポゼアクセシブル染色体配列解析 (scATAC-seq) の単細胞測定を統合する.
  • * タンパク質発現データを骨髄アトラスに投影する.
  • * 空間的な遺伝子発現の推定のためのインサイト遺伝子発現とscRNA-seqデータセットの調和.

主要な成果:

  • * 開発されたアンカリング戦略は,既存の方法と比較して,scRNA-seqデータを統合する上でのパフォーマンスを示しています.
  • * scRNA-seqとscATAC-seqを統合し,インターニューロンのサブセットにおけるクロマチンの変異を明らかにした.
  • * 骨髄アトラスにタンパク質発現データを投影することによってリンパ球群の特徴づけ
  • * in situ と scRNA-seq のデータを調和させることで,空間的な遺伝子発現パターンのトランスクリプトーム全体の割り算が可能になりました.

結論:

  • *アンカリング戦略は,多様な単細胞測定から調和した参照を組み立てるための堅固な枠組みを提供します.
  • * データセットとモダリティの間の生物学的情報の転送を容易にし,分析力を高めます.
  • * より包括的な単細胞データ分析と生物学的発見の道を開く.