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関連する概念動画

The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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pH Scale02:41

pH Scale

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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty in Measurement: Significant Figures03:34

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Gene Expression Analyses in Human Follicles
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ALDEx2を用いた差次的発現解析へのスケール不確実性の組み込み

Scott J Dos Santos1, Gregory B Gloor1

  • 1Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.

Current protocols
|February 4, 2026
PubMed
まとめ
この要約は機械生成です。

シーケンスデータの差次的変動解析は、サンプルスケールの不確実性を考慮することで改善される。ALDEx2

キーワード:
ALDEx2RNAシーケンス差次的変動差次的発現メタゲノミクス

さらに関連する動画

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miRNA Expression Analyses in Prostate Cancer Clinical Tissues
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科学分野:

  • 微生物学
  • バイオインフォマティクス
  • ゲノミクス

背景:

  • 差次的変動および発現解析は、シーケンスデータの標準的な解析です。
  • 現在の方法では、真のサンプルスケールに関する情報が不足していることが多く、技術的な変動の誤解釈につながります。
  • 既存の正規化技術は、生物学的なスケールに関する誤った仮定をしており、偽発見率を増加させます。

研究 の 目的:

  • RNAシーケンス、トランスクリプトーム、メタトランスクリプトームデータの差次的発現解析にスケールモデルを組み込むことを実証すること。
  • スケールモデリングが解析結果に与える影響を強調すること。
  • ALDEx2の出力の視覚化方法を提示すること。

主な方法:

  • スケールモデルを構築および適用するためにALDEx2 Rパッケージを利用すること。
  • バルクトランスクリプトームおよびメタトランスクリプトームデータセットで差次的発現解析を実行すること。
  • データ視覚化のために主成分分析を適用すること。

主要な成果:

  • スケールモデルは、正規化における誤った仮定を軽減し、偽発見率を減少させます。
  • スケールモデルを組み込むことで、差次的発現解析の精度が向上します。
  • ALDEx2の出力は、組成的主成分分析を用いて効果的に視覚化できます。

結論:

  • スケールモデルを介してサンプルスケールの不確実性を考慮することは、正確な差次的変動および発現解析に不可欠です。
  • ALDEx2は、標準的なバイオインフォマティクスワークフローにスケールモデリングを統合するためのフレームワークを提供します。
  • このアプローチは、高スループットシーケンスデータからの発見の信頼性を高めます。