Jove
Visualize
お問い合わせ

関連する概念動画

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.1K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

11.6K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.6K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.1K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

633
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
633
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

1.3K
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
1.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

From microscope to artificial intelligence: potential and perils in bone marrow evaluation.

Clinical chemistry and laboratory medicine·2026
Same author

Long-term follow-up of the transplant-eligible cohort of the EMN12/HOVON-129 study for primary plasma cell leukemia patients.

Blood cancer journal·2026
Same author

Exploring the Role of Macrophage Marker CD68 in Pediatric Acute Myeloid Leukemia.

International journal of molecular sciences·2026
Same author

Automated Computational Flow Cytometry Correlates Decreasing Neutrophil-to-Lymphocyte Ratio to Improved Survival in NSCLC After Immune Checkpoint Blockade.

Cancer immunology research·2026
Same author

First-year results of the International Leukemia/Lymphoma Target Board for pediatric relapsed and refractory hematological malignancies.

Haematologica·2026
Same author

Cytokine Dynamics Following Initiation of Gender-Affirming Hormone Therapy in Transgender Subjects.

The Journal of clinical endocrinology and metabolism·2026
Same journal

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Refractive Index-Correlated Pseudocoloring for Adaptive Color Fusion in Holotomographic Cytology.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Ensembling Unets for Rare Chromosomal Aberration Detection in Metaphase Images, Uncertainty Quantification, and Ionizing Radiation Dose Estimation.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

OMIP-121: Immune Phenotyping of Canine Peripheral Leukocytes by Mass Cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
関連記事をすべて見る
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する実験動画

Updated: Feb 25, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K

CompensAID: 参照エラーの自動検出ツール

Rosan Olsman1, Sarah Bonte2,3, Mattias Hofmans4,5

  • 1Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
|February 23, 2026
PubMed
まとめ
この要約は機械生成です。

CompensAIDはフローサイトメトリーデータの参照エラーを自動的に検出し,品質管理を改善します. この R ベースのツールは,マーカーの組み合わせを潜在的な不正確さでフラグし,手動検査の負担を軽減します.

キーワード:
慰謝料の支払いについてです.計算式フローサイトメトリー品質管理に関する品質管理参照エラー 参照エラー二次性ステインインデックス混合をなくすこと.

さらに関連する動画

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis
11:08

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis

Published on: June 19, 2018

10.2K
Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

10.0K

関連する実験動画

Last Updated: Feb 25, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K
Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis
11:08

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis

Published on: June 19, 2018

10.2K
Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

10.0K

科学分野:

  • 免疫学 免疫学とは
  • コンピュータ生物学 コンピュータ生物学
  • バイオテクノロジー バイオテクノロジー

背景:

  • フローサイトメトリーデータは,参照制御を用いて数学的に分離する必要があります.
  • 不正確なコントロール (参照誤差) は,フッ素濃度の推定値と人口分布を歪める.
  • マーカーの組み合わせを手動でエラーを検査することは,複雑なパネルや大規模なデータセットでは非実用的です.

研究 の 目的:

  • フローサイトメトリーにおける潜在的な参照エラーを自動的に特定するためのオープンソースのRベースのツールCompensAIDを開発する.
  • フローサイトメトリーデータ分析における品質管理ワークフローのサポートと強化.

主な方法:

  • CompensAIDは,密度ベースのカットオフ検出を使用して,ゲートネガティブとポジティブの集団をゲートします.
  • 2次性ステイン指数 (SSI) は,分割された陽性集団で計算されます.
  • マーカーの組み合わせは,最後のセグメントのSSIが -1.1 以下である場合にフラグ付けされます.

主要な成果:

  • CompensAIDは,従来のフローサイトメトリで0.96の感度を達成し,24の疑わしいマーカーの組み合わせのうち23を特定しました.
  • スペクトルフローサイトメトリーでは,感度が0.74で,28の疑わしい組み合わせのうち21を標識しました.
  • 偽陽性が観察され,しばしば不適切なゲーティングまたは低イベントカウントによるものです.

結論:

  • CompensAIDは,フローサイトメトリーにおける潜在的な参照誤差を検出するための堅牢な方法を提供します.
  • このツールは,手動検査の必要性を大幅に削減し,データの信頼性を高めます.
  • 改善されたフローサイトメトリーデータ分析のために,品質管理パイプラインにCompensAIDの統合が推奨されています.