公开(公告)号：US06835927B2

公开(公告)日：2004-12-28

申请号：US10272425

申请日：2002-10-15

申请人： Christopher H. Becker ,  Curtis A. Hastings ,  Scott M. Norton

发明人： Christopher H. Becker ,  Curtis A. Hastings ,  Scott M. Norton

IPC分类号： B01D5944

CPC分类号： H01J49/0036

摘要： Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

摘要翻译： 通过对光谱进行归一化，可以从质谱中获得关于化学或生物样品成分的相对定量信息，以产生准确反映负责物种浓度的峰值强度值。 归一化因子是由浓度在一系列样品中保持不变的那些固有成分的峰值强度计算出来的。 发生在不同样品中的组分的相对浓度可以从标准化峰强度估计。 与传统方法不同，不需要内标或附加试剂。 该方法对于蛋白质组学和代谢组学研究中的差异表型特别有用，其中鉴定了样品浓度变化的分子。 这些鉴定的物种可以作为疾病或治疗反应的生物标志物。

公开(公告)号：US07087896B2

公开(公告)日：2006-08-08

申请号：US11023234

申请日：2004-12-27

申请人： Christopher H. Becker ,  Curtis A. Hastings ,  Scott M. Norton ,  Sushmita Mimi Roy ,  Weixun Wang ,  Haihong Zhou ,  Thomas Andrew Shaler ,  Praveen Kumar ,  Markus Anderle ,  Hua Lin

发明人： Christopher H. Becker ,  Curtis A. Hastings ,  Scott M. Norton ,  Sushmita Mimi Roy ,  Weixun Wang ,  Haihong Zhou ,  Thomas Andrew Shaler ,  Praveen Kumar ,  Markus Anderle ,  Hua Lin

IPC分类号： B01D59/44 ,  H01J49/00

CPC分类号： H01J49/0036

摘要： Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

摘要翻译： 通过对光谱进行归一化，可以从质谱中获得关于化学或生物样品成分的相对定量信息，以产生准确反映负责物种浓度的峰值强度值。 归一化因子是由浓度在一系列样品中保持不变的那些固有成分的峰值强度计算出来的。 发生在不同样品中的组分的相对浓度可以从标准化峰强度估计。 与传统方法不同，不需要内标或附加试剂。 该方法对于蛋白质组学和代谢组学研究中的差异表型特别有用，其中鉴定了样品浓度变化的分子。 这些鉴定的物种可以作为疾病或治疗反应的生物标志物。

公开(公告)号：US07197401B2

公开(公告)日：2007-03-27

申请号：US11075370

申请日：2005-03-07

申请人： Curtis A. Hastings

发明人： Curtis A. Hastings

IPC分类号： G06F19/00

CPC分类号： G01N30/8624 ,  G01N30/72 ,  G01N30/8631 ,  G01N30/8634 ,  H01J49/0036 ,  Y10T436/24

摘要： An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

摘要翻译： 描述了从诸如二维液相色谱 - 质谱（LC-MS）数据的非常嘈杂的数据中选出峰的多维数据的自动峰选择方法。 这种数据的特征在于不均匀分布的噪声在不同的维度上变化。 该方法计算数据的每个一维分量的局部噪声阈值。 每个点对于数据集的每个维度都具有应用于其的局部噪声阈值，并且仅当其值超过所有施加的局部噪声阈值时，才将点选择为候选峰。 相邻的候选峰聚类成实际峰。 优选地，该方法被实施为用于分析复杂生物混合物的高通量平台的一部分。

公开(公告)号：US06873915B2

公开(公告)日：2005-03-29

申请号：US10226090

申请日：2002-08-22

申请人： Curtis A. Hastings

发明人： Curtis A. Hastings

IPC分类号： G01N27/447 ,  G01N30/72 ,  G01N30/86 ,  G06F19/00

CPC分类号： G01N30/8624 ,  G01N30/72 ,  G01N30/8631 ,  G01N30/8634 ,  H01J49/0036 ,  Y10T436/24

摘要： An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

摘要翻译： 描述了从诸如二维液相色谱 - 质谱（LC-MS）数据的非常嘈杂的数据中选出峰的多维数据的自动峰选择方法。 这种数据的特征在于不均匀分布的噪声在不同的维度上变化。 该方法计算数据的每个一维分量的局部噪声阈值。 每个点对于数据集的每个维度都具有应用于其的局部噪声阈值，并且仅当其值超过所有施加的局部噪声阈值时，才将点选择为候选峰。 相邻的候选峰聚类成实际峰。 优选地，该方法被实施为用于分析复杂生物混合物的高通量平台的一部分。