Methods for analysis of spectral data and their applications

• US 20050130321A1
• Filed: 04/23/2002
• Published: 06/16/2005
• Est. Priority Date: 04/23/2001
• Status: Abandoned Application

• Current Assignee: Metabometrix Limited
• Original Assignee: Metabometrix Limited
• Inventors: Nicholson, Jeremy K., Graiger, David John, Brindle, Joanne Tracey, Holmes, Elaine, Lindon, John Christopher
• Application Number:
  US10/475,734
• Publication Number:
  US 20050130321A1
• Time in Patent Office: Days
• US Class Current: 436/518
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61P 19/08 for bone diseases, e.g. rachitism, Paget's disease
• CPC Class Codes: G01R 33/4625 Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis
• CPC Class Codes: G01R 33/465 applied to biological material, e.g. in vitro testing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

This invention pertains to chemometric methods for the analysis of chemical, biochemical, and biological data, for example, spectral data, for example, nuclear magnetic resonance (NMR) spectra, and their applications, including, e.g., classification, diagnosis, prognosis.

Claims

1. A method of classifying a sample, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition.

2-182. -182. (canceled)

183. A method of classifying a sample, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition.

184. A method according to claim 183, wherein said sample is a sample from a subject and said predetermined condition is a predetermined condition of said subject.

185. A method according to claim 183, wherein said relating with a predetermined condition is relating with the presence or absence of a predetermined condition.

186. A method according to claim 184, wherein said relating with a predetermined condition is relating with the presence or absence of a predetermined condition.

187. A method according to claim 183, wherein said relating NMR spectral intensity is relating a modulation of NMR spectral intensity, relative to a control value.

188. A method according to claim 184, wherein said relating NMR spectral intensity is relating a modulation of NMR spectral intensity, relative to a control value.

189. A method according to claim 185, wherein said relating NMR spectral intensity is relating a modulation of NMR spectral intensity, relative to a control value.

190. A method according to claim 186, wherein said relating NMR spectral intensity is relating a modulation of NMR spectral intensity, relative to a control value.

191. A method according to claim 183, wherein said one or more predetermined diagnostic spectral windows is:

a single predetermined diagnostic spectral window.

192. A method according to claim 183, wherein said one or more predetermined diagnostic spectral windows is:

a plurality of predetermined diagnostic spectral windows.

193. A method according to claim 183, wherein

said one or more predetermined diagnostic spectral windows is:

a plurality of diagnostic spectral windows, and,

said NMR spectral intensity at one or more predetermined diagnostic spectral windows is;

a combination of a plurality of NMR spectral intensities, each of which is NMR spectral intensity for one of said plurality of predetermined diagnostic spectral windows.

194. A method according to claim 193, wherein said combination is a linear combination.

195. A method according to claim 183, wherein said one or more, predetermined diagnostic spectral windows are associated with one or more diagnostic species.

196. A method according to claim 183, wherein at least one of said one or more predetermined diagnostic spectral windows encompasses a chemical shift value for an NMR resonance of a diagnostic species.

197. A method according to claim 183, each of a plurality of said one or more predetermined diagnostic spectral windows encompasses a chemical shift value for an NMR resonance of a diagnostic species.

224. A method of classifying a subject comprising classifying a sample from said subject by a method according to claim 183.

225. A method of diagnosis of a predetermined condition of a subject comprising classifying a sample from said subject by a method according to claim 183.

226. A method of prognosis of a subject which employs a method according to claim 183.

227. A method of therapeutic monitoring of a subject undergoing therapy which employs a method according to claim 183.

228. A method of evaluating drug therapy and/or drug efficacy which employs a method according to claim 183.

241. A computer program, optionally embodied on a computer readable medium, comprising computer program means adapted to perform a method according to claim 183, when said program is run on a computer.

242. A system comprising:

(a) a first component comprising a device for obtaining NMR spectral intensity data for a sample; and

,

(b) a second component comprising computer system or device, such as a computer or linked computers, operatively configured to implement a method according to claim 183, and operatively linked to said first component.

198. A method of classifying a sample, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in said sample with a predetermined condition.

199. A method according to claim 198, wherein said sample is a sample from a subject and said predetermined condition is a predetermined condition of said subject.

200. A method, according to claim 198, wherein said relating with a predetermined condition is relating with the presence or absence of a predetermined condition.

201. A method, according to claim 199, wherein said relating with a predetermined condition is relating with the presence or absence of a predetermined condition.

202. A method according to claim 198, wherein said relating the amount of, or relative amount of one or more diagnostic species is relating a modulation of the amount of, or relative amount of one or more diagnostic species.

203. A method according to claim 199, wherein said relating the amount of, or relative amount of one or more diagnostic species is relating a modulation of the amount of, or relative amount of one or more diagnostic species.

204. A method according to claim 200, wherein said relating the amount of, or relative amount of one or more diagnostic species is relating a modulation of the amount of, or relative amount of one or more diagnostic species.

205. A method according to claim 201, wherein said relating the amount of, or relative amount of one or more diagnostic species is relating a modulation, of the amount of, or relative amount of one or more diagnostic species.

206. A method according to claim 198, wherein said classification is performed on the basis of an amount, or a relative amount, of a single diagnostic species.

207. A method according to claim 198, wherein said classification is performed on the basis of an amount, or a relative amount, of a plurality of diagnostic species.

208. A method according to claim 198, wherein said classification is performed on the basis of a total amount, or a relative total amount, of a plurality of diagnostic species.

209. A method according to claim 198, wherein:

said one or more diagnostic species is;

a plurality of diagnostic specie; and

,

said amount of, or relative amount of one or more diagnostic species is;

a combination of a plurality of amounts, or relative amounts, each of which is the amount of, or relative amount of one of said plurality of diagnostic species.

210. A method according to claim 209, wherein said combination is a linear combination.

211. A method of classifying a test sample, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

to classify said test sample.

212. A method according to claim 211, wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

and said classifying is classifying said test sample as being a member of one of said known classes.

213. A method according to claim 211, wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group;

and said model is used with a data set for said test sample;

and said classifying is classifying said test sample as being a member of one class selected from said class group.

214. A method according to claim 213, wherein said class group comprises classes associated with said predetermined condition.

215. A method according to claim 211, wherein said modelling method is a multivariate statistical analysis modelling method.

216. A method according to claim 211, wherein said modelling method is a multivariate statistical analysis modelling method which employs a pattern recognition method.

217. A method according to claim 211, wherein said modelling method is, or employs PCA, PLS, or PLS-DA.

218. A method according to claim 211, wherein said modelling method includes a step of data filtering, orthogonal data filtering, or OSC.

219. A method according to claim 211, wherein said modelling data comprise NMR spectral data.

220. A method according to claim 211, wherein said modelling data comprise both NMR spectral data and non-NMR spectral data.

221. A method according to claim 211, wherein said modelling data comprises at least one data set for each of a plurality of modelling samples.

222. A method according to claim 221, wherein each of said data sets comprises NMR spectral data.

223. A method according to claim 221, wherein each of said data sets comprises both NMR spectral data and non-NMR spectral data.

229. A method of identifying a diagnostic species, or a combination of a plurality of diagnostic species, for a predetermined condition, said method comprising the steps of:

(a) applying a multivariate statistical analysis method to experimental data;

wherein said experimental data comprises at least one data comprising experimental parameters measured for each of a plurality of experimental samples;

wherein said experimental samples define a class group consisting of a plurality of classes;

wherein at least one of said plurality of classes is a class associated with said predetermined condition, e.g., a class associated with the presence of said predetermined condition;

wherein at least one of said plurality of classes is a class not associated with said predetermined condition, e.g., a class associated with the absence of said predetermined condition;

wherein each of said experimental samples is of known class selected from said class group;

and;

(b) identifying one or more critical experimental parameters;

wherein each of said critical experimental parameters is statistically significantly different for classes of said class group, e.g., is statistically significant for discriminating between classes of said class group; and

,

(c) matching each of one or more of said one or more critical experimental parameters with said diagnostic species;

or;

(b) identifying a combination of a plurality of critical experimental parameters;

wherein said combination of a plurality of critical experimental parameters is statistically significantly different for classes of said class group, e.g., is statistically significant for discriminating between classes of said class group; and

,

(c) matching each of one or more of said plurality of critical experimental parameters with said combination of a plurality of diagnostic species.

230. A method, according to claim 229, wherein:

one or more of said critical experimental parameters is a spectral parameter,

and

said identifying and matching steps are;

(b) identifying one or more critical experimental spectral parameters; and

,

(c) matching each of one or more of said one or more critical experimental spectral parameters with a spectral feature, e.g., a spectral peak;

and matching one or more of said spectral peaks with said diagnostic species;

or;

(b) identifying a combination of a plurality of critical experimental spectral parameters; and

,

(c) matching each of a plurality of said plurality of critical experimental spectral parameters with a spectral feature, e.g., a spectral peak;

and matching one or more of said spectral peaks with said combination of a plurality of diagnostic species.

231. A method according to claim 229, wherein said multivariate statistical analysis method is a multivariate statistical analysis method which employs a pattern recognition method.

232. A method according to claim 229, wherein said multivariate statistical analysis method is, or employs PCA, PLS or PLS-DA.

233. A method according to claim 229, wherein said multivariate statistical analysis method includes a step of data filtering, a step of orthogonal data filtering, or a step of OSC.

234. A method according to claim 229, wherein said experimental parameters comprise NMR spectral data.

235. A method according to claim 229, wherein said experimental parameters comprise both NMR spectral data and non-NMR spectral data.

236. A method according to claim 229, wherein said class group comprises classes associated with said predetermined condition.

237. A method according to claim 236, said method further comprising the additional step of:

(d) confirming the identity of said diagnostic species.

238. A diagnostic species identified by a method according to claim 229.

239. A method of classification which employs or relies upon one or more diagnostic species identified by a method according to claim 229.

240. An assay for use in a method of classification, which assay relies upon one or more diagnostic species identified by a method according to claim 229.