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E-GEOD-11908 GSE11908 transcription profiling by array Homo sapiens

Construction of a modular analysis framework for blood Genomics Studies

·发布 Aug. 11, 2008 ·更新 March 15, 2019

412

样本数

412

实验数

芯片平台

相关文献

实验描述

We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.

参考文献

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.

Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, Stichweh D, Blankenship D, Li L, Munagala I, Bennett L, Allantaz F, Mejias A, Ardura M, Kaizer E, Monnet L, Allman W, Randall H, Johnson D, Lanier A, Punaro M, Wittkowski KM, White P, Fay J, Klintmalm G, Ramilo O, Palucka AK, Banchereau J, Pascual V

PMID: 18631455

芯片平台

A-AFFY-33

Affymetrix GeneChip Human Genome HG-U133A [HG-U133A]（211 例）

A-AFFY-34

Affymetrix GeneChip Human Genome HG-U133B [HG-U133B]（201 例）

样本属性

age

1 month, 1 year, 1.5 month, 1.7 month, 10 year, 11 month, 11 year, 12 year, 13 year, 14 year, 15 month, 15 year, 16 year, 17 year, 18 year, 2 month, 2 year, 2.5 month, 28 year, 29 year, 3 month, 3 year, 32 year, 35 year, 4 month, 4 year, 41 year, 42 year, 43 year, 44 year, 45 year, 47 year, 48 year, 5 month, 5 year, 50 year, 51 year, 52 year, 53 year, 54 year, 56 year, 57 year, 58 year, 59 year, 6 year, 60 year, 61 year, 63 year, 64 year, 65 year, 66 year, 68 year, 69 year, 7 year, 71 year, 8 month, 8 year, 9 month, 9 year

cell type

peripheral blood mononuclear cell

disease

abscess, abscess, bacteremia, alcoholic liver cirrhosis, Autoimmune Hepatitis, bacteremia, carcinoid tumor, cellulitis, endocarditis, bacteremia, Fulminant hepatic failure, hepatitis B infection, hepatitis C infection, infection, infectious arthritis, bacteriemia, infectious meningitis, liver disease, Lung Abscess, melanoma, non-alcoholic steatohepatitis, osteomyelitis, osteomyelitis, bacteriemia, pneumonia, primary biliary cirrhosis, sclerosing cholangitis, systemic lupus erythematosus, systemic-onset juvenile idiopathic arthritis, type I diabetes mellitus, urinary tract infection

ethnic group

African American, American, Asian

individual

BM_111601, CB_041002, IDDM_1, IDDM_11, IDDM_14, IDDM_15, IDDM_16, IDDM_17, IDDM_18, IDDM_19, IDDM_20, IDDM_22, IDDM_23, IDDM_24, IDDM_25, IDDM_3, IDDM_4, IDDM_5, IDDM_6, IDDM_7, IDDM_8, IDDM_9, INF_005, INF_012, INF_013, INF_024, INF_030, INF_031, INF_034, INF_040, INF_043, INF_048, INF_057, INF_062, INF_066, INF_067, INF_069, INF_070, INF_074, INF_082, INF_084, INF_086, INF_088, INF_089, INF_090, INF_118, INF_120, INF_133, INF_139, INF_148, INF_149, INF_150, INF_151, INF_152, INF_154, INF_156, INF_161, INF_168, INF_171, INF_179, INF_206, INF_315, MEL_23, MEL_24, MEL_26, MEL_27, MEL_29, MEL_30, MEL_32, MEL_34, MEL_35, MEL_36, MEL_40, MEL_43, MEL_44, MEL_45, MEL_46, MEL_47, MEL_48, MEL_49, MEL_50, MEL_51, MEL_52, MEL_54, MJ_110102, OS_041502A, RA_110701, RET0655, RET1292, RET1297, RET1308, RET1322, RET1323, RET1325, RET1329, RET1340, RET1348, RET1355, RET1364, RET1413, RET1673, RET1684, RET1686, RET1689, RET1700, RET1701, RET1702, RET1706, RET1710, RET1714, RET1754, RET1771, RET1805, RET1814, RET1838, RET1839, RET1841, RET1843, RET1845, RET1847, RET1854, RET1974, RET655, SLE100_050703, SLE105_053003, SLE105_16_0503, SLE106_060203, SLE107_060403, SLE108_061103, SLE111_070303, SLE113, SLE114, SLE117_103003, SLE121_022704, SLE149_20_0603, SLE17_02_1001, SLE19_00_0402, SLE20_060603, SLE21_12_0302, SLE27_14_0202, SLE31_110602, SLE32_061103, SLE35_08_1101, SLE36_20_1001, SLE40_12_0402, SLE41_111502, SLE43_012302, SLE43_102302, SLE45_090902, SLE48_110602, SLE50_010803, SLE51_071702, SLE52_071702, SLE53_042803, SLE56_20_1002, SLE57_072402, SLE60_021604, SLE61_10_1101, SLE62_030504, SLE65_08_1201, SLE66_091602, SLE67_042803, SLE74_031004, SLE75_060203, SLE76_060203, SLE78_021304, SLE79_102402, SLE82_032604, SLE83_02_1102, SLE85_110602, SLE87_110602, SLE88_18_0702, SLE90_112002, SLE90_112202, SLE91_021604, SLE94_060403, SLE96_021804, SLE98_012903, SLE99_030304, sys01_80402, sys02_52802, sys06_53002, sys12_22704, sys12_62702, sys13_63002, sys13_90304, sys14_63002, sys15_70202, sys16_70202, sys19_51602, sys20_71802, sys21_111402, sys21_111704, sys22_52802, sys23_72302, sys24_72302, sys25_92404, sys27_111002, sys28_81302, sys29_81302, sys31_90802, sys32_111402, sys33_92902, sys35_91902, sys37_110502, sys39_111202, sys40_60202, sys42_111402, sys44_112102, sys47_121003, sys47_33104, sys49_62002, sys51_12704, sys51_22003, sys52_50403, sys53_50603, sys56_21804, sys62_33104, sys68_111004, sys68_11304, sys69_22404, sys70_52102, sys74_110304, sys78_101304, sys81

infect

Escherichia coli, Staphylococcus aureus, Methicillin-Resistant Staphylococcus Aureus, Staphylococcus aureus, Methicillin-Susceptible Staphylococcus Aureus

organism

Homo sapiens

organism part

blood

sex

female, male

实验信息

登记号

E-GEOD-11908

GEO 编号

GSE11908

实验类型

transcription profiling by array

物种

Homo sapiens

发布日期

Aug. 11, 2008

更新日期

March 15, 2019

提交者

Damien Chaussabel

外部链接

ArrayExpress 原始页 NCBI GEO：GSE11908

分析服务

Construction of a modular analysis framework for blood Genomics Studies

联系地址

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