\u7b2c82\u56de\u30a4\u30f3\u30b7\u30ea\u30b3\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u7814\u7a76\u4f1a\u3092\u4e0b\u8a18\u306e\u3068\u304a\u308a\u884c\u3044\u307e\u3059\u306e\u3067\u3054\u6848\u5185\u3044\u305f\u3057\u307e\u3059\u3002\u4eca\u56de\u306f\u5927\u962a\u5927\u5b66\u514d\u75ab\u5b66\u30d5\u30ed\u30f3\u30c6\u30a3\u30a2\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u30fbAlexis Vandenbon \u5148\u751f\u3092\u8b1b\u5e2b\u3068\u3057\u3066\u304a\u8fce\u3048\u3057\u3001\u300cImmuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system\u300d\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u8b1b\u6f14\u3057\u3066\u3044\u305f\u3060\u304d\u307e\u3059\u3002<\/p>\n
\u30fb\u65e5\u6642\uff1a\u5e73\u621029\u5e742\u670810\u65e5(\u91d1)\u3000 5:00\u20106:30 pm \uff0a\u672c\u8b1b\u6f14\u306f\u533b\u5b66\u7cfb\u7814\u7a76\u79d1\u7cfb\u7d71\u8b1b\u7fa9\u30b3\u30fc\u30b9\u79d1\u76ee\u306e\u6388\u696d\u3068\u3057\u3066\u632f\u66ff\u53ef\u80fd\u3067\u3059\u3002<\/p>\n \u30fb\u6982\u8981\uff1aLarge amounts of experimental data available in public databases contain an enormous potential for elucidating gene regulatory interactions. However, in practice it is difficult to extract new knowledge or hypotheses from such data. One obstacle is the study-specific biases or batch effects that are present in the original data. Although gene coexpression is often used for inferring biological networks, how batch effects influence such networks is not well studied. Here, we prepared a large collection of gene expression data for 24 cell types of the mouse immune system. We found widespread batch effects in this data, and showed that they strongly affect gene coexpression estimates. Removal of batch effects considerably improved the consistency between inferred correlations and prior knowledge. Using the processed data, we constructed Immuno-Navigator, a batch-corrected gene coexpression database. Using our database, we generated hypotheses about candidate regulators in specific immune cells. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin \u03b28 (Itgb8), we experimentally confirmed an association between Itgb8 expression and Treg-specific epigenetic remodelling. We believe that Immuno-Navigator (sysimm.ifrec.osaka-u.ac.jp\/immuno-navigator\/) will be of great use for generating hypotheses for specific cell types in studies of the immune system.<\/p>\n \u30fb\u4e16\u8a71\u4eba\uff1a\u5bfa\u53e3\u4fca\u4ecb\u3001\u9577\ufa11\u6b63\u6717<\/p>\n","protected":false},"excerpt":{"rendered":" \u7b2c82\u56de\u30a4\u30f3\u30b7\u30ea\u30b3\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u7814\u7a76\u4f1a\u3092\u4e0b\u8a18\u306e\u3068\u304a\u308a\u884c\u3044\u307e\u3059\u306e\u3067\u3054\u6848\u5185\u3044\u305f\u3057\u307e\u3059\u3002 … \u7d9a\u304d\u3092\u8aad\u3080
\n\u30fb\u5834\u6240\uff1a\u6771\u5317\u30e1\u30c7\u30a3\u30ab\u30eb\u30fb\u30e1\u30ac\u30d0\u30f3\u30af\u68df<\/a>3\u968e\u5c0f\u4f1a\u8b70\u5ba42
\n\u30fb\u6f14\u984c\uff1aImmuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system
\n\u30fb\u8b1b\u5e2b\uff1aAlexis Vandenbon \uff08\u5927\u962a\u5927\u5b66\u514d\u75ab\u5b66\u30d5\u30ed\u30f3\u30c6\u30a3\u30a2\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\uff09 <\/p>\n