L1000 data 知識(shí)點(diǎn)+處理流程

L1000 data proceeds through a data processing pipeline outlined in the figure below. Briefly, the pipeline captures raw data from Luminex FlexMap 3D scanners as it is generated, deconvolutes 978 transcripts from only 500 Luminex bead colors, normalizes the data based on 80 invariant control genes, infers the expression of the non-measured transcripts, determines differentially expressed genes following a perturbation compared to controls, and generates composite signatures across biological replicates. Along the way the data are subjected to rigorous quality control filters at both the sample and plate level.


Data processing pipeline

Level 1

Level 1 -LXB - raw fluorescent intensity (FI) values measured for every bead detected by Luminex scanners. The FI is proportional to the amount of amplicon bound to the bead, and hence also proportional to the transcript abundance of the genes that particular bead is interrogating. Each 384-well plate generates 384 LXB files, where each file contains a fluorescent intensity value for each observed bead in the well. Here, the data from each perturbagen treatment is referred to as a profile, experiment, or instance.

Level2

Level 2 - GEX - Gene expression levels for the 978 landmark genes, deconvoluted from the measured fluorescent intensity values. (See supplementary information in Subramanian, et al., 2017 for details on peak deconvolution.) Here, the data from each perturbagen treatment is referred to as a profile, experiment, or instance.

Level3

Level 3a - NORM - Gene expression (GEX, Level 2) are normalized to invariant gene set curves and quantile normalized across each plate. Here, the data from each perturbagen treatment is referred to as a profile, experiment, or instance.

Level 3b - INF- Additional values for 11,350 additional genes not directly measured in the L10000 assay are inferred based on the normalized values for the 978 landmark genes.

Level4

Level 4 - ZS - Z-scores for each gene based on Level 3 with respect to the entire plate population. This comparison of profiles to their appropriate population control generates a list of differentially expressed genes.

Level5

Level 5 - MODZ - replicate-collapsed z-score vectors based on Level 4. Replicate collapse generates one differential expression vector, which we term a signature. Connectivity analyses are performed on signatures.

For levels 1 and 2, values are present for only the 978 landmark features. For levels 3-5, values are present for each of the 12,328 genes (978 landmark plus 11,350 inferred).

The code for the data processing pipeline is available in the cmapM GitHub repository. The procedure to replicate each step the pipeline along with sample data are detailed here.

最后編輯于
?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請(qǐng)聯(lián)系作者
  • 序言:七十年代末窃判,一起剝皮案震驚了整個(gè)濱河市,隨后出現(xiàn)的幾起案子喇闸,更是在濱河造成了極大的恐慌袄琳,老刑警劉巖,帶你破解...
    沈念sama閱讀 211,042評(píng)論 6 490
  • 序言:濱河連續(xù)發(fā)生了三起死亡事件燃乍,死亡現(xiàn)場(chǎng)離奇詭異唆樊,居然都是意外死亡,警方通過查閱死者的電腦和手機(jī)刻蟹,發(fā)現(xiàn)死者居然都...
    沈念sama閱讀 89,996評(píng)論 2 384
  • 文/潘曉璐 我一進(jìn)店門逗旁,熙熙樓的掌柜王于貴愁眉苦臉地迎上來,“玉大人舆瘪,你說我怎么就攤上這事片效。” “怎么了英古?”我有些...
    開封第一講書人閱讀 156,674評(píng)論 0 345
  • 文/不壞的土叔 我叫張陵淀衣,是天一觀的道長(zhǎng)。 經(jīng)常有香客問我召调,道長(zhǎng)膨桥,這世上最難降的妖魔是什么? 我笑而不...
    開封第一講書人閱讀 56,340評(píng)論 1 283
  • 正文 為了忘掉前任唠叛,我火速辦了婚禮国撵,結(jié)果婚禮上,老公的妹妹穿的比我還像新娘玻墅。我一直安慰自己介牙,他們只是感情好,可當(dāng)我...
    茶點(diǎn)故事閱讀 65,404評(píng)論 5 384
  • 文/花漫 我一把揭開白布澳厢。 她就那樣靜靜地躺著环础,像睡著了一般囚似。 火紅的嫁衣襯著肌膚如雪。 梳的紋絲不亂的頭發(fā)上线得,一...
    開封第一講書人閱讀 49,749評(píng)論 1 289
  • 那天饶唤,我揣著相機(jī)與錄音,去河邊找鬼贯钩。 笑死募狂,一個(gè)胖子當(dāng)著我的面吹牛,可吹牛的內(nèi)容都是我干的角雷。 我是一名探鬼主播祸穷,決...
    沈念sama閱讀 38,902評(píng)論 3 405
  • 文/蒼蘭香墨 我猛地睜開眼,長(zhǎng)吁一口氣:“原來是場(chǎng)噩夢(mèng)啊……” “哼勺三!你這毒婦竟也來了雷滚?” 一聲冷哼從身側(cè)響起,我...
    開封第一講書人閱讀 37,662評(píng)論 0 266
  • 序言:老撾萬(wàn)榮一對(duì)情侶失蹤吗坚,失蹤者是張志新(化名)和其女友劉穎祈远,沒想到半個(gè)月后,有當(dāng)?shù)厝嗽跇淞掷锇l(fā)現(xiàn)了一具尸體商源,經(jīng)...
    沈念sama閱讀 44,110評(píng)論 1 303
  • 正文 獨(dú)居荒郊野嶺守林人離奇死亡车份,尸身上長(zhǎng)有42處帶血的膿包…… 初始之章·張勛 以下內(nèi)容為張勛視角 年9月15日...
    茶點(diǎn)故事閱讀 36,451評(píng)論 2 325
  • 正文 我和宋清朗相戀三年,在試婚紗的時(shí)候發(fā)現(xiàn)自己被綠了牡彻。 大學(xué)時(shí)的朋友給我發(fā)了我未婚夫和他白月光在一起吃飯的照片躬充。...
    茶點(diǎn)故事閱讀 38,577評(píng)論 1 340
  • 序言:一個(gè)原本活蹦亂跳的男人離奇死亡,死狀恐怖讨便,靈堂內(nèi)的尸體忽然破棺而出充甚,到底是詐尸還是另有隱情,我是刑警寧澤霸褒,帶...
    沈念sama閱讀 34,258評(píng)論 4 328
  • 正文 年R本政府宣布伴找,位于F島的核電站,受9級(jí)特大地震影響废菱,放射性物質(zhì)發(fā)生泄漏技矮。R本人自食惡果不足惜,卻給世界環(huán)境...
    茶點(diǎn)故事閱讀 39,848評(píng)論 3 312
  • 文/蒙蒙 一殊轴、第九天 我趴在偏房一處隱蔽的房頂上張望衰倦。 院中可真熱鬧,春花似錦旁理、人聲如沸樊零。這莊子的主人今日做“春日...
    開封第一講書人閱讀 30,726評(píng)論 0 21
  • 文/蒼蘭香墨 我抬頭看了看天上的太陽(yáng)。三九已至郁副,卻和暖如春,著一層夾襖步出監(jiān)牢的瞬間豌习,已是汗流浹背存谎。 一陣腳步聲響...
    開封第一講書人閱讀 31,952評(píng)論 1 264
  • 我被黑心中介騙來泰國(guó)打工, 沒想到剛下飛機(jī)就差點(diǎn)兒被人妖公主榨干…… 1. 我叫王不留肥隆,地道東北人墩虹。 一個(gè)月前我還...
    沈念sama閱讀 46,271評(píng)論 2 360
  • 正文 我出身青樓,卻偏偏與公主長(zhǎng)得像诫钓,于是被迫代替她去往敵國(guó)和親旬昭。 傳聞我的和親對(duì)象是個(gè)殘疾皇子,可洞房花燭夜當(dāng)晚...
    茶點(diǎn)故事閱讀 43,452評(píng)論 2 348

推薦閱讀更多精彩內(nèi)容