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Unable to preventDefault inside passive event listener due to target being treated as

谷歌浏览器的警告： [Intervention] Unable to preventDefault inside passive event listener due to target being treated

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Unable to preventDefault inside passive event listener due to target being treated as passive 怎么办？

原因：google浏览器为了最快速的相应touch事件，做出的改变。历史：当浏览器首先对默认的事件进行响应的时候，要检查一下是否进行了默认事件的取消。这样就...

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【学习】《R实战》读书笔记（第六章）

29 None 3 77 Treated Male 30 None 4 17 Treated Male 32 Marked 5 36 Treated...Treated Male 59 Marked 9 33 Treated Male 63 None 10 55 Treated Male 63 None 11...30 Treated Male 64 None 12 5 Treated Male 64 Some 13 63 Treated Male 69...None 14 83 Treated Male 70 Marked 15 66 Treated Female 23 None 16 40 Treated Female...41 Marked 20 37 Treated Female 48 None 21 82 Treated Female 48 Marked 22 53 Treated

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R in action读书笔记（6）-第七章：基本统计分析（中）

7.2 频数表和列联表 > library(vcd) > head(Arthritis) ID Treatment Sex Age Improved 1 57 Treated Male 27...Some 2 46 Treated Male 29 None 3 77 Treated Male 30 None 4 17 Treated Male 32 Marked...5 36 Treated Male 46 Marked 6 23 Treated Male 58 Marked 7.2.1 生成频数表函数...行和与行比例可以这样计算： > margin.table(mytable,1) Treatment Placebo Treated 43 41 > prop.table(mytable...5 2 , , Improved = Marked Sex Treatment Female Male Placebo 6 1 Treated 16

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对转录组测序的counts矩阵去除批次效应

treated2fb ... untreated4fb (7 total) varLabels: sizeFactor condition type varMetadata: labelDescription...cts)=rownames(coldata) coldata cts[1:4,1:4] 表达矩阵和表型信息如下： > coldata condition type treated1...treated single_read treated2 treated paired_end treated3 treated paired_end untreated1...treated2 treated3 untreated1 FBgn0000003 0 0 1 0 FBgn0000008 78...type+condition) dds <- DESeq(dds) resultsNames(dds) res treated

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DESeq2转录组差异表达分析实例

coldata)] all(rownames(coldata) == colnames(cts)) 获得了两个数据集 > coldata condition type treated1...treated single-read treated2 treated paired-end treated3 treated paired-end untreated1...single-read untreated3 untreated paired-end untreated4 untreated paired-end > head(cts) treated1...treated2 treated3 untreated1 untreated2 untreated3 FBgn0000003 0 0 1 0...=10 dds<-dds[keep,] dds<-DESeq(dds) restreated

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RNA-seq 差异分析的点点滴滴（2）

head(cts,2) ## untreated1 untreated2 untreated3 untreated4 treated1 treated2 ## FBgn0000003...treated single-read ## treated2fb treated paired-end ## treated3fb treated paired-end...FBgn0261574 FBgn0261575 ## rowData names(0): ## colnames(7): treated1 treated2 ... untreated3 untreated4...sampleFiles treated",list.files(directory),value=TRUE) sampleCondition <- sub("(....FBgn0261575:001 ## FBgn0261575:002 ## rowData names(0): ## colnames(7): treated1fb.txt treated2fb.txt

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更为强大的复杂热图

## 1 treate~ Kal1 37 treated...singl~ Secretory 1.10 ## 2 treate~ Kal1 41 treated paire~ Secretory...1.10 ## 3 treate~ Kal1 50 treated paire~ Secretory 1.10 ## 4 untrea~ Kal1...singl~ Intracel~ 0.329 ## 9 treate~ Ant2 2478 treated paire~ Intracel~...0.329 ## 10 treate~ Ant2 2575 treated paire~ Intracel~ 0.329 ## # ... with 494

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因果推断DiD方法在游戏数据分析中的实践

我们设置了两个哑变量（dummy variable）treated_group和after_treated，分别代表是否实验组、是否已经treated即11月18日。...公式如下： image.png 数据样例如下： roleid is_act thedate treated_group after_treated 0 1 20211116 1 0 1 1 20211116...拟合使用python statsmodels库进行拟合： fit_res = smf.ols('is_act ~ treated_group*after_treated', data=combined_df...0.0295 0.002 14.668 0.000 0.026 0.033 after_treated -0.0162 0.002 -9.636 0.000 -0.019 -0.013 treated_group...:after_treated -0.0195 0.003 -6.859 0.000 -0.025 -0.014 这里的treated_group:after_treated就代表处理效用，系数是负的，

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因果推断（四）断点回归（RD）

************************model summary:********************************* Estimation Equation: y ~ TREATED...-------------------- Intercept 1.0297 0.046 22.267 0.000 0.939 1.120 TREATED...******************model summary customize 1:*************************** Estimation Equation: y ~ TREATED...上述模型表明TREATED有显著影响模型验证 # 模型验证 data_placebo = rdd.truncated_data(data, 'x', yname='y', cut=0) # 任意位置设置断点...随机设置断点在位置0，TREATED影响不显著符合预期总结 RDD能很好的针对政策干预、营销活动的影响效果进行因果推断。

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【学习】【R语言读书会】《R实战》读书笔记（第七章）

Arthritis starting httpd help server … done > head(Arthritis) ID Treatment Sex Age Improved 1 57 Treated...Male 27 Some 2 46 Treated Male 29 None 3 77 Treated Male 30 None 4 17 Treated Male...32 Marked 5 36 Treated Male 46 Marked 6 23 Treated Male 58 Marked 创建和操作相依表的的函数，如图所示。...+ Improved, data=Arthritis) > mytable1 Improved Treatment None Some Marked Placebo 29 7 7 Treated...13 7 21 > margin.table(mytable1, 1) Treatment Placebo Treated 43 41 > margin.table(mytable1

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评估细胞因子活性、免疫细胞极化和细胞间通讯的利器：IREA 分析（二）

projection score is calculated by finding the cosine similarity score between user input and cytokine-treated...or PBS-treated cells....The effect size is the mean difference between projection scores on cytokine-treated cells and on PBS-treated...is.na(post_treated)) treated)) < 2) { return(NA) } else { return(wilcox.test(post_treated, pre_treated

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说好的内参基因稳定不变呢？它确差异表达了吗？

untreated GSM1572234 KD2 - untreated GSM1572235 KD3 - untreated GSM1572236 KD4 - untreated GSM1572237 KD5 - treated...GSM1572238 KD6 - treated GSM1572239 KD7 - treated GSM1572240 KD8 - treated 作者给出来的每个样品一个独立的表达量矩阵txt格式的文本文件...rawcount[1:4,1:4] rawcount['GAPDH',] cg = log2(edgeR::cpm(rawcount)+1)['GAPDH',] gp = rep(c('untreated','treated...t-test data: cg by gp t = 2.7911, df = 5.9258, p-value = 0.03195 sample estimates: mean in group treated...GSE64486_case_vs_control_foldgenes_broad.txt.gz 1.3 Mb GSE64486_treated_vs_control_foldgenes_broad.txt.gz

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2014年测了近600样品的转录组芯片但是仅发表在 PLoS One

因为涉及到3个不同的癌症细胞系，所以这个数据集可以拆分成为下面的3个： GSE50811 Breast cancer cell lines treated with eribulin and paclitaxel...GSE50830 Endometrial cancer cell lines treated with eribulin and paclitaxel GSE50831 Ovarian cancer...cell lines treated with eribulin and paclitaxel 这600样品的转录组芯片，总共是67个细胞系，对照和2种处理，各自3个重复，如下所示： 27 breast..., 19 endometrial, and 21 ovarian cancer cell lines treated with eribulin and paclitaxel.

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TensorFlow中的数据类型

0维张量（标量），列表值将转换为1维张量（向量），列表套列表将被转换成2维张量（矩阵）等等，以下示例来自于TensorFlow for Machine Intelligence.t_0 = 19 # Treated...or "scalar"tf.zeros_like(t_0) # ==> 0tf.ones_like(t_0) # ==> 1t_1 = [b"apple", b"peach", b"grape"] # treated...TypeErrort_2 = [[True, False, False], [False, False, True], [False, True, False]] # treated

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经典教程：全转录数据分析实战

//zenodo.org/record/4710649/files/SRR1019437_RNASEQ_CTL.fastqsanger.gz fastqsanger.gz SRR1019438 BR treated...: 选择Single-end 将输出的 collection 重命名为 Control miRNA trimmed 在 BR treated miRNA 数据集上再次运行上面的步骤. trim_miRNA...miRNA trimmed作为输入，重复上述步骤，并将其重命名为Collapsed BR treated miRNA。...通过提供MiRDeep2 BR treated miRNA作为输入，重复第四步，并将输出重命名为 MiRDeep2 BR treated miRNA 和 MiRDeep2 BR treated miRNA...miRNA 将输出重命名为 BR treated miRNA counts miRNA 差异表达分析：DESeq2 DESeq2是一种基于负二项广义线性模型的差异基因表达分析工具。

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「经验」不适合做AB实验的场景下，通过这4种方式来衡量策略效果

这种通过treated用户去匹配no treated用户的方式，称之为Matching，常见的Matching方式有以下几种，如下图所示。...其一：Matching是以treated群体为标杆去匹配no treated群体，验证的是treatment给实验组用户带来的影响；而Weighting是以大盘用户为标杆去匹配群体，验证的是treatment...03、Regressing Regressing同Matching、Weighting思路完全不同，不再为treated群体样本一一匹配，而是通过预测来估计treated群体样本落在对照组的指标表现情况

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「R」基本统计分析

> library(grid) > library(vcd) > head(Arthritis) ID Treatment Sex Age Improved 1 57 Treated Male...27 Some 2 46 Treated Male 29 None 3 77 Treated Male 30 None 4 17 Treated Male...32 Marked 5 36 Treated Male 46 Marked 6 23 Treated Male 58 Marked > mytable <- with(Arthritis...data=Arthritis) > mytable Improved Treatment None Some Marked Placebo 29 7 7 Treated...Improved Treatment None Some Marked Placebo 0.6744186 0.1627907 0.1627907 Treated

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经典教程：全转录数据分析实战

//zenodo.org/record/4710649/files/SRR1019437_RNASEQ_CTL.fastqsanger.gz fastqsanger.gz SRR1019438 BR treated...: 选择Single-end 将输出的 collection 重命名为 Control miRNA trimmed 在 BR treated miRNA 数据集上再次运行上面的步骤....miRNA trimmed作为输入，重复上述步骤，并将其重命名为Collapsed BR treated miRNA。...通过提供MiRDeep2 BR treated miRNA作为输入，重复第四步，并将输出重命名为 MiRDeep2 BR treated miRNA 和 MiRDeep2 BR treated miRNA...miRNA 将输出重命名为 BR treated miRNA counts miRNA 差异表达分析：DESeq2 DESeq2是一种基于负二项广义线性模型的差异基因表达分析工具。

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RNA-seq 差异分析的点点滴滴（3）

设定因子水平可以通过两种方法实现，一种是通过factor函数： dds$condition treated...dds) res <- results(dds) res 请注意，可以使用以下任一等效命令指定要为其构建结果表的系数或对比度： res treated_vs_untreated...") res treated","untreated")) 这两个命令等价性的一个例外情况是，当使用 contrast...resultsNames(dds) ## [1] "Intercept" "condition_treated_vs_untreated" resLFC treated_vs_untreated", type="apeglm") resLFC Reference [1] Source:

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Unable to preventDefault inside passive event listener due to target being treated as

Unable to preventDefault inside passive event listener due to target being treated as passive 怎么办？

【学习】《R实战》读书笔记（第六章）

R in action读书笔记（6）-第七章：基本统计分析（中）

对转录组测序的counts矩阵去除批次效应

DESeq2转录组差异表达分析实例

RNA-seq 差异分析的点点滴滴（2）

更为强大的复杂热图

因果推断DiD方法在游戏数据分析中的实践

因果推断（四）断点回归（RD）

【学习】【R语言读书会】《R实战》读书笔记（第七章）

评估细胞因子活性、免疫细胞极化和细胞间通讯的利器：IREA 分析（二）

说好的内参基因稳定不变呢？它确差异表达了吗？

2014年测了近600样品的转录组芯片但是仅发表在 PLoS One

TensorFlow中的数据类型

经典教程：全转录数据分析实战

「经验」不适合做AB实验的场景下，通过这4种方式来衡量策略效果

「R」基本统计分析

经典教程：全转录数据分析实战

RNA-seq 差异分析的点点滴滴（3）

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