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aglient芯片原始数据处理

3.6K3 2

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有些文章的原始数据找不到就算了吧，真不一定有

GSM2561842_T74013.txt.gz E.bg <- backgroundCorrect(Eraw, method="normexp",offset=50) #背景清除 # Array 1 corrected...# Array 2 corrected # Array 3 corrected # Array 4 corrected # Array 5 corrected # Array 6 corrected...# Array 7 corrected # Array 8 corrected # Array 9 corrected # Array 10 corrected # Array 11 corrected...# Array 12 corrected # Array 13 corrected # Array 14 corrected E.norm <- normalizeBetweenArrays(log2

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深度学习-加快训练速度

Output: "v_corrected". ### START CODE HERE ### (approx. 2 lines) v_corrected["dW" + str...(l + 1)] = v["dW" + str(l + 1)]/(1-np.power(beta1,t)) v_corrected["db" + str(l + 1)] = v["db"...Output: "s_corrected". ### START CODE HERE ### (approx. 2 lines) s_corrected["dW" + str...Inputs: "parameters, learning_rate, v_corrected, s_corrected, epsilon"....parameters["b" + str(l + 1)] - learning_rate * v_corrected["db" + str(l + 1)] / np.sqrt(s_corrected["

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gpr格式的芯片原始数据处理（神技能）

3.2K4 1

教你分析后缀为gpr的芯片数据

差异分析详细代码示例如下 > RG <- backgroundCorrect(RG, method="normexp") Array 1 corrected Array 2 corrected Array...3 corrected Array 1 corrected Array 2 corrected Array 3 corrected > MA <- normalizeWithinArrays(RG)

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Google Earth Engine ——MODIS006MYD09GQ表面反射率产品提供了在没有大气散射或吸收的情况下在地面测量的表面光谱反射率的估计。低层数据经过大气气体和气溶胶的校正。

Low-level data are corrected for atmospheric gases and aerosols....product produced at ideal quality - all bands1: Corrected product produced at less than ideal quality...- some or all bands2: Corrected product not produced due to cloud effects - all bands3: Corrected product...product produced at ideal quality - all bands 1: Corrected product produced at less than ideal quality...- some or all bands 2: Corrected product not produced due to cloud effects - all bands 3: Corrected

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有些文章的原始数据找不到就算了吧，真不一定有

2273 0

R语言ggplot2画气泡图展示富集分析的结果

dat<-read.table("kegg.txt", sep = "\t", header = T) colnames(dat) 根据Corrected.P.Value...筛选结果，我这里把Corrected.P.Value阈值设置为0.0001。...library(tidyverse) dat %>% filter(Corrected.P.Value dat01 dim(dat01) dim(dat) 最基本的图 library...dat01$Input.number/200 ggplot(dat01,aes(x=GeneRatio,y=Term))+ geom_point(aes(size=Input.number,color=Corrected.P.Value...ggplot(dat01,aes(x=GeneRatio,y=reorder(Term,GeneRatio)))+ geom_point(aes(size=Input.number,color=Corrected.P.Value

3.5K5 1

Google Earth Engine ——MYD09Q1.006 Aqua 地表反射率 8-Day Global 250m提供了第1和第2波段的表面光谱反射率的估计，并对气体、气溶胶和瑞利散射等校正

product provides an estimate of the surface spectral reflectance of bands 1 and 2 at 250m resolution and corrected...product produced at ideal quality - all bands1: Corrected product produced at less than ideal quality...- some or all bands2: Corrected product not produced due to cloud effects - all bands3: Corrected product...product produced at ideal quality - all bands 1: Corrected product produced at less than ideal quality...- some or all bands 2: Corrected product not produced due to cloud effects - all bands 3: Corrected

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手把手教你处理illumina beadchip芯片数据

# 背景校正 normal–exponential convolution model > RG <- backgroundCorrect(RG, method="normexp") Array 1 corrected...Array 2 corrected Array 3 corrected .......Array 739 corrected Array 740 corrected Array 741 corrected > # quantile 归一化 > RG <- normalizeBetweenArrays

1.6K3 0

R语言实现非房室模型算法

## Correct nonzero concentrations at time 0to be BLQ. theoph_corrected <- datasets::Theoph theoph_corrected...$conc[theoph_corrected$Time== 0] <- 0 conc_obj_corrected <-PKNCAconc(theoph_corrected, conc~Time|Subject...Calculate the new steady-stateconcentrations with 24 hour dosing steady_state <-superposition(conc_obj_corrected...steady-stateconcentrations with 24 hour dosing complex_interval_steady_state <-superposition(conc_obj_corrected...见实例： up_to_steady_state <-superposition(conc_obj_corrected, tau=4

3.5K4 2

SMNN：对单细胞数据进行批次校正

= sc.read("Sample2.h5ad") sample3 = sc.read("Sample3.h5ad") hvgs = load_from_file("Some HVGs.csv") corrected...sample1, sample2, sample3, var_subset=hvgs, batch_categories = ["N0123X", "N0124X", "T0124X"]) adata = corrected...corrected.results <- SMNNcorrect(batches = list(data_SMNN$batch1.mat, data_SMNN$batch2.mat), batch.cluster.labels...# Output after correction for batch ## Output (1): the batch-corrected expression matrix corrected.results...$corrected[[2]][1:10,1:10] ## Output (2): mutual nearest neighbor information corrected.results$pairs

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使用scran包的MNN算法来去除多个单细胞转录组数据批次效应

接着使用scater包的fastMNN去除批次效应 out <- fastMNN(b1, b2) dim(out$corrected) out$batch reducedDim(combined, "corrected...") <- out$correct plotReducedDim(combined, "corrected", colour_by="batch") + ggtitle("With correction...使用scater包的fastMNN去除批次效应 out <- fastMNN(b1, b2) dim(out$corrected) out$batch reducedDim(combined, "corrected...") <- out$correct plotReducedDim(combined, "corrected", shape_by ="batch",colour_by='Biological_Condition

2.4K2 0

R语言ggplot2做柱形图展示富集分析的结果

(这里根据什么标准设置这个阈值我也不太清楚，欢迎大家留言讨论) library(tidyverse) dat %>% filter(Corrected.P.Value dat01...dim(dat) 最基本的柱形图 library(ggplot2) ggplot(dat01,aes(x=Input.number/500,y=Term))+ geom_col(aes(fill=Corrected.P.Value...dat01,aes(x=Input.number/500, y=reorder(Term,Input.number/500)))+ geom_col(aes(fill=Corrected.P.Value...dat01,aes(x=Input.number/500, y=reorder(Term,Input.number/500)))+ geom_col(aes(fill=Corrected.P.Value

2.5K2 0

Vim第三讲置入、替换、更改

. ---> The end of this line needs to be corrected using the c$ command. $ c$ to be corrected using...the c$ command. ---> The end of this line needs to be corrected using the c$ command.| ---> The end...of this line needs to be corrected using the c$ command. $ 总结要重新置入已经删除的文本内容，请按小写字母 p 键。

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02.改善深层神经网络：超参数调试、正则化以及优化 W2.优化算法（作业：优化方法）

= {} # Initializing first moment estimate, python dictionary s_corrected...Output: "v_corrected". ### START CODE HERE ### (approx. 2 lines) v_corrected["dW" + str...Output: "s_corrected". ### START CODE HERE ### (approx. 2 lines) s_corrected["dW" + str...Inputs: "parameters, learning_rate, v_corrected, s_corrected, epsilon"....= parameters["b" + str(l+1)] - learning_rate*v_corrected["db" + str(l+1)]/(np.sqrt(s_corrected["db" +

3441 0

python——opencv入门（一）

=gamma_trans(img,0.5) cv2.imshow("img",img) cv2.imshow("img_corrected",img_corrected) # cv2.waitKey(0...) # 分通道计算Gamma矫正后的直方图 hist_b_corrected=cv2.calcHist([img_corrected],[0],None,[256],[0,256]) hist_g_corrected...=cv2.calcHist([img_corrected],[1],None,[256],[0,256]) hist_r_corrected=cv2.calcHist([img_corrected],[...None,[256],[0,256]) # 直方图可视化 fig=plt.figure() pix_hists=[ [hist_b,hist_g,hist_r], [hist_b_corrected...,hist_g_corrected,hist_r_corrected] ] pix_vals=range(256) for sub_plt,pix_hist in zip([121,122],pix_hists

9134 0

NLP中的预处理：使用Python进行文本归一化

The exception is '...'. """ corrected = str(text) corrected = re.sub(r'([!?...,;])\1+', r'\1', corrected) corrected = re.sub(r'\.{2,}', r'......whitespaces, removing duplicates. """ corrected = str(text) corrected = re.sub(r"//t"...,r"\t", corrected) corrected = re.sub(r"( )\1+",r"\1", corrected) corrected = re.sub(r"(\n)...\1+",r"\1", corrected) corrected = re.sub(r"(\r)\1+",r"\1", corrected) corrected = re.sub(r

2.6K2 1

资深博导：我以为数据预处理是常识，直到遇到自己的学生

(input_data): # 计算参考光谱（均值光谱） ref_spectrum = np.mean(input_data, axis=0) # 初始化校正后的光谱数据矩阵 corrected_spectra...(input_data.shape[0]): fit = np.polyfit(ref_spectrum, input_data[i, :], 1, full=True) corrected_spectra...[i, :] = (input_data[i, :] - fit[0][1]) / fit[0][0] return corrected_spectra# 应用MSCmsc_spectra = msc...[i, :] = input_data[i, :] - baseline_values return corrected_spectra# 应用基线校正corrected_spectra = baseline_correction..., color='green', alpha=0.1)plt.title('Baseline Corrected Spectra')plt.xlabel('Wavelength Index')plt.ylabel

1150 0

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aglient芯片原始数据处理

有些文章的原始数据找不到就算了吧，真不一定有

深度学习-加快训练速度

gpr格式的芯片原始数据处理（神技能）

教你分析后缀为gpr的芯片数据

Google Earth Engine ——MODIS006MYD09GQ表面反射率产品提供了在没有大气散射或吸收的情况下在地面测量的表面光谱反射率的估计。低层数据经过大气气体和气溶胶的校正。

有些文章的原始数据找不到就算了吧，真不一定有

R语言ggplot2画气泡图展示富集分析的结果

Google Earth Engine ——MYD09Q1.006 Aqua 地表反射率 8-Day Global 250m提供了第1和第2波段的表面光谱反射率的估计，并对气体、气溶胶和瑞利散射等校正

手把手教你处理illumina beadchip芯片数据

R语言实现非房室模型算法

SMNN：对单细胞数据进行批次校正

使用scran包的MNN算法来去除多个单细胞转录组数据批次效应

R语言ggplot2做柱形图展示富集分析的结果

Vim第三讲置入、替换、更改

02.改善深层神经网络：超参数调试、正则化以及优化 W2.优化算法（作业：优化方法）

python——opencv入门（一）

NLP中的预处理：使用Python进行文本归一化

资深博导：我以为数据预处理是常识，直到遇到自己的学生

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