corrected - 腾讯云开发者社区

文章/答案/技术大牛

发布

内存故障分析原

2.8K3 0

aglient芯片原始数据处理

3.9K3 2

您找到你想要的搜索结果了吗？

是的

没有找到

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

2702 0

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["

8582 0

gpr格式的芯片原始数据处理（神技能）

array=6) image.png 3.背景校正和标准化 RGne <- backgroundCorrect(RG, method="normexp", offset=25) ## Array 1 corrected...## Array 2 corrected ## Array 3 corrected ## Array 4 corrected ## Array 5 corrected ## Array 6 corrected...## Array 1 corrected ## Array 2 corrected ## Array 3 corrected ## Array 4 corrected ## Array 5 corrected...## Array 6 corrected MA <- normalizeWithinArrays(RGne) 此时得到的MA文件就类似于表达矩阵的前体了，可以直接拿来做差异分析 4.

3.6K4 1

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

3501 0

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)

1.1K2 0

有些文章的原始数据找不到就算了吧，真不一定有

3353 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

4.2K5 1

matlab实现叠前地震数据的动校正和叠加

实现叠前地震数据的动校正（NMO）和叠加（Stack）function [stack_section, nmo_corrected] = nmo_stack(data, t, offsets, velocity...或与t长度相同的向量]% dt - 时间采样间隔 (单位: 秒)%% 输出参数:% stack_section - 叠加剖面 [时间采样点数 × 1]% nmo_corrected...ones(nt, 1);elseif length(velocity) ~= nt error('速度向量velocity的长度必须与时间轴t一致');end% 初始化动校正后的道集nmo_corrected...round(tx(itr) / dt) + 1; % 确保索引在有效范围内 if tidx >= 1 && tidx corrected...(it, itr) = data(tidx, itr); end endend% 叠加处理 (沿道方向平均)stack_section = mean(nmo_corrected,

2231 0

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

2.6K2 0

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

1.9K3 0

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

2871 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

1.1K4 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 corrected, conc~Time|Subject...Calculate the new steady-stateconcentrations with 24 hour dosing steady_state corrected...steady-stateconcentrations with 24 hour dosing complex_interval_steady_state corrected...见实例： up_to_steady_state corrected, tau=4

4K4 2

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

3K2 0

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

1.2K3 0

方法分享---利用多组学和生态空间分析对组织状态进行定量表征（CODEX、CosMx）

}", flush=True) p_vals_corrected_dict[ct] = pvals_corrected[i]for i in range(len(selected_cell_types...[ct]:.3f}", flush=True) if spot_pvals_corrected[ct] corrected_dict[ct] > 0.05:...print(f"{ct} in whole tissue has p value = {p_vals_corrected_dict[ct]:.3f}", flush=True) print...[i]:.3f}", ha='center', fontsize=8, rotation=0) print(f"{ct} has p value = {pvals_corrected[i]:.3f...}", flush=True) p_vals_corrected_dict[ct] = pvals_corrected[i]for i in range(len(selected_cell_types

3082 0

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" +

4851 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。

7653 0

点击加载更多

内存故障分析原

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）

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。

教你分析后缀为gpr的芯片数据

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

matlab实现叠前地震数据的动校正和叠加

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

手把手教你处理illumina beadchip芯片数据

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

python——opencv入门（一）

R语言实现非房室模型算法

R语言ggplot2做柱形图展示富集分析的结果

SMNN：对单细胞数据进行批次校正

方法分享---利用多组学和生态空间分析对组织状态进行定量表征（CODEX、CosMx）

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）

Vim第三讲置入、替换、更改

相关资讯

热门标签

活动推荐

运营活动

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐