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- 来自专栏R语言及实用科研软件
🤩 granulator | 快速运行多种反卷积方法计算细胞比例！~
~ # print cell type proportions for svr model on ABIS_S0 reference profile decon$proportions$svr_ABIS_S0 ~ # plot cell type proportions for svr model on ABIS_S0 reference profile plot_proportions(deconvoluted # plot cell type proportions plot_deconvolute(deconvoluted = decon, scale = TRUE, labels = FALSE) 反卷积方法的 Benchmarking # benchmark methods by correlating estimated to measured cell type proportions bench <- # plot pearson correlation between predictions and true proportions plot_benchmark(benchmarked = bench
26300编辑于 2025-07-27 - 来自专栏翻译scikit-learn Cookbook
Stratified k-fold K-fold分层
Stratified k-fold is nice because its scheme is specifically designed to maintain the class proportions For larger samples. it probably won't be as big of a deal.We'll then plot the class proportions at each step to illustrate how the class proportions are maintained: 我们将生成一个小的数据集，在这个数据集，我们使用k-fold分层估计，我们想让它小到我们可以看到方差 range(n_folds), kfold_y_props, label="Stratified",color='k', ls='--') ax.set_title("Comparing class proportions 315 396] As we can see, we got roughly the sample sizes of each class for our training and testing proportions
1.1K10发布于 2019-12-16 - 来自专栏CSDNToQQCode
常用的Negative prompt用语-测试模型（Stable-Diffusion）
hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions , extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs 毁容、总体比例、畸形的四肢、缺失的手臂、缺失的腿、额外的手臂、多余的腿、融合的手指、太多的手指、长脖子 出图测试： Negative prompt常用到的单词2 bad anatomy, bad proportions mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions , extra limbs, cloned face, disfigured, out of frame, ugly, extra limbs, bad anatomy, gross proportions
2.4K30编辑于 2023-11-11 - 来自专栏生信技能树
bulk RNA-seq反卷积新包：granulator，来看看！
# print measured cell type proportions (percentages) groundTruth_ABIS[1:5, 1:5] 2.反卷积分析流程 granulator for svr model on ABIS_S0 reference profile decon$proportions$svr_ABIS_S0[1:5, 1:5] dim(decon$proportions $svr_ABIS_S0) decon$proportions$svr_ABIS_S0 rowSums(decon$proportions$svr_ABIS_S0) 该函数返回估算的细胞类型系数和比例 可以使用plot_proportions()函数绘制估算的细胞类型比例。 请注意，细胞类型比例的总和不能超过100%： # plot cell type proportions for svr model on ABIS_S0 reference profile plot_proportions
89210编辑于 2025-06-29 脚本更新----visium数据的细胞类型距离分析
dist } } }colnames(dist_mat) <- rownames(pos)rownames(dist_mat) <- rownames(pos)计算距离，以三种细胞类型为例proportions , distance_wide$PTC_myCAF_mean, paired = TRUE)需要source的脚本distance_comparison <- function(distances, proportions , topic1, topic2, t1_thresh, t2_thresh){ # Filter the desired topics for barcode proportions greater than threshold and only keep barcode and topic column filtered_props_1 <- proportions[proportions[, greater than threshold and only keep barcode and topic column filtered_props_2 <- proportions[proportions
34710编辑于 2025-01-13- 来自专栏CSDNToQQCode
Stable Diffusion——Adetailer面部处理
mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,dehydrated,bad anatomy,bad proportions ,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,dehydrated,bad anatomy,bad proportions ,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra ,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra
82710编辑于 2023-12-03 - 来自专栏机器学习与统计学
Duke@coursera 数据分析与统计推断unit5 inference for categorical variables
一、sampling variability & CLT for proportions ? 四、estimating the difference between two proportions ? ? 五、hypothesis tests for comparing two proportions ? ? ? ? 六、small sample proportion ?
81230发布于 2019-04-10 - 来自专栏天意生信俱乐部
Python做单细胞测序数据分析必备技能
adata, s_genes, g2m_genes) # 可视化细胞周期得分 sc.pl.scatter(adata, x='score_S', y='score_G2M') # 计算每个簇的细胞比例 proportions = adata.obs['leiden'].value_counts(normalize=True) print(proportions) 7. adata, 'leiden', method='t-test') sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False) # 细胞组成分析 proportions = adata.obs['leiden'].value_counts(normalize=True) print("细胞簇比例:\n", proportions) # 可视化细胞组成 proportions.plot (kind='bar') plt.ylabel('Proportion') plt.title('Cell Cluster Proportions') plt.show() 总结： 掌握Python在单细胞转录组分析中的应用
1.1K11编辑于 2025-01-22 - 来自专栏AI算法能力提高班
AIGC绘画图集 | 清爽风格
mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry, (bad anatomy:1.21), (bad proportions mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry, (bad anatomy:1.21), (bad proportions mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry, (bad anatomy:1.21), (bad proportions mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry, (bad anatomy:1.21), (bad proportions
1.1K10编辑于 2023-12-29 - 来自专栏AIGC-AI飞行家
原神盲盒风格：AI绘画Stable Diffusion原神人物公仔实操：核心tag+lora模型汇总
, gross proportions, skin spots, acnes, skin blemishes, DeepNegative,(fat:1.2),facing away, looking away ,gross proportions,text,error,missing fingers,missing arms,missing legs,extra digit, 正面关键词： (masterpiece , gross proportions, skin spots, acnes, skin blemishes, DeepNegative,(fat:1.2),facing away, looking away , gross proportions, skin spots, acnes, skin blemishes, DeepNegative,(fat:1.2),facing away, looking away , gross proportions, skin spots, acnes, skin blemishes, DeepNegative,(fat:1.2),facing away, looking away
3.7K30编辑于 2023-07-22 内容复习---单细胞空间分析之NMF
<- table(seurat_obj$MP_assignment, seurat_obj$orig.ident) sample_mp_proportions <- prop.table(sample_mp_proportions , margin = 2) # 计算MP间的相关性 mp_correlations <- cor(t(sample_mp_proportions), method = "spearman") = sample_mp_proportions, mp_correlations = mp_correlations ))}# 使用示例# Seurat对象名为 seurat_obj# results y = "Meta-Programs", fill = "Correlation")}# 可视化MP在样本中的分布plot_mp_distribution <- function(sample_mp_proportions ) { dist_melt <- reshape2::melt(sample_mp_proportions) ggplot(dist_melt, aes(x = Var2, y = value, fill
50321编辑于 2025-11-15- 来自专栏SpringBoot教程
国风美少女【InsCode Stable Diffusion 美图活动一期】
hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions , extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs
60540编辑于 2023-10-14 - 来自专栏产品研究所
08-可视化操作-探索泰坦尼克灾难数据
绘制一个展示男女乘客比例的扇形图 males = (titanic['Sex'] == 'male').sum() females = (titanic['Sex'] == 'female').sum() proportions = [males, females] plt.pie( proportions, labels = ['Males','Females'], shadow = False,
1.4K10发布于 2019-05-28 - 来自专栏机器学习与统计学
Duke@coursera 数据分析与统计推断 unit1 part2 introduction to data
alternative making a decision ‣ results from the simulations look likethe data → the difference between the proportions are independent) ‣ results from the simulations do not looklike the data → the difference between the proportions
62010发布于 2019-04-10 - 来自专栏HsuHeinrich
AB试验（二）统计基础
如何计算：比例检验可以用Python的proportions_ztest函数，t检验可以用Python的ttest_ind函数。 如果包括0的话意味着两组指标有可能相同，如果不包括0则说明两组指标不同 如何计算：比例检验可以用Python的confint_proportions_2indep函数，t检验可以用Python的tconfint_diff 总结 日常A/B最常见的就是分析概率类指标和均值类指标，经验上，概率类指标采用双尾双样本比例检验（z），可用proportions_ztest函数计算p值，confint_proportions_2indep
1.1K20编辑于 2023-09-18 - 来自专栏CSDNToQQCode
使用高性能服务器训练StableDiffusion——人物模型.safetensors
hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions , extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs
41121编辑于 2024-04-27 - 来自专栏CSDNToQQCode
Stable Diffusion——外挂VAE模型
mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,dehydrated,bad anatomy,bad proportions ,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra
2.9K10编辑于 2023-12-25 - 来自专栏ATYUN订阅号
机器学习项目：使用Keras和tfjs构建血细胞分类模型
letsum = 0; for(var i= 0; i < predictions.length; i++)sum+=predictions[i] //cumpute proportions let proportions= [] for(var i= 0; i < predictions.length; i++){ let prop = (predictions[i]* 100)/sum proportions.push(prop) } return { EOSINOPHIL : proportions[0], LYMPHOCYTE : proportions[1], MONOCYTE : proportions [2], NEUTROPHIL : proportions[3] } } } 总结 这个项目对我来说真的很棒，我学会了如何使用谷歌colab在云上训练ML
1.9K30发布于 2018-09-26 - 来自专栏R语言及实用科研软件
🤩 MuSiC | 基于单细胞数据分析Bulk转录组中细胞组分！~
# Bulk expression matrix bulk.mtx = exprs(GSE50244.bulk.eset) # Estimate cell type proportions Est.prop.GSE50244 # Jitter plot of estimated cell type proportions jitter.fig = Jitter_Est(list(data.matrix(Est.prop.GSE50244 Est.prop.allgene)), method.name = c('MuSiC', 'NNLS'), title = 'Jitter plot of Est Proportions # Create dataframe for beta cell proportions and HbA1c levels m.prop.ana = data.frame(pData(GSE50244. XinT2D.construct.full$num.real, by.col = FALSE) head(XinT2D.construct.full$prop.real) # Estimate cell type proportions
44310编辑于 2025-07-21 - 来自专栏信数据得永生
计算与推断思维 十六、比较两个样本
( '0', proportions(training_counts.column('0')), '1', proportions(training_counts.column('1')) (counts.column(1)), 'Not Cancer', proportions(counts.column(2)) ) dists.barh(0) 与“非癌症”类别的分布相比，“ (1)), 'Not Cancer', proportions(shuffled_counts.column(2)) ) shuffled_dists.barh(0) 这与原始条形图看起来有点不同 (shuffled_counts.column(1)), proportions(shuffled_counts.column(2))) tvds = np.append(tvds, new_tvd (counts.column(1)), proportions(counts.column(2))) # Assuming the null is true, randomly permute
66430编辑于 2022-12-01
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