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单细胞DEGs、markers、celltypes | 专辑完结篇
$reg <- ifelse(B_markers_found$abs_logfc>1&as.numeric(B_markers_found$p_val)<0.05,"deg","normal") table $reg <- ifelse(markers_found$abs_logfc>1&as.numeric(markers_found$p_val)<0.05,"deg","normal") table(markers_found $reg <- ifelse(kangB_markers_found$abs_logfc>1&as.numeric(kangB_markers_found$p_val)<0.05,"deg","normal $abs_logfc <- abs(kang_markers_found$avg_log2FC) kang_markers_found$reg <- ifelse(kang_markers_found$ abs_logfc>1&as.numeric(kang_markers_found$p_val)<0.05,"deg","normal") table(kang_markers_found$reg)
4.1K40编辑于 2023-09-19 - 来自专栏YOLO大作战
基于YOLOv8-pose的画笔关键点（bic_markers）检测
添加描述 pose官方在COCO数据集上做了更多测试： 1.1数据集介绍 Ultralytics介绍了bic_markers数据集，这是一个为姿态估计任务设计的多功能集合。 2.bic_markers关键点训练 2.1 新建data/bic_markers/bic_markers.yaml kpt_shape: - 2 - 3 names: 0: marker path /datasets/bic markers train: train.txt val: val.txt 2.2修改ultralytics/cfg/models/v8/yolov8-pose.yaml 默认参数开启训练 from ultralytics.cfg import entrypoint arg="yolo pose train model=yolov8-pose.yaml data=data/bic_markers /bic_markers.yaml" entrypoint(arg) 模型配置如下： 2.4训练结果分析 100个epoch以后 BoxPR_curve.png PosePR_curve.png
2.1K10编辑于 2024-01-16 - 来自专栏技术社区
微信小程序map组件 markers 展示当前位置&&修改标记点图标
('myMap') this.mapCtx.moveToLocation() } 改个标记点的默认样式： 方法一 <view> <map id="myMap" scale="16" markers ="{{markers}}" bindmarkertap="markertap" bindregionchange="regionchange" show-location style="width: 100%; height: 400px;"></map> </view> Page({ data: { markers: [{ iconPath: "../.. [0].latitude"; const log= "markers[0].longitude"; var that = this; wx.getLocation({ type ="{{markers}}" bindmarkertap="markertap" bindregionchange="regionchange" show-location style="width:
5.5K20编辑于 2022-06-16 - 来自专栏生信技能树
展示你的特征基因：带辣椒粉的markers基因umap图
今天来看看如何展示你的特征基因，这个需求在单细胞分析中非常常见。图来自文献《The aged tumor microenvironment limits T cell control of cancer》，于2024年6月25日发表在Nat Immunol杂志上（IF27.8）。如下：
70800编辑于 2025-03-11 - 来自专栏单细胞天地
任意细胞亚群的差异分析
对各个细胞亚群找高表达量的标记基因 代码如下： if (file.exists('sce.markers.all_10_celltype.Rdata')) { load('sce.markers.all 'Mono',sce.markers$cluster) table(kp) cg_sce.markers = sce.markers [ kp ,] # 然后挑选细胞 kp=grepl('Mono', Idents(sce ) ) table(kp) sce=sce[,kp] sce table( Idents(sce )) cg_sce.markers=cg_sce.markers[cg_sce.markers ) cg_markers_df=markers_df[abs(markers_df$avg_logFC) >1,] dim(cg_markers_df) DoHeatmap(subset(sce, downsample ) cg_markers=markers[abs(markers$avg_logFC) >1,] dim(cg_markers) DoHeatmap(subset(sce, downsample = 15
1.5K50发布于 2021-05-18 - 来自专栏生信技能树
超详细T细胞亚群标记基因列表
<- list() # CD4+ T细胞亚群 cell_markers$`Naive T cells` <- c("CCR7", "SELL", "TCF7", "IL7R", "GPR183", ", "CD44") cell_markers$`Th1-like T cells` <- c("UCP2", "APCB1B", "TBX21", "IFNG") cell_markers$`Th17 ", "GZMB", "GZMH", "IFNG") cell_markers$`TSTR` <- c("HSPA1A", "HSPA1B") cell_markers$`TSEN` <- c("低表达 CD27") cell_markers$`p-TEX` <- c("TCF7", "CD27", "CD28", "EOMES") # 非常规T细胞亚群 cell_markers$`NKT cells ") cell_markers$`DNT` <- c("GZMK") # 打印list对象 print(cell_markers) 按照功能分类： R代码版：用的时候可能需要稍微修改一下 #####
2.3K10编辑于 2025-05-25 - 来自专栏单细胞天地
NC单细胞文章复现(三)：Clustering
epithelial_markers, immune_markers = immune_markers, other_markers = other_markers) mean_exprs[[cluster_here = epithelial_markers, immune_markers = immune_markers, other_markers = other_markers) #类似地，clusters2 = epithelial_markers, immune_markers = immune_markers, other_markers epithelial_markers, immune_markers = immune_markers, other_markers = other_markers) # clusters3中unknown epithelial_markers, immune_markers = immune_markers, other_markers = other_markers) # only re-assign
89530发布于 2021-07-02 - 来自专栏数据 学术 商业 新闻
细节拉满！这些小Tips(字体、线类型、标记等)让你绘图更简单~~
、Unfilled markers以及Marker fill styles 三种常用marker进行展示，详细如下： 「filled markers」： import matplotlib.pyplot ', fontsize=14) for ax, markers in zip(axs, split_list(Line2D.filled_markers)): for y, marker in markers', fontsize=14) # Filter out filled markers and marker settings that do nothing. unfilled_markers = 'nothing' and m not in Line2D.filled_markers] for ax, markers in zip(axs, split_list(unfilled_markers [2]Linestyles: https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html#sphx-glr-gallery-lines-bars-and-markers-linestyles-py
87110编辑于 2022-02-17 - 来自专栏单细胞天地
NC单细胞文章复现(三)：复杂热图
#我们读取已经整理好的markers，开始是有53个markers all_markers <- read.table("data/markers_clean.txt", sep = "\t", header markers <- unique(all_markers[all_markers$gene %in% rowData(sceset_final)$hgnc_symbol, ]) length(markers markers$type_heatmap <- markers$type markers$type_heatmap[which(markers$type_heatmap == "luminalprogenitor (markers$type_heatmap == "Tcell")] <- "T cell" markers$type_long_heatmap <- markers$type_long markers 3个immune markers，而且还含有另外1种类型的1个markers，则返回有3个immune markers的类型。
3.4K30发布于 2021-07-02 - 来自专栏生信菜鸟团
对单细胞每个cluster进行批量富集分析
) sce.markers=merge(sce.markers,ids,by.x='gene',by.y='SYMBOL') head(sce.markers) dim(sce.markers ) sce.markers$group=sce.markers$cluster sce.markers=sce.markers[sce.markers$group! #sce.markers$cluster=sce.markers$mygroup dim(sce.markers) head(sce.markers) gcSample ) sce.markers$group=sce.markers$cluster sce.markers=sce.markers[sce.markers$group! #sce.markers$cluster=sce.markers$mygroup dim(sce.markers) head(sce.markers) gcSample
1.5K41编辑于 2023-09-24 - 来自专栏单细胞
单细胞差异基因火山图绘制
为什么火山图中间出现了空白这是因为在进行FindMarkers时默认设置了一定的阈值，可以通过修改参数的阈值来修改自己的火山图（比如下边的示例代码中的min.pct和logfc.threshold参数)markers $sign <- ifelse(markers$p_val_adj < 0.005 & abs(markers$avg_log2FC) > 2, rownames (markers),NA)#当然也可以自定义的，随机的k <- c("TP53","CD34","CD68")markers <- markers %>%mutate(Sign = ifelse(rownames (markers) %in% k, rownames(markers), NA))#自定义阈值log2FC = 0.585padj = 0.05colnames(markers)library(ggplot2 library(ggrepel)markers <- markers %>% mutate(Difference = pct.1 - pct.2) %>% rownames_to_column("
97510编辑于 2024-06-27 - 来自专栏bit哲学院
python 分水岭算法的实现
markers : int or array of int The marker image. Returns ------- image, markers, mask : arrays The validated and formatted arrays. Image will have dtype float64, markers int32, and mask int8. , markers) elif markers.shape ! markers: int, or ndarray of int, same shape as `image` The desired number of markers, or an array
1.2K50发布于 2021-01-06 - 来自专栏机器学习/数据可视化
plotly-express-8-plotly实现散点图
t = np.linspace(0, 10, 50) y = np.sin(t) fig = go.Figure(data=go.Scatter(x=t, y=y, mode="markers")) fig.show ",name="markers")) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines +markers',name='lines+markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, ',name='markers')) fig.show() ? ', marker_color='rgba(25, 182, 193, .9)' )) # fig.update_traces(mode='markers', marker_line_width
1.9K10发布于 2021-03-01 - 来自专栏bit哲学院
cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgproc\src\se
)*0.6, 255, 0)# 确定前景 surface_fg = np.uint8(sure_fg) unknown = cv.subtract(sure_bg, surface_fg) ret, markers = cv.connectedComponents(surface_fg) # markers = np.uint8(markers) markers = markers + 1 markers[unknown == 255] = 0 waterimg[markers == -1] = 255 markers = cv.watershed(gray, markers) 错误名称： cv2.error: src.type() == CV_8UC3 && dst.type() == CV_32SC1 in function 'cv::watershed' 意思是该函数正在尝试将8通道的转为32通道 解决方法 markers = cv.watershed(img, markers) 该函数中img必须为三通道，即不能为灰度图或二值图像，可以用cvtcolor将gray2bgr，这样就不会出错了。
1.7K00发布于 2021-01-16 - 来自专栏算法与编程之美
小程序map切换不同的标记点
==0}}" class="current"> <mapchart markers="{{markers_0}}" polyline="{{polyline}}" longitude="{{longitude ==1}}"> <mapchart markers="{{markers_1}}" polyline="{{polyline}}" longitude="{{longitude}}" latitude ==2}}"> <mapchart markers="{{markers_2}}" polyline="{{polyline}}" longitude="{{longitude}}" latitude ==3}}"> <mapchart markers="{{markers_3}}" polyline="{{polyline}}" longitude="{{longitude}}" latitude _0: [ ]//里面写标记点的相关信息 //动物场馆 markers_1: [ ] //游览点 markers_2: [ ] //卫生间 markers_3: [ ] map.wxss /* pages
1.2K30发布于 2021-11-23 - 来自专栏生信技能树
不知道细胞亚群的生物学功能？clusterProfiler来帮你
.0.2' write.csv(sce.markers,file=paste0(pro,'_sce.markers.csv')) library(dplyr) top10 <- sce.markers ')) library(dplyr) top3 <- sce.markers %>% group_by(cluster) %>% top_n(3, avg_log2FC) DoHeatmap(sce ')) save(sce.markers,file = 'sce.markers.Rdata') 但是各个亚群的基因数量太多， 仍然是肉眼看的累，如果生物学背景知识不够，很难说出个所以然，这个时候clusterProfiler 的compareCluster函数就可以派上用场啦 load(file = 'sce.markers.Rdata') table(sce.markers$cluster) library(clusterProfiler ) sce.markers=merge(sce.markers,ids,by.x='gene',by.y='SYMBOL') gcSample=split(sce.markers$ENTREZID, sce.markers
97820发布于 2021-07-29 - 来自专栏生信技能树
殊路同归的关键单细胞亚群鉴定算法
group.by="celltype",label = T) p1 cox_results=cox_results[rownames(cox_results)%in% rownames(scRNA),] cox_markers ) signature.names <- paste0(names(cox_markers), "_UCell") options(repr.plot.width=6, repr.plot.height <- deg_Scissor_markers[which(deg_Scissor_markers$p_val_adj<0.05),] head(deg_Scissor_markers) cox_markers $up = rownames(deg_Scissor_markers)[deg_Scissor_markers$avg_log2FC>0] cox_markers$down = rownames(deg_Scissor_markers )[deg_Scissor_markers$avg_log2FC < 0] require("VennDiagram") grid.newpage() venn.plot <- venn.diagram
43120编辑于 2023-11-03 - 来自专栏单细胞天地
手把手带你复现NC图表之Figure 2
","nuclear")] All.Markers$pct.diff <- All.Markers$pct.1 - All.Markers$pct.2 All.Markers$Detected.Intracellular $cluster, All.Markers$gene), paste(Sample.level_Markers$cluster, Sample.level_Markers $gene)) All.Markers$p_val_adj.Sample <- Sample.level_Markers$p_val_adj[Marker.match] All.Markers$log2FC.Sample <- All.Markers$gene %in% CGP_pathwaysH$NABA_BASEMENT_MEMBRANES All.Markers$NABA_COLLAGENS <- All.Markers names(All.Markers) All.Markers$type <- factor( apply(All.Markers[,c("NABA_BASEMENT_MEMBRANES","NABA_COLLAGENS
63610编辑于 2023-09-26 - 来自专栏生信补给站
scRNA分析|一（尽）文（力）解决你的单细胞火山图问题
of cluster Epi cluster1.markers <- FindMarkers(sce2, ident.1 = "Epi", min.pct = 0.25) head(cluster1. $threshold="ns"; cluster1.markers[which(cluster1.markers$avg_log2FC > log2FC & cluster1.markers$p_val_adj <padj),]$threshold="up"; cluster1.markers[which(cluster1.markers$avg_log2FC < (-log2FC) & cluster1. markers$p_val_adj < padj),]$threshold="down"; cluster1.markers$threshold=factor(cluster1.markers$threshold 使用ggplot2 点图绘制方式 library(ggrepel) cluster1.markers <- cluster1.markers %>% mutate(Difference = pct.1
12.3K33编辑于 2022-11-03 - 来自专栏全栈程序员必看
显示隐藏高德地图点标注的文本标签「建议收藏」
效果如图 如下代码是百度地图通用的方法，显示隐藏文本标签，但是用在高德地图上不起作用，网上百度无果 hideMarkTitle: function(status) { var markers = this.map.getOverlays(); for(var i = 0; i < markers.length; i++) { if(markers[i].toString () == "[object Marker]") { if(markers[i].getLabel() ! = null) { markers[i].getLabel().setStyle({ display: status }); } ; i++) { this.markers[i].setLabel({ content:"", }); } }else{
3.5K20编辑于 2022-08-27
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