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异常: “Unexpected character (‘ï‘ (code 239)): was expecting a colon to separate field name and value
org.springframework.http.converter.HttpMessageNotReadableException: JSON parse error: Unexpected character (‘ï’ (code 239)): was expecting a colon com.fasterxml.jackson.core.JsonParseException: Unexpected character (‘ï’ (code 239)): was expecting a colon
2.2K20发布于 2020-11-26 - 来自专栏生信小驿站
文献翻译A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM（1）Abstr
从Gene Expression Omnibus数据中收集5个结肠癌样本微阵列数据和癌症基因组图谱（TCGA）。在预处理之后，GSE17537中的数据是使用用于微阵列数据的线性模型（LIMMA）方法鉴定差异表达基因（DEGs）。 DEG进一步进行了基于PPI网络的社区评分和支持向量机（SVM）。然后使用SVM和Cox回归分析通过四个数据集GSE38832，GSE17538，GSE28814和TCGA验证。
70820发布于 2018-08-27 - 来自专栏R语言及实用科研软件
🤩 autoReg | 分分钟输出各种发表级回归图表（二）
colon$status.factor <- factor(colon$status,labels=c("Alive","Died")) colon$obstruct.factor <- factor( colon$obstruct,labels=c("No","Yes")) colon$perfor.factor <- factor(colon$perfor,labels=c("No","Yes")) colon$status.factor <- setLabel(colon$status.factor,"Mortality") colon$rx <- setLabel(colon$rx,"Treatment ") colon$age <- setLabel(colon$age,"Age(Years)") colon$sex.factor <- setLabel(colon$sex.factor,"Sex") (colon$perfor.factor,"Perforation") colon$nodes <- setLabel(colon$nodes,"Positive nodes") fit <- glm
2.4K21编辑于 2022-10-31 - 来自专栏单细胞天地
探索人类肠道免疫细胞图谱
_16S7255675', 'feature_types-Human_colon_16S7255675', 'gene_ids-Human_colon_16S7255676', 'feature_types-Human_colon _16S7255676', 'gene_ids-Human_colon_16S7255677', 'feature_types-Human_colon_16S7255677', 'gene_ids-Human_colon _16S7255678', 'feature_types-Human_colon_16S7255678', 'gene_ids-Human_colon_16S7255679', 'feature_types-Human_colon _16S7255679', 'gene_ids-Human_colon_16S7255680', 'feature_types-Human_colon_16S7255680', 'gene_ids-Human_colon _16S7255682', 'gene_ids-Human_colon_16S8001863', 'feature_types-Human_colon_16S8001863', 'gene_ids-Human_colon
69530发布于 2020-11-25 - 来自专栏作图丫
pathwayPCA：基于主成分分析的通路分析
colon_pathwayCollection是一个典型的通路基因子集的例子。 包括两种元素：一个列表（colon_pathwayCollectionpathways）是15个通路及其对应的通路中基因；一个（colon_pathwayCollectionTERMS）是15个通路的名字 ") CreatePathwayCollection( sets_ls = colon_pathwayCollection$pathways, TERMS = colon_pathwayCollection " ) 6. getPathpVals #计算有监督PCA的通路P值 colon_superpc <- SuperPCA_pVals( colon_Omics, numPCs = 2, # ) getAssay(colon_Omics) getPathwayCollection(colon_Omics) （2）SubsetOmicsResponse 从OmicsSurv中提取response_numh
1.8K20编辑于 2022-03-29 - 来自专栏优雅R
「R」分面生存曲线
::::::::::::::::::::::::::::::::::::::::::::::::::: fit <- survfit(Surv(time, status) ~ sex, data = colon ) ggsurvplot_facet(fit, colon, facet.by = "rx", palette = "jco", pval = TRUE) ? variables: rx and adhere #:::::::::::::::::::::::::::::::::::::::::::::::::::::::: ggsurvplot_facet(fit, colon ::::::::::::::::::::::::::::::::::::::::::::: fit2 <- survfit(Surv(time, status) ~ sex + rx, data = colon ) ggsurvplot_facet(fit2, colon, facet.by = "adhere", palette = "jco", pval = TRUE) ?
57920发布于 2020-07-06 - 来自专栏单细胞天地
通过单细胞图谱改进结直肠癌分类和临床分层
, Left Side colon cancer GSM6048348 Patient 3, Tumor, Right Side colon cancer GSM6048349 Patient 4, Tumor , Left Side colon cancer GSM6048350 Patient 5, Tumor, Right Side colon cancer GSM6048351 Patient 6, Tumor , Right Side colon cancer GSM6048354 Patient 9, Tumor, Left Side colon cancer GSM6048355 Patient 10, Tumor, Left Side colon cancer GSM6048356 Patient 11, Tumor, Right Side colon cancer GSM6048357 Patient 12, Tumor, Right Side colon cancer GSM6048358 Patient 13, Tumor, Right Side colon cancer GSM6048359
54110编辑于 2024-02-22 - 来自专栏生信技能树
scanpy读取空转Visum HD数据&基础分析
# Output Files wget https://cf.10xgenomics.com/samples/spatial-exp/3.0.0/Visium_HD_Human_Colon_Cancer_P1 /3.0.0/Visium_HD_Human_Colon_Cancer_P1/Visium_HD_Human_Colon_Cancer_P1_spatial.tar.gz wget https://cf .10xgenomics.com/samples/spatial-exp/3.0.0/Visium_HD_Human_Colon_Cancer_P1/Visium_HD_Human_Colon_Cancer_P1 /3.0.0/Visium_HD_Human_Colon_Cancer_P1/Visium_HD_Human_Colon_Cancer_P1_feature_slice.h5 wget https:// cf.10xgenomics.com/samples/spatial-exp/3.0.0/Visium_HD_Human_Colon_Cancer_P1/Visium_HD_Human_Colon_Cancer_P1
2.6K01编辑于 2025-03-06 - 来自专栏AustinDatabases
MYSQL 中间件 为什么选择 PROXYSQL VS INNODB CLUSTER
gtid_set = GTID_NORMALIZE(gtid_set); SET colon_pos = LOCATE2(':', gtid_set, 1); WHILE colon_pos ! = LENGTH(gtid_set) + 1 DO SET next_dash_pos = LOCATE2('-', gtid_set, colon_pos + 1); SET next_colon_pos = LOCATE2(':', gtid_set, colon_pos + 1); SET next_comma_pos = LOCATE2(',', gtid_set, colon_pos ) - (next_dash_pos + 1)) - SUBSTR(gtid_set, colon_pos + 1, next_dash_pos - (colon_pos + 1)) + 1; ELSE SET result = result + 1; END IF; SET colon_pos = next_colon_pos; END
83410发布于 2020-08-27 - 来自专栏医学和生信笔记
ChAMP分析甲基化数据：样本信息csv的制作和IDAT读取
"Colon adjacent normal 466T", "Colon cancer tissue 466T", "Colon adjacent normal 469T", "Colon cancer tissue 469T", "Colon adjacent normal 398T", "Colon cancer tissue 398T", "Colon adjacent normal 361T", "Colon cancer tissue 361T", "Colon cancer tissue 467T", "Colon adjacent normal 467T") ) 我们需要把IDAT的文件名字加到
2.4K30编辑于 2022-11-15 - 来自专栏医学和生信笔记
R语言生存曲线的可视化(超详细)
(fit2, colon, facet.by = "adhere", palette = "jco", pval = TRUE) 同时绘制多个生存函数 data(colon ) ## Warning in data(colon): data set 'colon' not found f1 <- survfit(Surv(time, status) ~ adhere, data = colon) f2 <- survfit(Surv(time, status) ~ rx, data = colon) fits <- list(sex = f1, rx = f2) # = colon$status, pfs.time = sample(colon$time), pfs.status = colon$status, sex = colon$sex, rx = colon$rx, adhere = colon$adhere ) # 总体的PFS和OS生存曲线 pfs <- survfit( Surv(pfs.time, pfs.status) ~
3.8K21编辑于 2023-02-14 Rust 宏开发之属性参数解析
: Some( Colon, ), // 字段类型 String qself: None, path: Path { leading_colon 属性名称 builder path: Path { leading_colon qself: None, path: Path { leading_colon , ), // Meta meta: Meta::List { path: Path { leading_colon
47710编辑于 2024-01-22- 来自专栏百味科研芝士
手把手教你绘制NEJM级生存曲线
绘制简单生存曲线 使用colon数据集进行演示。 data(colon) # 加载内置数据集 fit <- survfit(Surv(time,status) ~ sex, data = colon) # 拟合生存曲线 ggsurvplot(fit, data = colon) # 绘制大图 ? ggsurvplot(fit, data = colon, ylim = c(0.4, 1)) # 绘制小图 ? install.packages("eoffice") # 安装包 library(eoffice) # 加载包 # 导出大图 ggsurvplot(fit, data = colon) # 绘制图形
1.8K20发布于 2020-11-13 - 来自专栏生信菜鸟团
第一眼差点就被这个变化倍数唬住了
的数据挖掘文章：《Molecular Pathway Analysis Indicates a Distinct Metabolic Phenotype in Women With Right-Sided Colon Cancer》，针对4个公共的表达量芯片数据集，分别是：SE41258, GSE39582, GSE37892, and GSE14333，做差异分析，这样的话样品数量就很可观了 ： right-sided colon cancers (RCCs) (tumors arising between the cecum and proximal transverse colon) left-sided colon cancers (LCCs) (tumors arising between the distal transverse colon and sigmoid colon, excluding the rectum). GSE14333数据集里面，就有记录CRC病人的Left 和 Right 信息，可以做差异分析，如下所示的病人临床信息 > table(group_list) group_list Colon
14200编辑于 2025-01-20 - 来自专栏作图丫
R包survminer画生存曲线的实用技能，你get了吗？
) ggsurvplot(fit, data = colon, fun = "cumhaz", #定义生存曲线变换的任意函数，"event"累积事件f(y) = 1-y, "cumhaz"累积风险函数 ) fit2 <- surv_fit(Surv(time, status) ~ adhere, data = colon) fit.list <- list(sex = fit1, adhere = fit2 ) ggforest(model) #Fig 19 colon <- within(colon, {sex <- factor(sex, labels = c("female", "male")) ", "muscle", "serosa", "contig.")) }) head(colon)#Fig 20 bigmodel<-coxph(Surv(time, status) ~ sex + rx + adhere + differ + extent + node4,data = colon ) ggforest(bigmodel, fontsize=1.5,#图中注释的相对大小，默认0.7
2.1K31编辑于 2022-03-29 - 来自专栏生信技能树
8个高分杂志的空转数据全套标准分析（多样本）
3,826 (ST-P1, liver), 4,658 (ST-P2, liver), 3,695 (ST-P3, liver), 3,721 (ST-P4, liver), 3,313 (ST-P1, colon ), 4,174 (ST-P2, colon), 4,007 (ST-P3, colon), and 3,902 (ST-P4, colon) spots. $`ST-colon2` An object of class Seurat 41100 features across 4174 samples within 2 assays Active $`ST-colon3` An object of class Seurat 38937 features across 4007 samples within 2 assays Active $`ST-colon4` An object of class Seurat 39968 features across 3902 samples within 2 assays Active
40210编辑于 2025-11-20 - 来自专栏生信技能树
scTrio-seq（逆向收费读文献2019-15）
1.html 本研究纳入的病人队列 在文章附件有详细描述： Patient ID Gender Age AJCC Stage Primary Site CRC01 Female 50 IV Left Colon CRC02 Female 56 IV Right Colon CRC03 Male 60 III Rectum CRC04 Female 71 III Right Colon CRC06 Female 59 III Left Colon CRC09 Male 71 IV Left Colon CRC10 Male 63 III Left Colon CRC11 Female 51 III Left Colon CRC12 Female 49 III Rectum CRC13 Male 51 III Left Colon CRC14 Female 76 III Rectum CRC15 Female 79 IV Right Colon 可以看到实验设计还是蛮周全的，每个病人在不同治疗时间点，都取样了，而且每个样品都使用了scTrio-seq技术进行探索。
99110发布于 2019-10-09 - 来自专栏算法工程师的学习日志
Matlab实现采集电脑的CPU等硬件信息
''} ); for ii=1:numel( txt ) if isempty( txt{ii} ) continue; end % Look for the colon that separates the property name from the value colon = find( txt{ii}==':', 1, 'first' ); if isempty( colon ) || colon==1 || colon==length( txt{ii} ) continue; end fieldName = strtrim ( txt{ii}(1:colon-1) ); fieldValue = strtrim( txt{ii}(colon+1:end) ); if isempty( fieldName )
61910编辑于 2024-02-22 全流程更新----Spatial HD数据全流程更新（数据分析 + 图像识别）
包括共定位等等）实现方法，以10X数据为例###下载数据curl -O https://cf.10xgenomics.com/samples/spatial-exp/3.0.0/Visium_HD_Human_Colon_Cancer /Visium_HD_Human_Colon_Cancer_tissue_image.btf####Visium HD output filecurl -O https://cf.10xgenomics.com /samples/spatial-exp/3.0.0/Visium_HD_Human_Colon_Cancer/Visium_HD_Human_Colon_Cancer_binned_outputs.tar.gztar -xvzf Visium_HD_Human_Colon_Cancer_binned_outputs.tar.gz.└── binned_outputs/ └── square_002um/ 1000 # Number of highly variable genes to use n_clusters: 4 chunks_to_run: []cell_markers: # Human Colon
63410编辑于 2024-10-21- 来自专栏大前端全栈开发
手写JSON.parse和JSON.stringify
currentToken = { type: 'key', value: '' } return onKey } if (currentToken.type === 'colon return onAarry } } function onColon(e) { if (colonReg.test(e)) { currentToken = { type: 'colon -----// [// { type: 'objectStart', value: '{' },// { type: 'key', value: 'name' },// { type: 'colon ', value: ']' },// { type: 'comma', value: ',' },// { type: 'key', value: 'son' },// { type: 'colon :' },// { type: 'objectStart', value: '{' },// { type: 'key', value: 'nickname' },// { type: 'colon
50610编辑于 2023-11-29
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