Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging

We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.

From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 u...

我们采用47种寡核苷酸条形码标记抗体组，对人类结肠的四个区段（升结肠、横结肠、降结肠、乙状结肠）开展了CODEX（co-detection by indexing，索引式共检测）多重成像实验。随后对获取的图像执行标准CODEX图像处理流程，包括图像拼接、漂移校正、循环合并、背景扣除、反卷积以及最佳焦平面判定，并完成单细胞分割。该流程的输出结果为一个包含近13万个细胞的数据框，其中量化了每个标记物对应的荧光信号强度。我们将该数据框作为输入，应用于5种归一化处理流程，同时对比了Z-score归一化、双对数Z变换归一化、最小-最大归一化以及反双曲正弦归一化这四种方法与原始未处理数据集的效果。我们将这些经过归一化处理的数据框作为输入，应用于4种无监督聚类算法：K均值（k-means）、莱顿（Leiden）聚类、X-shift欧几里得聚类以及X-shift角度聚类。基于聚类输出结果，我们对数据中观测到的细胞所对应的聚类结果进行标注，最终生成20个……