A multi-omics based anti-inflammatory immune signature characterizes Long COVID Syndrome - Kovarik et al.

Supplementary Table S1 related to Figure 1: Student's t-test statistics between long-covid and recovered, long-covid and healthy as well as recovered and healthy are shown. For each identified protein, log2 label-free quantification (LFQ) intensities, numbers of identified peptides, numbers of identified unique peptides as well as the sequence coverage is listed. Supplementary Table S2 related to Figure 2: Student's t-test statistics between long-covid and recovered, long-covid and healthy as well as recovered and healthy are shown. For each identified eicosanoid normalized area under the curve (nAUC) values are listed. Supplementary Table S3 related to Figure 3: Student's t-test statistics between long-covid and recovered, long-covid and healthy as well as recovered and healthy are shown. For each identified metabolite log2 concentrations in µM are listed. Supplementary Table S4 related to Figure 3: Heatmap data matrix. Normalized values generated by dividing the concentration of each lipid through the average concentration of this lipid over all samples.

图1对应补充表S1：本表格展示了长新冠组、康复组与健康组之间的学生t检验统计量。针对每一个鉴定得到的蛋白质，列出了其经log₂转换的无标记定量（label-free quantification, LFQ）强度、鉴定到的肽段数、鉴定到的独特肽段数以及序列覆盖度。
图2对应补充表S2：本表格展示了长新冠组、康复组与健康组之间的学生t检验统计量。针对每一个鉴定得到的类二十烷酸，列出了其归一化曲线下面积（normalized area under the curve, nAUC）数值。
图3对应补充表S3：本表格展示了长新冠组、康复组与健康组之间的学生t检验统计量。针对每一个鉴定得到的代谢物，列出了其以微摩尔（µM）为单位的经log₂转换的浓度值。
图3对应补充表S4：热图数据矩阵。该矩阵中的归一化值通过将每一种脂质的浓度除以该脂质在所有样本中的平均浓度计算得到。