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                                  PATHOLOGY LYMPHOMA Show tissue menu
                                  BREAST CANCER CARCINOID CERVICAL CANCER COLORECTAL CANCER ENDOMETRIAL CANCER GLIOMA HEAD AND NECK CANCER LIVER CANCER LUNG CANCER LYMPHOMA
                                  MELANOMA OVARIAN CANCER PANCREATIC CANCER PROSTATE CANCER RENAL CANCER SKIN CANCER STOMACH CANCER TESTIS CANCER THYROID CANCER UROTHELIAL CANCER
                                  Dictionary
                                  Lymphoma
                                  Human pathology
                                  Lymphoma
                                  Interactive survival scatter ploti

                                  The Survival Scatter plot shows the clinical status (i.e. dead or alive) for all individuals in the patient cohort, based on the same data that underlies the corresponding Kaplan-Meier plots. Patients that are alive at last time for follow-up are shown in blue and patients who have died during the study are shown in red.

                                  The x-axis shows the expression levels (FPKM) of the investigated gene in the tumor tissue at the time of diagnosis. The y-axis shows the follow-up time after diagnosis (years). Both axes are complimented with kernel density curves demonstrating the data density over the axes. The top density plot shows the expression levels (FPKM) distribution among dead (red) and alive patients (blue). The right density plot shows the data density of the survived years of dead patients with high and low expression levels respectively, stratified using the cutoff indicated by the vertical dashed line through the Survival Scatter plot. This cutoff is automatically defined based on the FPKM cutoff that minimizes the p-score. The cutoff can be changed by dragging the vertical line or by entering a cutoff value in the square labeled "Current cut-off".

                                  Under the Survival Scatter plot the p-score landscape (black curve; left axis) is shown together with dead median separation (red curve; right axis). Dead median separation is the difference in median mRNA expression between patients who have died with high and low expression, respectively. It is calculated as follows: median FPKM expression of dead patients with high expression - median FPKM expression of dead patients with low expression. This is intended to aid the user in visually exploring custom cutoffs and the associated p-scores and dead median separation.

                                  Individual patient data is displayed and can be filtered by clicking on one or more of the category buttons on the top of the page. Categories describing expression level and patient information include: high, low, alive, dead, female, male and tumor stages. The scale of the x-axis can be toggled between linear and log-scale by clicking on the "x log" button. Mouse-over function shows TCGA ID, patient information and mRNA expression (FPKM) for each patient.

                                  & Survival analysisi

                                  Kaplan-Meier plots summarize results from analysis of correlation between mRNA expression level and patient survival. Patients were divided based on level of expression into one of the two groups "low" (under cut off) or "high" (over cut off). X-axis shows time for survival (years) and y-axis shows the probability of survival, where 1.0 corresponds to 100 percent.

                                    Survival analysis data not available.
                                  LYMPHOMA - Protein expressioni

                                  A mouse-over function shows sample information and annotation data. Click on an image to view it in a full screen mode. Samples can be filtered based on level of antibody staining by selecting one or several of the following categories: high, medium, low and not detected. The assay and annotation is described here.

                                  Note that samples used for immunohistochemistry by the Human Protein Atlas do not correspond to samples in the TCGA dataset.

                                  Pending cancer tissue analysis

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                                  The Human Protein Atlas project is funded
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