Composition of Cellular Subsets by Flow Cytometry Identifies Differences Between MDS Subtypes and Aplastic Anemia but No Differences Are Identified Between Cases with and without Monosomy 7.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3802-3802
Author(s):  
Ester Mejstrikova ◽  
Vendula Pelkova ◽  
Michaela Reiterová ◽  
Martina Sukova ◽  
Zuzana Zemanova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Flow Cytometry ◽  
B Cells ◽  
B Cell ◽  
Aplastic Anemia ◽  
Principal Component ◽  
Component Analysis ◽  
Monosomy 7 ◽  
Parametric Statistics ◽  
Non Parametric

Abstract Abstract 3802 Poster Board III-738 Introduction Monosomy 7 or del(7q) are frequent cytogenetic abnormalities in children with myelodysplastic syndrome (MDS) and associates with poor prognosis. MDS globally affects all cellular subsets in bone marrow and in peripheral blood. We asked whether flow cytometry (FC) can separate individual subtypes of MDS from each other and from aplastic anemia (SAA) and whether in individual subtypes of childhood MDS can separate patients with and without monosomy 7. Patients/analyzed parameters In total we analyzed 94 children with centrally analyzed immunophenotype in the reference lab who were diagnosed and treated for MDS or SAA between 1998 and 2009. In total we analyzed 14 patients with refractory cytopenia, 37 patients with advanced forms of MDS (JMML 10, RAEB 25, CMML 2) and 43 patients with SAA. Monosomy 7/del(7q) was present in 17 patients (RC 6, JMML 3, RAEB 8). Analyzed parameters were as follows: B cells, CD10+CD19+, CD19+45dim/neg, CD19+34+, CD19/CD34 ratio, CD34+, CD117 cells, CD34+38dim/neg, CD3+, CD3+4+, CD3+8+, CD3+HLADR+. Statistics We analyzed all parameters using non parametric tests (Mann-Whitney, Kruskal Wallis) and principal component analysis (PCA). Results Principal component analysis of all analyzed patients together clearly separates advanced forms of MDS from RC and SAA, the most contributing factor being the number of CD34 and CD117+ cells. In non parametric statistics following factors significantly differ among MDS subtypes and SAA (Kruskal-Wallis): CD19, CD117, CD34, CD3, CD3+4+, CD8+ and CD3+HLADR+. RC and SAA patients are separated mainly by the number of B cells and the CD34:CD19 ratio. In addition, the following parameters differ between RC and SAA (Mann-Whitney): CD34, CD117 and CD3+HLADR+. Unlike the CD34:CD19 ratio, the number of CD19+34+ precursors does not differ between RC and SAA patients. Patients with monosomy 7 do not differ from the remaining patients when all MDS patients are analyzed together or separately in the respective subgroups (RC, non RC, JMML) by PCA or by non parametric statistics. Conclusion PCA separates advanced MDS forms from RC and SAA. Advanced forms of MDS are characterized by increased percentage of CD34+ and CD117+ cells compared to RC and SAA patients. The global reduction of B cell progenitor compartment is pronounced especially in non-JMML cases of MDS, whereas SAA patients typically present with isolated reduction of cells at early stages (CD19+34+) of B cell development. Patients with monosomy 7 cluster within the respective disease category, they do not form own cluster in PCA. Supported by MSMT VZ MSM0021620813, MZO 00064203 VZ FNM, MZO VFN2005, IGA NR/9531-3, NPV 2B06064. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Multiparameter Phosphoprofiling of the B-Cell Receptor Pathway in Waldenström’s Macroglobulinemia

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 141-141 ◽  
Author(s):  
Kimon V Argyropoulos ◽  
Carly GK Ziegler ◽  
Gregoire Altan-Bonnet ◽  
Ahmet Dogan ◽  
Marcel R.M. van den Brink ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
B Cells ◽  
B Cell ◽  
Healthy Donor ◽  
Principal Component ◽  
Cell Receptor ◽  
Component Analysis ◽  
Differential Signaling ◽  
Bcr Signaling ◽  
Cd20 Expression

Abstract Signaling orchestrates all aspects of cellular homeostasis and therefore altered responses to extrinsic stimuli are substantial to cancer pathogenesis. Physiological B-cell survival, proliferation and differentiation is highly dependent on the B-cell Receptor (BCR) pathway. In B-cell lymphomas this pathways is highly conserved and constitutively activated, as a result of genetic, epigenetic and microenvironmental alterations. In the context of the recently discovered MYD88 L265P and CXCR4-WHIM mutations, most signaling studies in Waldenström’s Macroglobulinemia (WM) have focused on TLR and CXCR4 signaling. Nevertheless, BCR signalosome and its’ role in the biology of WM remains poorly understood. Moreover, while novel BCR-directed biological agents, like ibrutinib, show activity in WM, there still is no BCR-specific therapeutic rationale for the treatment of WM. Over the last decade, a rapidly emerging technology used for signaling studies is phospho-flow cytometry, which permits the tracking of multiple intracellular signaling molecules at a single-cell level. The application of single-cell phosphoprofiling in WM bone marrow samples enabled us to characterized aberrant BCR signaling in both an inter-patient and inter-clonal setting. Overall, primary WM cells exhibited profound remodeling of the BCR-pathway, compared to normal B-cells. In basal state, cells had a heterogeneous pattern of activation for almost all the proteins tested, with Btk (p<0.01), PLCγ2, ERK (p<0.05) and Akt (p<0.001) being the most highly phosphorylated. Upon BCR stimulation, WM cells exhibited pronounced proximal and distal hyperactivation, as reflected by the robust induction of LYN (p<0.05) and PLCγ2 (p<0.001)and ERK (p<0.05) phosphorylation. WM cells’ BCR hyperresponsiveness could be explained by the highest levels of surface IgM expression, which positively correlated with the BCR-stimulation potential of each patient. However, in the presence of H2O2, a pan-phosphatase inhibitor,healthy donor B-cells had no significant differenece from WM cells in the activation of LYN, PLCγ2 and ERK, providing evidence that a second mechanism for BCR signaling potentiation could be the loss of phosphatase regulation. Principal component analysis (PCA) showed that BCR phosphosignatures are WM-specific and can discriminate normal B-cells from clonal WM cells and unsupervised hierarchichal clustering revealed a pronounced signaling heterogeneity among WM patients. Interestingly, a pattern of increased basal levels of activation and low BCR signaling capacity, was seen in patients with indolent disease, who were under observation. On the contrary, patients with more aggressive disease exhibited high BCR activation, suggesting that the utilization of the pathway may be of highest importance during disease progression. Finally, as part of an ongoing ibrutinib clinical trial in patients with relapsed/refractory WM, WM cells showed a significant normalization in key signaling nodes over the course of treatment. In an interclonal setting, we observed that CD20low cells exhibited impaired BCR signaling, in both proximal and distal levels, while BCR hyporesponsiveness was not restored in the presence of H2O2, suggesting that this is not an epiphenomenon of increased phosphatase input. Since CD20low cells additionally expressed lower levels of CD19, we hypothesize that this WM clonal subpopulation exhibits impaired BCR activation due to the loss of positive input by CD20- and CD19-costimulation. The presence of this novel BCR insensitive subclone, which we plan to further characterize, could be of great clinical importance for the biology of the disease and the efficacy of BCR-directed therapies. Figure 1 (a) Basal, aIgM induced and aIgM+H2O2 induced phosphorylation of key nodal signaling proteins in WM (red) and Healthy Donor B-cells (black), (b) Principal component analysis of the generated phosphosignatures distinguishes WM from Healthy Donor B-cells, (c) Hierarchial clustering analysis distinguishes patient subsets with distinct BCR-phosphosignatures, (d) Ibrutinib trial: Representative change in basal levels of phosphorylation for 3 phosphoproteins on 6 months and 12 months of treatment (normalized to the pretreatment baseline), (e) WM subset analysis based on CD20 expression shows interclonal differential signaling (here LYN activation is shown in a representative WM sample). Figure 1. (a) Basal, aIgM induced and aIgM+H2O2 induced phosphorylation of key nodal signaling proteins in WM (red) and Healthy Donor B-cells (black), (b) Principal component analysis of the generated phosphosignatures distinguishes WM from Healthy Donor B-cells, (c) Hierarchial clustering analysis distinguishes patient subsets with distinct BCR-phosphosignatures, (d) Ibrutinib trial: Representative change in basal levels of phosphorylation for 3 phosphoproteins on 6 months and 12 months of treatment (normalized to the pretreatment baseline), (e) WM subset analysis based on CD20 expression shows interclonal differential signaling (here LYN activation is shown in a representative WM sample). Disclosures No relevant conflicts of interest to declare.

Principal component analysis based on non-parametric maximum entropy

2010 ◽  
Vol 73 (10-12) ◽  
pp. 1840-1852 ◽  
Author(s):  
Ran He ◽  
Baogang Hu ◽  
XiaoTong Yuan ◽  
Wei-Shi Zheng
Keyword(s):  
Principal Component Analysis ◽  
Maximum Entropy ◽  
Principal Component ◽  
Component Analysis ◽  
Non Parametric

scholarly journals ALGORITMA PRINCIPAL COMPONEN ANALYSIS DALAM PEMROSESAN SINYAL ELECTROKARDIOGRAM

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Husaini Husaini ◽  
Huzaeni Huzaeni ◽  
Fahmi Fahmi
Keyword(s):  
Principal Component Analysis ◽  
Parametric Method ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Redundant Data ◽  
The Cost ◽  
Pca Algorithm ◽  
Non Parametric

Abstrak — Principal Component Analysis (PCA) merupakan salah satu teknik yang ada dalam statistic dan merupakan metode non parametric untuk mengekstraksi informasi-informasi yang bersesuaian dari sekumpulan data yang masih diragukan dan memerlukan proses untuk menghilangkan gangguan-gangguan yang ada. Data yang dimaksud salah satunya adalah sinyal ektrokardiogram (EKG). Sinyal EKG merupakan sinyal yang diperoleh dari rekaman aktifitas elektrik dari jantung. Rekaman sinyal EKG tidak saja digunakan untuk tujuan diagnosa, tapi juga disimpan sebagai referensi dalam mengklasifikasi EKG arrhythmia. Untuk mendapatkan hasil yang lebih baik maka data-data sinyal EKG akan direduksi dimensinya dengan tujuan untuk menghilangkab data-data yang tidak sesuai, tidak relevan dan data redundant sehingga dapat menghemat biaya komputasinya dan mencegah data-data yang over-fitting. Tulisan ini memaparkan tentang ide dasar dari PCA dalam mereduksi dimensi data-data dari sinyal  EKG. Hasil yang ditampilkan adalah berupa proses-proses dalam algoritma PCA dan akurasi klasifikasi sinyal  dengan metode KNN dan Naive Bayes.Kata kunci : principal component analysisi (PCA), sinyal EKG, reduksi dimensi Abstract — The Principal Component Analysis (PCA) is one of the existing techniques in statistics and a non parametric method for extracting the information from a collection of data that still in doubt and requires a process to remove any disturbances. The data in question one of them is the signal ektrokardiogram (ECG). ECG signals are signals obtained from recording electrical activity from the heart. ECG signal recording is not only used for diagnostic purposes, but is also stored as a reference in classifying ECG arrhythmias. To get better results then the ECG signal data will be reduced the dimension. The aim to removed data that are not appropriate, irrelevant and redundant data so as to save the cost of computing and prevent data over-fitting. This paper describes the basic idea of PCA in reducing the dimensions of data from ECG signals. The results shown are the processes in PCA algorithm and signal classification accuracy by KNN and Naive Bayes methods.Keywords— Principal Component Analysis, ECG Signal, reduction dimentionality

scholarly journals Measuring local competitiveness: comparing and integrating two methods PCA and AHP

2021 ◽  
Author(s):  
Katarzyna A. Kurek ◽  
Wim Heijman ◽  
Johan van Ophem ◽  
Stanisław Gędek ◽  
Jacek Strojny
Keyword(s):  
Social Sciences ◽  
Principal Component Analysis ◽  
Analytical Hierarchy Process ◽  
Mixed Method ◽  
Principal Component ◽  
Component Analysis ◽  
Mixed Modelling ◽  
Hierarchy Process ◽  
Comparable Model ◽  
Non Parametric

AbstractThis article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination.

Correspondence Analysis In Heavy Mineral Interpretation

1994 ◽  
Author(s):  
J. Tourenq ◽  
V. Rohrlich
Keyword(s):  
Principal Component Analysis ◽  
Correspondence Analysis ◽  
Principal Component ◽  
Sedimentary Basins ◽  
Component Analysis ◽  
Heavy Mineral ◽  
Heavy Minerals ◽  
Data Sets ◽  
Mineral Data ◽  
Non Parametric

Correspondence analysis, a non-parametric principal component analysis, has been used to analyze heavy mineral data so that variations between both samples and minerals can be studied simultaneously. Four data sets were selected to demonstrate the method. The first example, modern sediments from the River Nile, illustrates how correspondence analysis brings out extra details in heavy mineral associations. The other examples come from the Plio-Quaternary "Bourbonnais Formation" of the French Massif Central. The first data set demonstrates how the principal factor plane (with axes 1 and 2) highlights relationships between geographical position and the predominant heavy mineral association (metamorphic minerals and zircon), suggesting the paleogeographic source. In the second set, the factor plane of axes 1 and 3 indicates a subdivision of the metamorphic mineral assemblage, suggesting two sources of metamorphic minerals. Finally, outcrop samples were projected onto the factor plane and reveal ancient drainage systems important for the accumulation of the Bourbonnais sands. Statistical methods used in interpreting heavy minerals in sediments range from simple and classical methods, such as calculation of means and standard deviations, to the calculation of correspondences and variances. Use of multivariate methods is increasingly frequent (Maurer, 1983; Stattegger, 1986; 1987; Delaune et al., 1989; Mezzadri and Saccani, 1989) since the first studies of Imbrie and vanAndel (1964). Ordination techniques such as principal component analysis (Harman, 1961) synthesize large amounts of data and extract the most important relationships. We have chosen a non-parametric form of principal component analysis called correspondence analysis. This technique has been used in sedimentology by Chenet and Teil (1979) to investigate deep-sea samples, by Cojan and Teil (1982) and Mercier et al. (1987) to define paleoenvironments, and by Cojan and Beaudoin (1986) to show paleoecological control of deposition in French sedimentary basins. Correspondence analysis has been used successfully to interpret heavy mineral data (Tourenq et al, 1978a, 1978b; Bolin et al, 1982; Tourenq, 1986, 1989; Faulp et al, 1988; Ambroise et al, 1987). We provide examples of different situations where the method can be applied. We will not present the mathematical and statistical procedures involved in correspondence analysis, but refer readers to Benzécri et al.

Application of Advanced Statistical Methods in the Plant Biotechnology and Phytochemistry

2015 ◽  
Vol 712 ◽  
pp. 101-106
Author(s):  
Ewa Skrzypczak-Pietraszek ◽  
Jacek Pietraszek
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Plant Biotechnology ◽  
Principal Component ◽  
Final Analysis ◽  
Component Analysis ◽  
Measured Data ◽  
Problem Definition ◽  
Wallis Anova ◽  
Non Parametric

The large dimensionality and unknown distributions are often met in a plant biotechnology and phytochemistry investigations. In this paper two methods are presented: principal component analysis allowing to reduce dimensionality and non-parametric Kruskal-Wallis ANOVA allowing to separate factors’ influence even if the distribution is unknown. The paper contains: problem definition, presentation of the measured data and the final analysis. The paper should be potentially useful to other industrial or research approaches.

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