A comparison of three procedures for robust PCA of experimental results of the homogeneity test of a new sodium diclofenac candidate certified reference material

The use of ANOVA-PCA and DD-SIMCA in the development of corn flour laboratory reference materials

The development of a collaborative study as a requirement for the preparation of a laboratory reference material candidate is reported in this paper. The evaluation was performed by 13 laboratories invited to quantify the calcium, potassium, magnesium, phosphorus, copper, iron, manganese and zinc; 8 of them presented results for all the analytes under investigation. The data were statistically analyzed by applying the z-score robust technique as recommended by ISO Guide 35. For the potassium element, laboratories 4 and 13 presented questionable results. Laboratory 5 proved to be unsatisfactory for calcium and zinc. ANOVA-PCA and DD-SIMCA were also applied to evaluate stability and interlaboratory studies results, respectively. It has been demonstrated that multivariate data analysis can be successfully applied as an alternative method to the recommendations made by ISO 13528 and ISO Guide 35 with defined confidence intervals.

Analysis of silver coins from colonial Brazil by hand held XRF and micro-XRF

In this work, 960 réis coins from the period when Brazil was a colony of Portugal were analyzed using the x-ray fluorescence (XRF) spectrometry. The history of these coins, dated between the end of the 17th century and the beginning of the 19th century, had a great influence on the immigration of the Portuguese Prince Regent D. João to Brazil, who arrived in 1808. Bearing in mind the need to expand the timid Brazilian monetary system, the Portuguese crown decided to collect Spanish silver pesos of 8 reales, recoined with a value of 960 réis. The recoinage procedure was carried out using a stamp; therefore, in many cases, it is possible to check the base currency. In this work, were investigated 17 samples of 960 réis coins by XRF, in which the base coin was 8 reales manufactured with raw materials from Mexican mines. In addition to characterizing the elemental composition of the coins, the XRF data were evaluated using multivariate statistical method of Robust Principal Component Analysis (ROBPCA), which was used to classify the coins based on their elemental composition. However, with XRF, elementary information is obtained for a depth of only a few micrometers. One of the essential issues in Ag-Cu metal alloys is the Ag enrichment, which can cause changes to the elemental composition of the surface. Therefore, initially, a study was carried out to verify whether the surface compositions of the coins were altered by the Ag enrichment.

Robust principal component analysis for accurate outlier sample detection in RNA-Seq data

BACKGROUND

High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.

RESULTS

We report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.

CONCLUSIONS

rPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.