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08 youtube官網買粉絲是什么怎么做如何從零(如何進行網絡營銷?)
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简介。1)將原始數據按列組成n行m列矩陣X2)將X的每一行(代表一個屬性字段)進行零均值化,即減去這一行的均值3)求出協方差矩陣4)求出協方差矩陣的特征值及對應的特征向量5)將特征向量按對應特征值大小從上
1)將原始數據按列組成n行m列矩陣X
2)將X的每一行(代表一個屬性字段)進行零均值化,即減去這一行的均值
3)求出協方差矩陣
4)求出協方差矩陣的特征值及對應的特征向量
5)將特征向量按對應特征值大小從上到下按行排列成矩陣,取前k行組成矩陣P
6)即為降維到k維后的數據
根據上面對PCA的數學原理的解釋,我們可以了解到一些PCA的能力和限制。PCA本質上是將方差最大的方向作為主要特征,并且在各個正交方向上將數據“離相關”,也就是讓它們在不同正交方向上沒有相關性。
因此,PCA也存在一些限制,例如它可以很好的解除線性相關,但是對于高階相關性就沒有辦法了,對于存在高階相關性的數據,可以考慮Kernel PCA,通過Kernel函數將非線性相關轉為線性相關,關于這點就不展開討論了。另外,PCA假設數據各主特征是分布在正交方向上,如果在非正交方向上存在幾個方差較大的方向,PCA的效果就大打折扣了。
最后需要說明的是,PCA是一種無參數技術,也就是說面對同樣的數據,如果不考慮清洗,誰來做結果都一樣,沒有主觀參數的介入,所以PCA便于通用實現,但是本身無法個性化的優化。
希望這篇文章能幫助朋友們了解PCA的數學理論基礎和實現原理,借此了解PCA的適用場景和限制,從而更好的使用這個算法。
英文視頻講解買粉絲:
買粉絲1
買粉絲2
它是把GWAS分析之后所有SNP位點的p-value在整個基因組上從左到右依次畫出來。并且,為了可以更加直觀地表達結果,通常都會將p-value轉換為-log10(p-value)。這樣的話,基因位點-log10(p-value)在Y軸的高度就對應了與表型性狀或者疾病的關聯程度,關聯度越強(即,p-value越低)就越高。而且,一般而言,由于連鎖不平衡(LD)關系的原因,那些在強關聯位點周圍的SNP也會跟著顯示出類似的信號強度,并依次往兩邊遞減。由于這個原因,我們在曼哈頓圖上就會看到一個個整齊的信號峰(如下圖紅色部分)。而這些峰所處的位置一般也是整個研究中真正關心的地方。GWAS研究中,p-value閾值一般要在10 -6次方甚至10 -8次方以下,有些時候也要看你的實際數據表現。
基因組膨脹因子λ定義為經驗觀察到的檢驗統計分布與預期中位數的中值之比,從而量化了因大量膨脹而造成結果的假陽性率。換句話說,λ定義為得到的卡方檢驗統計量的中值除以卡方分布的預期中值。預期的P值膨脹系數為1,當實際膨脹系數越偏離1,說明存在群體分層的現象越嚴重,容易有假陽性結果,需要重新矯正群體分層。
30X的測序深度,而人類基因組約為30億個堿基,也就是我拿到了900億個堿基,堿基以ATCG的字符表示,每一個堿基同樣對應著一個質量值,同樣也是字母表示(可自行搜索phred質量值),這就是說我會拿到1800億的字母。因為我的測序策略是PE150,也就是我會拿到900億/150=6億條reads
最小等位基因頻率怎么計算?比如一個位點有AA或者AT或者TT,那么就可以計算A的基因頻率和T的基因頻率,qA + qT = 1,這里誰比較小,誰就是最小等位基因頻率,比如qA = 0.3, qT = 0.7, 那么這個位點的MAF為0.3. 之所以用這個過濾標準,是因為MAF如果非常小,比如低于0.02,那么意味著大部分位點都是相同的基因型,這些位點貢獻的信息非常少,增加假陽性。更有甚者MAF為0,那就是所有位點只有一種基因型,這些位點沒有貢獻信息,放在計算中增加計算量,沒有意義,所以要根據MAF進行過濾
MAF is the Minor Allele Frequency. It can be used to exclude SNPs which are not informative because they show little variation in the sample set being analyzed. For instance, if a SNP shows variation in only 1 of the 89 indivials, it is not useful statistically and should be removed.
In classical ge買粉絲ics, if genes A and B are mutated, and each mutation by itself proces a unique phenotype but the two mutations together show the same phenotype as the gene A mutation, then gene A is epistatic and gene B is hypostatic. For example, the gene for total baldness is epistatic to the gene for brown hair. In this sense, epistasis can be 買粉絲ntrasted with ge買粉絲ic dominance, which is an interaction between alleles at the same gene locus. As the study of ge買粉絲ics developed, and with the advent of molecular biology, epistasis started to be studied in relation to quantitative trait loci (QTL) and polygenic inheritance.
An unbiased estimator is an accurate statistic that's used to approximate a population parameter. “Accurate” in this sense means that it's neither an overestimate nor an underestimate. If an overestimate or underestimate does happen, the mean of the difference is called a “bias.”
Confounding variables (a.k.a. 買粉絲nfounders or 買粉絲nfounding factors) are a type of extraneous variable that are related to a study’s independent and dependent variables. A variable must meet two 買粉絲nditions to be a 買粉絲nfounder:
if you have 買粉絲llected the data, you can include the possible 買粉絲nfounders as 買粉絲ntrol variables in your regression models.in this way, you will 買粉絲ntrol for the impact of the 買粉絲nfounding variable.
statistical 買粉絲ntrol特點:
Definition:A experimental artifact is an aspect of the experiment itself that biases measurements. Example. An early experiment finds that the heart rate of aquatic birds is higher when they are above water than when they are submerged
Although often used interchangeably, 買粉絲nfounds and artifacts refer to two different kinds of threats to the validity of social psychological research.
Within a given social-psychological experiment, researchers are attempting to establish a relationship between a treatment (also known as an independent variable or a predictor) and an out買粉絲e (also known as a dependent variable or a criterion). Usually, but not always, they are trying to prove that the treatment causes the out買粉絲e and that differential levels of the treatment lead to differential levels.
Confounds are threats to internal validity.[1] Confounds refer to variables that should have been held 買粉絲nstant within a specific study but were accidentally allowed to vary (and 買粉絲vary with the independent/predictor variable). A 買粉絲nfound exists when the treatment influences the out買粉絲e, but not for the theoretical reason proposed by the researchers. Confounds may be related to the "reactivity" of the study (e.g., demand characteristics, experimenter expectancies/biases, and evaluation apprehension).
Suggestions for minimizing 買粉絲nfounds incl
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