微分极值分析软件包
blacksheep-outliers的Python项目详细描述
害群之马
微分极值分析工具
安装
使用pip
pip install blacksheep-outliers
使用conda
conda install -c bioconda blacksheep-outliers
要求
熊猫
努比
matplotlib
肖伯恩
scipy
scikit学习
使用量
在python中
importdeva# Read in datavalues_file=''#insert values file hereannotations_file=''#insert annotations file herevalues=deva.read_in_values(values_file)annotations=deva.read_in_values(annotations_file)# Binarize annotation columnsannotations=deva.binarize_annotations(annotations)# Run outliers comparative analysisoutliers,qvalues=deva.run_outliers(values,annotations,save_outlier_table=True,save_qvalues=True,save_comparison_summaries=True)# Pull out resultsqvalues_table=qvalues.dfvis_table=outliers.frac_table# Make heatmaps for significant genesforcolinannotations.columns:axs=deva.plot_heatmap(annotations,qvalues_table,col,vis_table,savefig=True)# Normalize valuesphospho=deva.read_in_values('')#Fill in file hereprotein=deva.read_in_values('')#Fill in file here
命令行界面
示例
deva binarize annotations.tsv --output_prefix annotations_test deva outliers values.csv annotations_test.binarized.tsv --output_prefix test\ --write_outlier_table --write_comparison_summaries --write_gene_list \ --make_heatmaps
完整帮助 只需制作离群值表：
usage: deva outliers_table [-h][--output_prefix OUTPUT_PREFIX][--iqrs IQRS][--up_or_down {up,down}][--ind_sep IND_SEP][--do_not_aggregate][--write_frac_table] values Takes a table of values and converts to a table of outlier counts. positional arguments: values File path to input values. Columns must be samples, genes must be sites or genes. Only .tsv and .csv accepted. optional arguments: -h, --help show this help message and exit --output_prefix OUTPUT_PREFIX Output prefix for writing files. Default outliers. --iqrs IQRS Number of interquartile ranges (IQRs) above or below the median to consider a value an outlier. Default is 1.5 IQRs. --up_or_down {up,down} Whether to look for up or down outliers. Choices are up or down. Default up. --ind_sep IND_SEP If site labels have a parent molecule (e.g. a gene name such as ATM) and a site identifier (e.g. S365) this is the delimiter between the two elements. Default is - --do_not_aggregate Use flag if you do not want to sum outliers based on site prefixes. --write_frac_table Use flag if you want to write a table with fraction of values per site, per sample that are outliers. Will not be written by default. Useful for visualization.
对注释表中的列进行二值化。 **警告：不要包含非分类列或不希望二值化的列。你会 最后是一张巨大的没有挥舞的桌子。**
usage: deva binarize [-h][--output_prefix OUTPUT_PREFIX] annotations Takes an annotation table where some columns may have more than 2 possible values (not including empty/null values) and outputs an annotation table with only two values per annotation. Propagates null values. positional arguments: annotations Annotation table with samples as rows and annotation labels as columns. optional arguments: -h, --help show this help message and exit --output_prefix OUTPUT_PREFIX Output prefix for writing files. Default outliers.
使用异常值计数比较注释表列中描述的所有组
usage: deva compare_groups [-h][--output_prefix OUTPUT_PREFIX][--frac_filter FRAC_FILTER][--write_comparison_summaries][--iqrs IQRS][--up_or_down {up,down}][--write_gene_list][--make_heatmaps][--fdr FDR][--red_or_blue {red,blue}][--annotation_colors ANNOTATION_COLORS] outliers_table annotations Takes an annotation table and outlier count table (output of outliers_table) and outputs qvalues from a statistical test that looks for enrichment of outlier values in each group in the annotation table. For each value in each comparison, the qvalue table will have 1 column, if there are any genes in that comparison. positional arguments: outliers_table Table of outlier counts (output of outliers_table). Must be .tsv or .csv file, with outlier and non- outlier counts as columns, and genes/sites as rows. annotations Table of annotations. Must be .csv or .tsv. Samples as rows and comparisons as columns. Comparisons must have only unique values (not including missing values). If there are more options than that, you can use binarize to prepare the table. optional arguments: -h, --help show this help message and exit --output_prefix OUTPUT_PREFIX Output prefix for writing files. Default outliers. --frac_filter FRAC_FILTER The minimum fraction of samples per group that must have an outlier in a gene toconsider that gene in the analysis. This is used to prevent a high number of outlier values in 1 sample from driving a low qvalue. Default 0.3 --write_comparison_summaries Use flag to write a separate file for each column in the annotations table, with outlier counts in each group, p-values and q-values in each group. --iqrs IQRS Number of IQRs used to define outliers in the input count table. Optional. --up_or_down {up,down} Whether input outlier table represents up or down outliers. Needed for output file labels. Default up --write_gene_list Use flag to write a list of significantly enriched genes for each value in each comparison. If used, need an fdr threshold as well. --make_heatmaps Use flag to draw a heatmap of signficantly enriched genes for each value in each comparison. If used, need an fdr threshold as well. --fdr FDR FDR cut off to use for signficantly enriched gene lists and heatmaps. Default 0.05 --red_or_blue {red,blue} If --make_heatmaps is called, color of values to draw on heatmap. Default red. --annotation_colors ANNOTATION_COLORS File with color map to use for annotation header if --make_heatmaps is used. Must have a 'value color' format for each value in annotations. Any value not represented will be assigned a new color.
使热图可视化注释表中每组的丰富基因
usage: deva visualize [-h][--output_prefix OUTPUT_PREFIX][--annotations_to_show ANNOTATIONS_TO_SHOW [ANNOTATIONS_TO_SHOW ...]][--fdr FDR][--red_or_blue {red,blue}][--annotation_colors ANNOTATION_COLORS][--write_gene_list] comparison_qvalues annotations visualization_table comparison_of_interest Used to make custom heatmaps from significant genes. positional arguments: comparison_qvalues Table of qvalues, output from compare_groups. Must be .csv or .tsv. Has genes/sites as rows and comparison values as columns. annotations Table of annotations used to generate qvalues. visualization_table Values to visualize in heatmap. Samples as columns and genes/sites as rows. Using outlier fraction table is recommended, but original values can also be used if no aggregation was used. comparison_of_interest Name of column in qvalues table from which to visualize significant genes. optional arguments: -h, --help show this help message and exit --output_prefix OUTPUT_PREFIX Output prefix for writing files. Default outliers. --annotations_to_show ANNOTATIONS_TO_SHOW [ANNOTATIONS_TO_SHOW ...] Names of columns from the annotation table to show in the header of the heatmap. Default is all columns. --fdr FDR FDR threshold to use to select genes to visualize. Default 0.05 --red_or_blue {red,blue} Color of values to draw on heatmap. Default red. --annotation_colors ANNOTATION_COLORS File with color map to use for annotation header. Must have a line with 'value color' format for each value in annotations. Any value not represented will be assigned a new color. --write_gene_list Use flag to write a list of significantly enriched genes for each value in each comparison.
运行整个管道：调用异常值，对注释表中的所有组执行比较 ，可以为每个组制作热图。
usage: deva outliers [-h][--output_prefix OUTPUT_PREFIX][--iqrs IQRS][--up_or_down {up,down}][--do_not_aggregate][--write_outlier_table][--write_frac_table][--ind_sep IND_SEP][--frac_filter FRAC_FILTER][--write_comparison_summaries][--fdr FDR][--write_gene_list][--make_heatmaps][--red_or_blue {red,blue}][--annotation_colors ANNOTATION_COLORS] values annotations Runs whole outliers pipeline. Has options to output every possible output. positional arguments: values File path to input values. Samples are columns and genes/sites are rows. Only .tsv and .csv accepted. annotations File path to annotation values. Rows are sample names, header is different annotations. e.g. mutation status. optional arguments: -h, --help show this help message and exit --output_prefix OUTPUT_PREFIX Output prefix for writing files. Default outliers. --iqrs IQRS Number of inter-quartile ranges (IQRs) above or below the median to consider a value an outlier. Default is 1.5. --up_or_down {up,down} Whether to look for up or down outliers. Choices are up or down. Default up. --do_not_aggregate Use flag if you do not want to sum outliers based on site prefixes. --write_outlier_table Use flag to write a table of outlier counts. --write_frac_table Use flag if you want to write a table with fraction of values per site per sample that are outliers. Useful for custom visualization. --ind_sep IND_SEP If site labels have a parent molecule (e.g. a gene name such as ATM) and a site identifier (e.g. S365) this is the delimiter between the two elements. Default is - --frac_filter FRAC_FILTER The minimum fraction of samples per group that must have an outlier in a gene toconsider that gene in the analysis. This is used to prevent a high number of outlier values in 1 sample from driving a low qvalue. Default 0.3 --write_comparison_summaries Use flag to write a separate file for each column in the annotations table, with outlier counts in each group, p-values and q-values in each group. --fdr FDR FDR threshold to use to select genes to visualize. Default 0.05 --write_gene_list Use flag to write a list of significantly enriched genes for each value in each comparison. --make_heatmaps Use flag to draw a heatmap of significantly enriched genes for each value in each comparison. If used, need an fdr threshold as well. --red_or_blue {red,blue} Color of values to draw on heatmap. Default red. --annotation_colors ANNOTATION_COLORS File with color map to use for annotation header. Must have a line with 'value color' format for each value in annotations. Any value not represented will be assigned a new color.
用于查找不同数据级别无法解释的值差异。例如
，这可用于找出差异磷酸化（磷酸化）引起的变化
目标值）不是由于蛋白质丰度的变化（蛋白质作为标准值）。 有关更详细的小插曲，请参阅我们的[补充笔记本]（https://github.com
/橄榄球板/黑羊俱乐部供应） 欢迎加入QQ群-->： 979659372
警告：两个表之间的行ID必须匹配
usage: deva normalize [-h][--ind_sep IND_SEP][--output_prefix OUTPUT_PREFIX]
target_values normalizer_values
Takes a target table and a normalizer table, and returns a normalized target
table. Builds a regularized linear model for each line in the target table
using the matching row ID in the normalizer table, and finds the residuals of
that model for each value. for example, this could be used to normalize
phospho-peptide data by protein abundance data; resulting values will reflect
only abundance differences due to phosphorylation changes, not peptide
abundances. Another use could be normalizing RNA by CNA.
positional arguments:
target_values Table of values to be normalized. Sites/genes as rows,
samples as columns. Row identifiers must be unique.
normalizer_values Table of values to use for normalization. Sites/genes
as rows, samples as columns. Row identifiers must be
unique, and must match the pre-ind_sep part of the
target values identifiers.
optional arguments:
-h, --help show this help message and exit
--ind_sep IND_SEP Separator used in index if target is site specific.
Row IDs before ind_sep in the target must match the
row IDs in normalizer_values. If row IDs already
match, leave blank.
--output_prefix OUTPUT_PREFIX
Prefix for output file. Suffix will be
'.normalized.tsv'
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