def load(): DeconRNASeq.load_r() rpy2.robjects.r("data(rat_liver_brain)") x = rpy2.robjects.r('all.datasets') df = rpy2.robjects.pandas2ri.ri2py(x) df.index = rpy2.robjects.r('rownames')(x) sample_info = { 'd15282bm': 'liver', 'd15283bm': 'liver', 'd15284bm': 'liver', 'd15285bm': 'mix', 'd15286bm': 'mix', 'd15287bm': 'mix', 'd15288bm': 'mix', 'd15289bm': 'mix', 'd15290bm': 'mix', 'd15291bm': 'mix', 'd15292bm': 'mix', 'd15293bm': 'mix', 'd15294bm': 'brain', 'd15295bm': 'brain', 'd15296bm': 'brain', } return sample_info, df
def select_genes(df_tpm, reference_information): DeconRNASeq.load_r() rpy2.robjects.r("data(liver_kidney)") x = rpy2.robjects.r('signatures') xdf = rpy2.robjects.pandas2ri.ri2py(x) xdf.index = rpy2.robjects.r("rownames")(x) xdf = xdf[xdf.kidney > xdf.liver] return xdf # gg.convert_dataframe_to_r(x, True)
def load(): DeconRNASeq.load_r() rpy2.robjects.r("data(liver_kidney)") x = rpy2.robjects.r('datasets') df = rpy2.robjects.pandas2ri.ri2py(x) df.index = rpy2.robjects.r('rownames')(x) sample_info = {} for x in df.columns: sample_info[x] = np.nan return sample_info, df
def Percentage_DeconRNASeq(): DeconRNASeq.load_r() def calc(df_tpm, genes_for_estimation): if isinstance(genes_for_estimation, list): signature_df = df_tpm.ix[genes_for_estimation, [ 'reference_sample', 'reference_contamination']] else: signature_df = genes_for_estimation if not isinstance(signature_df, rpy2.robjects.vectors.DataFrame): signature_df = gg.convert_dataframe_to_r(signature_df, True) if signature_df is None: print genes_for_estimation print type(genes_for_estimation) raise ValueError("Here it is") mixture_df = df_tpm[['observed']] # for some arcane reason it won't work if you run it with one sample # only... mixture_df.insert(1, 'duplicate', mixture_df['observed']) mixture_df = gg.convert_dataframe_to_r(mixture_df, True) result = rpy2.robjects.r('DeconRNASeq')( mixture_df, signature_df, ) calc_percentage = result[0][1] # the contamination percentage calc_percentage_std = np.nan percentage, corrected = correct_with_percentage( df_tpm, 'observed', ['reference_sample'], ['reference_contamination'], calc_percentage) return { 'percentage': calc_percentage, 'percentage_std': calc_percentage_std, 'corrected': corrected, } return 'DeconRNAseq', calc
def select_genes(df_tpm, reference_information): DeconRNASeq.load_r() rpy2.robjects.r("data(rat_liver_brain)") x = rpy2.robjects.r('array.signatures') return list(rpy2.robjects.r('rownames')(x))
def select_genes(df_tpm, reference_information): DeconRNASeq.load_r() rpy2.robjects.r("data(liver_kidney)") x = rpy2.robjects.r('signatures') return x