Extraction of Differentially Expressed Genes Under Starvation

Download Data¶

In [ ]:

import requests
from tqdm import tnrange, tqdm_notebook
def download_file(doi,ext):
    url = 'https://api.datacite.org/dois/'+doi+'/media'
    r = requests.get(url).json()
    netcdf_url = r['data'][0]['attributes']['url']
    r = requests.get(netcdf_url,stream=True)
    #Set file name
    fname = doi.split('/')[-1]+ext
    #Download file with progress bar
    if r.status_code == 403:
        print("File Unavailable")
    if 'content-length' not in r.headers:
        print("Did not get file")
    else:
        with open(fname, 'wb') as f:
            total_length = int(r.headers.get('content-length'))
            pbar = tnrange(int(total_length/1024), unit="B")
            for chunk in r.iter_content(chunk_size=1024):
                if chunk:
                    pbar.update()
                    f.write(chunk)
        return fname

In [ ]:

#Starvation h5ad data, all nonzero genes included, filtered for 'real cells' from de-multiplexing
download_file('10.22002/D1.1797','.gz')

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`

Out[ ]:

'D1.1797.gz'

In [ ]:

#CellRanger Starvation h5ad data
download_file('10.22002/D1.1798','.gz')

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`

Out[ ]:

'D1.1798.gz'

In [ ]:

#Kallisto bus clustered starvation data, h5ad
download_file('10.22002/D1.1796','.gz')

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`

Out[ ]:

'D1.1796.gz'

In [ ]:

#Saved DeSeq2 Results for Fed/Starved (Differentially expressed under starvation --> perturbed genes)
download_file('10.22002/D1.1810','.gz')

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`

Out[ ]:

'D1.1810.gz'

In [ ]:

#Human ortholog annotations
download_file('10.22002/D1.1819','.gz')

#Panther annotations
download_file('10.22002/D1.1820','.gz')

#GO Terms
download_file('10.22002/D1.1822','.gz')

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`

Out[ ]:

'D1.1822.gz'

In [ ]:

!gunzip *.gz

In [ ]:

!pip install --quiet anndata
!pip install --quiet scanpy==1.6.0
!pip3 install --quiet leidenalg
!pip install --quiet louvain

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     |████████████████████████████████| 71kB 9.0MB/s 
     |████████████████████████████████| 51kB 7.2MB/s 
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     |████████████████████████████████| 2.2MB 5.4MB/s 

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!pip3 install --quiet rpy2

Import Packages¶

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import pandas as pd
import anndata
import scanpy as sc
import numpy as np
import scipy.sparse

import warnings
warnings.filterwarnings('ignore')

from sklearn.neighbors import (KNeighborsClassifier,NeighborhoodComponentsAnalysis)
from sklearn.pipeline import Pipeline
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA

from sklearn.preprocessing import scale

import random

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
%matplotlib inline
sc.set_figure_params(dpi=125)

import seaborn as sns
sns.set(style="whitegrid")
%load_ext rpy2.ipython

In [ ]:

# See version of all installed packages, last done 11/27/2020
# !pip list -v > pkg_vers_20201127.txt

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#Read in annotations
from io import StringIO

hg_ortho_df = pd.read_csv(StringIO(''.join(l.replace('|', '\t') for l in open('D1.1819'))),
            sep="\t",header=None,skiprows=[0,1,2,3])

hg_ortho_df[['XLOC','TCONS']] = hg_ortho_df[13].str.split(expand=True) 
hg_ortho_df[['Gene','gi']] = hg_ortho_df[3].str.split(expand=True) 
hg_ortho_df['Description']= hg_ortho_df[11]


panther_df = pd.read_csv('D1.1820',
            sep="\t",header=None) #skiprows=[0,1,2,3]



goTerm_df = pd.read_csv('D1.1822',
            sep=" ",header=None) #skiprows=[0,1,2,3]

Run DeSeq2 Analysis for Starvation Data¶

In [ ]:

#Remove clusters with < 10 cells per condition

#Read in previously saved data
bus_fs_clus = anndata.read("D1.1796")
print(bus_fs_clus )

bus_fs_raw = anndata.read("D1.1797")

bus_fs_raw = bus_fs_raw[bus_fs_clus.obs_names,]
#bus_fs_raw.obs['orgID'] = bus_fs_clus.obs['orgID']
bus_fs_raw.obs['fed'] = bus_fs_clus.obs['fed']
bus_fs_raw.obs['cellRanger_louvain'] = bus_fs_clus.obs['cellRanger_louvain']
bus_fs_raw


#clusSize

Trying to set attribute `.obs` of view, copying.

AnnData object with n_obs × n_vars = 13673 × 8696
    obs: 'batch', 'n_counts', 'n_countslog', 'louvain', 'leiden', 'orgID', 'fed', 'starved', 'fed_neighbor_score', 'cellRanger_louvain', 'annos', 'new_cellRanger_louvain', 'annosSub'
    var: 'n_counts', 'mean', 'std'
    uns: 'annosSub_colors', 'annos_colors', 'cellRanger_louvain_colors', 'cellRanger_louvain_sizes', "dendrogram_['new_cellRanger_louvain']", 'dendrogram_new_cellRanger_louvain', 'fed_colors', 'fed_neighbor_score_colors', 'leiden', 'leiden_colors', 'louvain', 'louvain_colors', 'neighbors', 'new_cellRanger_louvain_colors', 'orgID_colors', 'paga', 'pca', 'rank_genes_groups', 'umap'
    obsm: 'X_nca', 'X_pca', 'X_tsne', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

Out[ ]:

AnnData object with n_obs × n_vars = 13673 × 46716
    obs: 'batch', 'fed', 'cellRanger_louvain'

In [ ]:

#Determine which clusters large enough to do DE with
def clusToKeep(bus_fs_clus):
  keep = []
  clusSize = {}
  for i in np.unique(bus_fs_clus.obs['cellRanger_louvain']):
      cells = bus_fs_clus[bus_fs_clus.obs['cellRanger_louvain'].isin([i])]
      fed_cells = len(cells[cells.obs['fed']=='True'].obs_names)
      starv_cells = len(cells[cells.obs['fed']=='False'].obs_names)
      min_cells = np.min([fed_cells,starv_cells])
      if min_cells > 10:
        keep += [i]
        #clusSize[i] = min_cells
  return keep

In [ ]:

#Subsample from full dataset, across each cluster
def getSampled_Cluster(bus_fs_clus,bus_fs_raw,keep):

  subSample = 100
  cellNames = np.array(bus_fs_clus.obs_names)
  fed = np.array(list(bus_fs_clus.obs['fed'] == 'True'))
  starv = np.array(list(bus_fs_clus.obs['fed'] == 'False'))
  #di = np.array(list(jelly4Raw.obs['condition'] == 'DI'))

  allCells = []
  for i in keep:
      #subSample =  clusSize[i] # REMOVE IF NOT LOOKING AT SIMILARLY SIZED CLUSTERS
      
      cells = np.array(list(bus_fs_clus.obs['cellRanger_louvain'].isin([i])))
      fed_cells = list(np.where(fed & cells)[0])
      starv_cells = list(np.where(starv & cells)[0])
      
      #Take all cells if < subSample
      if len(fed_cells) >= subSample:
          fed_choice = random.sample(fed_cells,subSample)
      else:
          fed_choice = fed_cells
          
      if len(starv_cells) >= subSample:
          starv_choice = random.sample(starv_cells,subSample)
      else:
          starv_choice = starv_cells
          
          
      pos = list(fed_choice)+list(starv_choice)
      #print(len(pos))
      
      allCells += list(cellNames[pos])

      
  sub_raw = bus_fs_raw[allCells,:]
  return sub_raw

In [ ]:

#For full dataset don't filter by highly variable
keep = clusToKeep(bus_fs_clus)
sub_raw = getSampled_Cluster(bus_fs_clus,bus_fs_raw,keep)
print(sub_raw)

sub_raw_copy = sub_raw.copy()
sc.pp.filter_cells(sub_raw, min_counts=0)
sc.pp.filter_genes(sub_raw, min_counts=0)
sc.pp.normalize_per_cell(sub_raw_copy, counts_per_cell_after=1e4)
sub_raw_copy.raw = sc.pp.log1p(sub_raw_copy, copy=True)

#sc.pp.highly_variable_genes(sub_raw_copy,n_top_genes=5000) #This is just a small example, for full data used all nonzero genes
#sub_raw = sub_raw[:,sub_raw_copy.var['highly_variable']]

Trying to set attribute `.obs` of view, copying.

View of AnnData object with n_obs × n_vars = 6026 × 46716
    obs: 'batch', 'fed', 'cellRanger_louvain'

In [ ]:

#Instantiate dataframe with gene names
def makeDF_forR(sub_raw):
  fullDF = pd.DataFrame(scipy.sparse.csr_matrix.toarray(sub_raw.X).T, index = sub_raw.var_names.tolist(), columns= sub_raw.obs_names.tolist())
  conds = sub_raw.obs['fed'].tolist()
  #ids = sub_jelly4Raw.obs['orgID'].tolist()
  clus = sub_raw.obs['cellRanger_louvain'].tolist()

  reps = np.repeat(0,len(sub_raw.obs_names))

  length = len(sub_raw[sub_raw.obs['fed'] == 'True'].obs_names)
  reps[sub_raw.obs['fed'] == 'True'] = range(1,length+1)

  length = len(sub_raw[sub_raw.obs['fed'] == 'False'].obs_names)
  reps[sub_raw.obs['fed'] == 'False'] = range(1,length+1)


  sampleDF = pd.DataFrame({'cell_ID': fullDF.columns}) \
          .assign(condition = conds) \
          .assign(replicate = reps) \
          .assign(cluster = clus) 
  sampleDF.index = sampleDF.cell_ID
  sampleDF.head()

  fullDF.to_csv('fullDF.csv')
  sampleDF.to_csv('sampleDF.csv')

In [ ]:

makeDF_forR(sub_raw)

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%%R 
fullDF <- read.csv(file = 'fullDF.csv')
sampleDF <- read.csv(file = 'sampleDF.csv')
head(sampleDF)

             cell_ID          cell_ID.1 condition replicate cluster
1 AGCAGCCTCTGGTATG-1 AGCAGCCTCTGGTATG-1      True         1       0
2 CGTGAGCGTATATCCG-2 CGTGAGCGTATATCCG-2      True         2       0
3 CATGGCGTCAGTTGAC-1 CATGGCGTCAGTTGAC-1      True         3       0
4 CCCAATCGTTGTTTGG-1 CCCAATCGTTGTTTGG-1      True         4       0
5 ACGAGCCCACATCTTT-2 ACGAGCCCACATCTTT-2      True         5       0
6 GTCACAAGTCTAGGTT-2 GTCACAAGTCTAGGTT-2      True         6       0

In [ ]:

%%R
rownames(sampleDF) <- sampleDF$cell_ID 
#Replace '.' in cell barcodes with '-'
rownames(fullDF) <- fullDF$X
colnames(fullDF) <- gsub("\\.", "-", colnames(fullDF))
fullDF <- subset(fullDF, select = -c(X) )
head(fullDF)

sampleDF <- subset(sampleDF, select = -c(cell_ID.1) )
# head(sampleDF)
sampleDF$condition <- factor(sampleDF$condition, labels = c("starved", "fed"))

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%%R
#Set up R environment
install.packages("BiocManager")
BiocManager::install(version = "3.10")

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%%R 
#install.packages("DESeq2",repos = "http://cran.us.r-project.org")
BiocManager::install("DESeq2")

R[write to console]: Bioconductor version 3.12 (BiocManager 1.30.10), R 4.0.3 (2020-10-10)

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In [ ]:

#Make output directory
!mkdir kallistoDEAnalysis_Starv

In [ ]:

%%R
clusters <- unique(sampleDF$cluster)
clusters

 [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[26] 25 26 27 28 29 30 31 32 33 34 35

In [ ]:

%%R 
#Run DeSeq2 for each of the cell types (between control and starved cells)
install.packages("DESeq2",repos = "http://cran.us.r-project.org")
library("DESeq2")

clusters <- unique(sampleDF$cluster)
Genes <- c()
Cluster <- c()
Condition <- c() 
padj <- c()
log2FC <- c()

for (i in clusters){
 
        indices = which(sampleDF$cluster == i)
        subset = fullDF[,indices]
        subset_meta = subset(sampleDF,cluster == i)


        dds <- DESeqDataSetFromMatrix(countData = subset, colData = subset_meta, design= ~replicate + condition)

        #Set control condition
        dds$condition <- relevel(dds$condition, ref = 'fed')
        dds <- DESeq(dds,test="LRT", reduced=~replicate, sfType="poscounts", useT=TRUE, minmu=1e-6, 
                     minReplicatesForReplace=Inf,betaPrior = FALSE)#parallel = TRUE

        #Starv v Fed results
        res <- results(dds,alpha=0.05,name="condition_starved_vs_fed")
        resLFC <- res 

        resLFC <- na.omit(resLFC)
        resOrdered <- resLFC[resLFC$padj < .05,]
        #Keep log2 fold changes < -1 or > 1
        resOrdered <- resOrdered[abs(resOrdered$log2FoldChange) > 1,] 
        outcomes <- resOrdered[order(resOrdered$padj),]

        Genes <- c(Genes,row.names(outcomes))
        Cluster <- c(Cluster,rep(i,length(row.names(outcomes))))
        Condition <- c(Condition,rep('Starved',length(row.names(outcomes)))) 
        padj <- c(padj,outcomes$padj)
        log2FC <- c(log2FC,outcomes$log2FoldChange)
         
    
}

deGenesDF <- data.frame(matrix(ncol = 6, nrow = length(Genes)))
names(deGenesDF) <- c("Genes", "Cluster", "Condition","padj","padjClus","log2FC")

deGenesDF$Genes <- Genes
deGenesDF$Cluster <- Cluster
deGenesDF$Condition <- Condition
deGenesDF$padj <- padj
deGenesDF$padjClus <- padj*length(unique(Cluster))
deGenesDF$log2FC <- log2FC

write.csv(deGenesDF,'./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv')

head(deGenesDF)

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R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
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R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
  to improve GLM convergence.

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]: final dispersion estimates

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R[write to console]: converting counts to integer mode

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  specifying a model with increasing fold change for higher values.
  did you mean for this to be a factor? if so, first convert
  this variable to a factor using the factor() function

R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
  to improve GLM convergence.

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
  specifying a model with increasing fold change for higher values.
  did you mean for this to be a factor? if so, first convert
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
  to improve GLM convergence.

R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
  specifying a model with increasing fold change for higher values.
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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R[write to console]: estimating size factors

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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

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  specifying a model with increasing fold change for higher values.
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

R[write to console]: estimating dispersions

R[write to console]: gene-wise dispersion estimates

R[write to console]: mean-dispersion relationship

R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
   function: y = a/x + b, and a local regression fit was automatically substituted.
   specify fitType='local' or 'mean' to avoid this message next time.

R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
  specifying a model with increasing fold change for higher values.
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  this variable to a factor using the factor() function

R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

R[write to console]: converting counts to integer mode

R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
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  specifying a model with increasing fold change for higher values.
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  this variable to a factor using the factor() function

R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
  to improve GLM convergence.

R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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R[write to console]: final dispersion estimates

R[write to console]: fitting model and testing

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R[write to console]:   the design formula contains one or more numeric variables with integer values,
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R[write to console]:   the design formula contains one or more numeric variables that have mean or
  standard deviation larger than 5 (an arbitrary threshold to trigger this message).
  it is generally a good idea to center and scale numeric variables in the design
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R[write to console]: estimating size factors

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R[write to console]: gene-wise dispersion estimates

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        Genes Cluster Condition         padj     padjClus    log2FC
1 XLOC_030861       0   Starved 9.125564e-14 3.102692e-12 -1.641450
2 XLOC_010635       0   Starved 5.121409e-13 1.741279e-11 -1.382850
3 XLOC_040775       0   Starved 1.881249e-11 6.396248e-10  1.093848
4 XLOC_012879       0   Starved 9.571692e-11 3.254375e-09 -1.921527
5 XLOC_028699       0   Starved 1.657337e-10 5.634945e-09 -1.099936
6 XLOC_011294       0   Starved 1.278944e-09 4.348410e-08 -1.137523

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%%R
install.packages("UpSetR",repos = "http://cran.us.r-project.org")

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In [ ]:

%%R -w 20 -h 20 --units in -r 500
#Cite http://people.seas.harvard.edu/~alex/papers/2014_infovis_upset.pdf
library("UpSetR")
deGenesDF <- read.csv(file = './kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv') #./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv

#Bonferronni correction across clusters
deGenesDF_toPlot = subset(deGenesDF,padjClus < .05)

# Create empty list to store vectors
vecsToPlot <- list()

clusters = unique(deGenesDF_toPlot$Cluster)
for (i in 1:length(clusters)){
    subset = subset(deGenesDF_toPlot,Cluster == clusters[i])
    vecsToPlot[[i]] <- unique(subset$Genes)
}

names(vecsToPlot) <- clusters

upset(fromList(vecsToPlot), sets = as.character(clusters),nintersects= NA,order.by = "freq",
      mainbar.y.label='',
      sets.x.label = '',
      text.scale = c(1.5, 2, 1.5, 2, 1.3, 1.3),
      show.numbers = "no",
      point.size = 2.8,
      mb.ratio= c(0.5, 0.5),
      queries = list(list(query = intersects, params = list("9"), color = "firebrick",active = T),
                     list(query = intersects, params = list("28"), color = "firebrick",active = T),
                     list(query = intersects, params = list("21"), color = "firebrick",active = T),
                     list(query = intersects, params = list("17"), color = "firebrick",active = T),
                     list(query = intersects, params = list("6"), color = "firebrick",active = T),
                     list(query = intersects, params = list("31"), color = "firebrick",active = T),
                     list(query = intersects, params = list("24"), color = "firebrick",active = T),
                     list(query = intersects, params = list("8"), color = "firebrick",active = T),
                     list(query = intersects, params = list("15"), color = "firebrick",active = T),
                     list(query = intersects, params = list("10"), color = "firebrick",active = T),
                     list(query = intersects, params = list("23"), color = "firebrick",active = T),
                     list(query = intersects, params = list("26"), color = "firebrick",active = T),
                     list(query = intersects, params = list("11"), color = "firebrick",active = T),
                     list(query = intersects, params = list("33"), color = "firebrick",active = T),
                     list(query = intersects, params = list("12"), color = "firebrick",active = T),
                     list(query = intersects, params = list("19"), color = "firebrick",active = T),
                     list(query = intersects, params = list("29"), color = "firebrick",active = T),
                     list(query = intersects, params = list("14"), color = "firebrick",active = T),
                     list(query = intersects, params = list("30"), color = "firebrick",active = T),
                     list(query = intersects, params = list("18"), color = "firebrick",active = T),
                     list(query = intersects, params = list("3"), color = "firebrick",active = T),
                     list(query = intersects, params = list("22"), color = "firebrick",active = T),
                     list(query = intersects, params = list("27"), color = "firebrick",active = T),
                     list(query = intersects, params = list("0"), color = "firebrick",active = T),
                     list(query = intersects, params = list("34"), color = "firebrick",active = T),
                     list(query = intersects, params = list("32"), color = "firebrick",active = T),
                     list(query = intersects, params = list("16"), color = "firebrick",active = T),
                     list(query = intersects, params = list("35"), color = "firebrick",active = T)))

In [ ]:

deseq_df = pd.read_csv('./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv',
            sep=",")
deseq_df.head()

In [ ]:

orthoGene = []
orthoDescr = []

pantherNum = []
pantherDescr = []

goTerms = []


for g in deseq_df.Genes:
        
  sub_df = hg_ortho_df[hg_ortho_df.XLOC.isin([g])]
  panth_df = panther_df[panther_df[0].isin([g])]
  go_df = goTerm_df[goTerm_df[0].isin([g])]

  if len(sub_df) > 0:
    #Save first result for gene/description
    orthoGene += [list(sub_df.Gene)[0]]
    orthoDescr += [list(sub_df.Description)[0]]
  else:
    orthoGene += ['NA']
    orthoDescr += ['NA']


  if len(panth_df) > 0:
    #Save first result for gene/description
    pantherNum += [list(panth_df[1])]
    pantherDescr += [list(panth_df[2])]
  else:
    pantherNum += ['NA']
    pantherDescr += ['NA']


  if len(go_df) > 0:
    #Save first result for gene/description
    goTerms += [list(go_df[1])]
  else:
    goTerms += ['NA']
 
deseq_df['orthoGene'] = orthoGene
deseq_df['orthoDescr'] = orthoDescr

deseq_df['pantherID'] = pantherNum
deseq_df['pantherDescr'] = pantherDescr

deseq_df['goTerms'] = goTerms
deseq_df.head()

Out[ ]:

	Unnamed: 0	Genes	Cluster	Condition	padj	padjClus	log2FC	orthoGene	orthoDescr	pantherID	pantherDescr	goTerms
0	1	XLOC_030861	0	Starved	9.125564e-14	3.102692e-12	-1.641450	SRSF1	serine/arginine-rich splicing factor 1 isofor...	[PTHR23147:SF44]	[SERINE/ARGININE-RICH SPLICING FACTOR 1]	[nan]
1	2	XLOC_010635	0	Starved	5.121409e-13	1.741279e-11	-1.382850	SRSF1	serine/arginine-rich splicing factor 1 isofor...	[PTHR23147:SF44]	[SERINE/ARGININE-RICH SPLICING FACTOR 1]	[nan]
2	3	XLOC_040775	0	Starved	1.881249e-11	6.396248e-10	1.093848	PINX1	PIN2/TERF1-interacting telomerase inhibitor 1...	[PTHR23149:SF27]	[PIN2/TERF1-INTERACTING TELOMERASE INHIBITOR 1]	[GO:0030234,GO:0004857,GO:0005515,GO:0003676,G...
3	4	XLOC_012879	0	Starved	9.571692e-11	3.254375e-09	-1.921527	NA	NA	[PTHR43056:SF5]	[ALPHA/BETA-HYDROLASES SUPERFAMILY PROTEIN]	[GO:0016787,GO:0044238,GO:0019538,GO:0006473,G...
4	5	XLOC_028699	0	Starved	1.657337e-10	5.634945e-09	-1.099936	NA	NA	NA	NA	NA

In [ ]:

deseq_df.to_csv('deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample_annotations.csv')

In [ ]: